--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1369 reviews by the users' company size, role or industry to find out how Databricks works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 1369 scale: '5' date_modified: '2026-09-28' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Databricks Reviews **Vendor:** Databricks Inc. **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.6/5.0 **Total Reviews:** 1,369 **AI Verified:** At least 10 G2 reviewers have confirmed using this product's AI features and functionality. ## About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. ## Databricks Pros & Cons Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews. **What users like:** - Users enjoy the **ease of use and extensive features** of Databricks, streamlining data warehousing and machine learning tasks. (192 reviews) - Users appreciate the **ease of use** of Databricks, enhancing their experience with its intuitive interface and efficient features. (155 reviews) - Users value the **seamless integrations with AWS services** that enhance efficiency and support diverse business needs. (141 reviews) - Users value the **seamless collaboration** provided by Databricks, enhancing teamwork on data projects and insights sharing. (114 reviews) - Users value the **wide array of integrated analytical features** in Databricks, enhancing efficiency and collaboration in data projects. (113 reviews) - Scalability (111 reviews) - ML Integration (106 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (102 reviews) - Machine Learning (97 reviews) - Users value the **effective data management features** of Databricks, simplifying their workflows and enhancing decision-making. (87 reviews) **What users dislike:** - Users face a **steep learning curve** with Databricks, as its complexity can be confusing for newcomers. (78 reviews) - Users note that the **cost of Databricks can be quite high** , particularly for large data projects and limited free options. (71 reviews) - Users find the **steep learning curve** of Databricks challenging, particularly for those unfamiliar with big data tools. (64 reviews) - Users find the **complexity** of Databricks challenging, especially during initial setup and navigation of advanced features. (45 reviews) - Users encounter **complex setup** challenges with Databricks initially, but support helps resolve issues quickly. (35 reviews) - Performance Issues (34 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (34 reviews) - Poor UI Design (33 reviews) - Users express frustration over **missing features** in Databricks, limiting its effectiveness for complex deployments and custom setups. (31 reviews) - Cost (29 reviews) ## Databricks Reviews ### 1. BI and Data Engineering in One Place, with AI-Assistant **Rating:** 4.0/5.0 stars **Reviewed by:** Corrado P. | Service Designer and Workshop Facilitator, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **Reviewed Date:** April 23, 2026 **What do you like best about Databricks?** Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems. Using AI to improve and make faster the SQL writing and execution **What do you dislike about Databricks?** Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so. **What problems is Databricks solving and how is that benefiting you?** Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data. **Official Response from Jess Darnell:** > It's great to hear that Databricks is solving fragmentation and inefficiency across the data lifecycle for you, and that it's removing friction between teams, tools, systems, and data. ### 2. Transforms Table Data into Trustworthy Visuals with Helpful Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Aruthra L. | Data Engineer, Logistics and Supply Chain, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like the concept of transforming data into visuals for each table. Genie Code also helps with debugging and validating the data, which makes it easier to trust what I’m working with. **What do you dislike about Databricks?** As a proprietary platform built on open-source foundations, it can still introduce vendor lock-in risks, particularly through components such as Unity Catalog and its custom APIs. **What problems is Databricks solving and how is that benefiting you?** Databricks primarily solves the longstanding challenges of fragmented data architectures by introducing the Lakehouse paradigm. It combines the low-cost, scalable storage of data lakes with the reliability, ACID transactions, and performance of traditional data warehouses. This eliminates data silos, reduces costly ETL duplication, and provides a single unified platform for structured, semi-structured, and unstructured data. **Official Response from Janelle Glover:** > Thanks for sharing your feedback! We're glad to hear that Databricks is helping you solve challenges associated with fragmented data architectures and that you find Genie Code helpful for debugging and validating data. ### 3. Comprehensive Analytics with Smooth Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I use Databricks for end-to-end analytics and pipeline development for machine learning. I love that it provides the whole analytical pipeline and machine learning workflows from exploratory data analysis to serving and monitoring, all in one place. It has made workflows and jobs run extremely smoothly and reliably. The initial setup was extremely easy, so much so that I thought I might be doing something wrong. Overall, I rate it a 10 out of 10 for recommending it to a friend or colleague. **What do you dislike about Databricks?** RBAC could be simplified. Maybe it is our infrastructure but we would like to be able to use UI and define role-based and access-based authentication to schema, tables, and columns. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for end-to-end analytics and machine learning pipelines. It makes workflows and jobs run smoothly and reliably, providing a complete analytical pipeline and ML workflows from EDA to serving and monitoring all in one place. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your analytics and machine learning workflows. We appreciate your feedback about RBAC and will take it into consideration for future improvements. ### 4. Databricks Streamlined Our ETL Migration with Delta Lake and Unified Analytics **Rating:** 3.5/5.0 stars **Reviewed by:** Yuvi M. | Data Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** Databricks transformed my day-to-day workflow, taking me from constant SQL Server/ADF headaches to scalable, unified analytics. Migrating stored procedures into Spark SQL notebooks was surprisingly smooth, and using Delta Lake MERGE instead of complicated UPDATE logic saved me weeks of rewriting. The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly. On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that. If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank. **What do you dislike about Databricks?** The cluster reconnects fairly often, which can be disruptive during active work sessions. Also, when I run complex or heavy queries, I notice clear lag in response times, and that slowdown can hurt productivity. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us centralize our data engineering and analytics workflows into a single, unified platform. It addresses the challenge of managing large-scale data pipelines by enabling our team to process and transform massive datasets efficiently with Spark. The collaborative notebook environment has also boosted productivity, making it easier for data engineers and analysts to work together. Overall, it has significantly reduced the time we spend on data preparation and has allowed us to focus more on deriving insights. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks, especially regarding the smooth migration process, cost savings with autoscaling clusters, and your experience with Genie and Unity Catalog. We understand your concerns about cluster reconnects and query response times, and we're actively working to enhance the platform's performance for all users. ### 5. Unified Lakehouse with Unity Catalog Makes Governance and Collaboration Seamless **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Accounting | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** Unified Lakehouse Architecture: Bringing together the best elements of data warehouses and data lakes in a single platform feels like a massive game-changer. It removes the burden of maintaining separate, siloed infrastructures for relational BI queries versus raw, unstructured data storage, which in turn lowers total cost of ownership and reduces overall engineering complexity. Centralized Governance with Unity Catalog: Handling security, access controls, and automated data lineage from one interface across all workspaces has dramatically simplified compliance. The ability to trace data downstream—from raw ingestion all the way to the final BI report or machine learning model—adds a lot of confidence in data integrity. Persona-Specific Collaborative Workspaces: The platform also does a great job supporting multiple technical disciplines without forcing teams into separate tools. Data engineers can build robust pipelines with multi-language notebooks, data scientists can manage the ML lifecycle natively through integrated MLflow, and business analysts can run high-performance queries using Databricks SQL, all while working at the same time on the exact same live datasets. **What do you dislike about Databricks?** Steep Learning Curve: Getting up to speed on the platform takes solid foundational knowledge of Apache Spark, cloud infrastructure, and languages like Python or Scala, which can make initial onboarding difficult for less technical team members. Complex Cost Governance: Cloud compute spend can rise quickly if cluster auto-termination settings, node sizing, and auto-scaling policies aren’t monitored closely and kept under tight control. Interface and Feature Transition Overhead: Because the platform evolves quickly, updates can sometimes lead to a fragmented UI experience, especially when moving workflows from legacy configurations to newer frameworks like Unity Catalog. **What problems is Databricks solving and how is that benefiting you?** Data Silos: Historically, organizations had to maintain a data lake for raw, unstructured data and a separate data warehouse for structured business intelligence. Databricks addresses this with its Lakehouse architecture, bringing both together into a single storage and performance layer. Team Fragmentation: Data engineers, data scientists, and business analysts often end up working in isolated tools and workflows. Databricks offers a collaborative workspace where these disciplines can work side by side on the same live datasets, using SQL, Python, Scala, or R. Infrastructure Complexity: Configuring, scaling, and managing distributed computing environments manually can be highly complex and time-consuming. Databricks automates cluster management, auto-scaling, and environment configuration so teams can stay focused on the data itself rather than ongoing server maintenance. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on Databricks! We're thrilled to hear that you find our Unified Lakehouse Architecture and Unity Catalog beneficial for simplifying governance and collaboration in your data workflows. We understand your concerns about the learning curve, cost governance, and interface transitions. We continuously strive to improve user experience and provide resources to support all team members in effectively utilizing our platform. Your insights are valuable, and we appreciate your feedback. If you have any specific questions or need assistance with any aspect of Databricks, please feel free to reach out. We're here to help and ensure you have a seamless experience with our platform. ### 6. Scalable Power with Manageable Trade-offs **Rating:** 4.5/5.0 stars **Reviewed by:** Janani D. | Senior Data Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** The collaborative notebooks are hands-down my favorite part of Databricks. I love being able to jump into a notebook with my team, tweak Spark SQL queries live on those massive shipment datasets, and watch everything sync instantly—without any version-control. It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver. Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms. **What do you dislike about Databricks?** One key drawback is the cost management—charges can accumulate rapidly if clusters are left running, requiring careful monitoring of DBU usage and auto-termination settings. Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses core challenges in managing large-scale data processing, such as scalability limitations in traditional databases and the complexity of integrating disparate tools for ETL workflows. It enables distributed Spark processing across clusters to handle massive datasets efficiently, while Delta Lake provides ACID-compliant storage to ensure data integrity amid evolving schemas or concurrent updates. This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the collaborative notebooks and the seamless Spark integration in Databricks. We understand your concerns about cost management and UI performance, and we are continuously working to improve these aspects for a better user experience. ### 7. Databricks Unifies Data and AI for Effortless ML at Scale **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it brings data and AI into one place, so you’re not jumping between tools. It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking. The integration with Apache Spark helps handle large datasets without extra setup. Overall, it just reduces the friction between raw data and actually getting useful AI outcomes. **What do you dislike about Databricks?** One thing I find challenging with Databricks is cost visibility-it can scale quickly, and predicting spend isn’t always straightforward. There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs. And for simpler use cases, it can feel like overkill compared to lighter-weight solutions. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of fragmented data and AI workflows by bringing everything-data engineering, analytics, and ML-into one platform. It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity. For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure. Overall, it helps focus more on solving business problems rather than managing tools. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks has helped streamline your data and AI workflows, reducing latency and pipeline complexity. We're committed to providing a platform that allows users to focus on solving business problems rather than managing tools. ### 8. Efficient ETL and AI-Driven Data Validation **Rating:** 4.5/5.0 stars **Reviewed by:** Dinesh Kumar D. | Senior Data Analyst, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I like the AI-supported environment in Databricks, which I use extensively for ETL tasks and experimental AIBD dashboards. It's really helpful for fixing code issues and handling logic implementation efficiently. The DLT feature is also a great addition for supporting streaming data. I find Delta Lake very useful for reliable data handling with its ACID transactions, schema enforcement, and reliable versioned data. Notebooks make it easy to develop, test, and debug data logic interactively. I also appreciate the workflows for automating and scheduling pipelines, which improve reliability and reduce manual effort. Databricks is cost-effective compared to other platforms like Synapse and Snowflake, and it's easy to track versions and handle failures. The initial setup was straightforward, with workspace creation and cluster setup being fairly easy for my team. **What do you dislike about Databricks?** The DLT, one of my personal experiences, as when set on DLT for one flow, I could not create another flow with the same table used previously. On a business aspect, it's normal to use one table for different reporting aspects as a base table and require different refresh timing. **What problems is Databricks solving and how is that benefiting you?** I perform ETL tasks and reporting with Databricks. It helps set up streaming data using DLT, and features like Delta Lake enhance data quality. Notebooks support interactive logic development, while workflows automate pipeline scheduling, reducing manual effort. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We're pleased to hear that the AI-supported environment, Delta Lake, and cost-effectiveness have been beneficial for your ETL tasks and reporting. We have noted your feedback about the limitations with Delta Live Tables and will take it into account for future improvements. ### 9. Streamlines ML Engineering with Powerful Features **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for machine learning engineering to basically solve all of the model life steps. I like using MLflow to track the entire model lineage and having the AI runtime assure that I have reproducible results. MLflow provides a structured, reproducible way to do my experiments and helps me keep track of what I've done before, and see how changes affect the overall metrics. The best thing about it is that it's open source, so I can also run it locally if I want to and inspect and export traces everywhere I want. **What do you dislike about Databricks?** I think environment management was always a bit of a hassle, so it's quite difficult to use Docker containers for that. Difficult thing was just finding out which were the best practices. There's some different ways to do the same thing. It was not clear which one is the best or preferred way to do it in our use cases. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for machine learning engineering to handle all model lifecycle steps. MLflow tracks model lineage, and the AI runtime ensures reproducible results. **Official Response from Jess Darnell:** > We're glad to hear that you find MLflow and AI runtime helpful for tracking model lineage and ensuring reproducible results. We understand that environment management can be challenging, and we are continuously working to improve our documentation and provide clear best practices for our users. ### 10. Powerful unified platform for data, analytics, and AI **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is that it brings data engineering, analytics, governance, and AI workflows into one unified platform. It makes it much easier to work with large datasets, collaborate across teams, and move from raw data to usable insights quickly. Features like Genie are especially useful because they make data more accessible through natural-language questions, helping business users explore data without always needing custom SQL or engineering support. **What do you dislike about Databricks?** The biggest downside is that Databricks can come with a learning curve, especially for people who are new to Spark, notebooks, clusters, or lakehouse architecture. Configuration and cost management also require attention, particularly as usage expands across multiple teams. Once the right patterns and governance are established, the platform becomes very powerful, but getting everyone onboarded and setting things up properly can take time. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of fragmented data work: separate tools for data engineering, analytics, machine learning, governance, and business reporting. As someone working across structured data and data science, I benefit from having those workflows closer together in one lakehouse platform instead of constantly moving data between systems. It helps me build more reliable pipelines, explore large datasets faster, and support analytics or ML use cases from the same governed data foundation. Features like Genie also make it easier for non-technical stakeholders to ask questions of the data directly, which reduces bottlenecks and frees up technical teams to focus on higher-value modelling, architecture, and data quality work. The main benefit is speed and trust: faster time from raw data to insight, less duplication, fewer handoffs between tools, and better collaboration between engineering, analytics, and business teams. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has helped you solve the problem of fragmented data work and brought your data engineering, analytics, and AI workflows closer together. We understand the importance of streamlining these processes and are committed to providing a platform that supports reliable pipelines, faster data exploration, and collaboration across technical and non-technical teams. ### 11. Seamless FinOps with Databricks: Innovative, Efficient, and Reliable **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like the Databricks workflows and I'm excited about getting started with the lake-based features, which I think are excellent. We also partner with Genie, which is an AI feature that helps us become more efficient. I appreciate that there are out-of-the-box features that help us fast-track our processes and automate certain tasks using simple language. The initial setup of Databricks was pretty easy. We had some Databricks SMEs who got everything working for our environment quite smoothly, making the adoption process easy. Overall, Databricks is serving the purpose it was taken for, and we're receiving everything we need from it without anything that I would want changed. **What do you dislike about Databricks?** None **What problems is Databricks solving and how is that benefiting you?** I use Databricks for FinOps, cloud cost management, and as a hub for finance data. It efficiently processes our finance data, creating visualizations and dashboards that aid leadership. With Genie and out-of-the-box features, it automates tasks, fast-tracks processes, and formats data for downstream needs. **Official Response from Janelle Glover:** > We're thrilled to hear that you're enjoying the Databricks workflows, Lakebase features, and Genie, as well as the seamless setup process. It's great to know that Databricks is serving your needs efficiently. Thank you for sharing your positive experience! ### 12. Databricks Streamlines Data Workflows with Powerful, Scalable Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Transportation/Trucking/Railroad | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks is a great platform that makes it easy to work with large amounts of data. It provides a user-friendly experience and offers powerful tools for data engineering, analytics, and machine learning. The platform is reliable, scalable, and helps teams collaborate effectively. Overall, Databricks has been a valuable solution for improving productivity and streamlining data workflows. **What do you dislike about Databricks?** One downside of Databricks is that it can be expensive, especially as usage grows. Some advanced features may also have a learning curve for new users. Additionally, managing costs and understanding the pricing structure can sometimes be challenging. However, the overall capabilities and performance of the platform generally outweigh these drawbacks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to work with data and run analytics workloads more efficiently. The platform helps simplify data management and provides a scalable environment for processing large datasets. It has helped improve workflow efficiency, collaboration, and overall productivity while making it easier to access and analyze data. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been valuable in streamlining your data workflows and improving productivity. Our platform is designed to provide a user-friendly experience and powerful tools for data engineering, analytics, and machine learning. ### 13. Boost Data Management with Speed and Agility **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks because it helps us a lot to centralize information and ensure the quality of our data. I am impressed by the speed at which technology moves in Databricks, that they listen to customers and try to support us. The integration with Genie has been crucial for us, as it helps us a lot in various use cases. Managing tokens was a repetitive task for my team, but with Databricks we can delegate and look for other execution alternatives. Additionally, we decided to switch to Databricks because this technology evolves quite a bit. **What do you dislike about Databricks?** The topic of agent governance becomes crucial; often maintaining a governance of agents or tokens per project is something we lack and that would greatly help to establish. At the beginning, like everything, it was complicated but with guidance we can stabilize, especially the technological change was what made it difficult in the organization. **What problems is Databricks solving and how is that benefiting you?** Databricks helps me centralize information and ensure data quality. It facilitates token management, allowing us to delegate repetitive tasks, which gives us room to explore other options. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been able to centralize your information and ensure data quality. We appreciate your feedback on the speed of technology and the integration with Genie. We understand the importance of agent governance and are continuously working to improve in this area. ### 14. Unified Platform with Smooth Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really value how smooth and integrated Databricks is for auto job scheduling, making it easy to share work with colleagues. It's convenient to have everything under one umbrella, so I don't need to rely on multiple tools to integrate jobs into the right format and tables. I appreciate that you can easily connect repos to read code and get notifications. Databricks provides a single platform where I can execute procedures and choose the kind of machine to run my tasks. The setup was not difficult, taking about a week or two to get used to, and it's quite easy once you understand the data and different product offerings. Overall, Databricks makes processing all our customers' data, transformations, and running data science models straightforward. **What do you dislike about Databricks?** I think the Genie was something which I was feeling like it was not performing that well because whenever we're asking it to write a code, it was taking a longer time. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to process and transform customer data, run data science models, and perform auto job scheduling. It integrates smoothly under one platform, eliminating reliance on multiple tools, and simplifies sharing work with colleagues. **Official Response from Janelle Glover:** > We appreciate your feedback on your experience with Databricks. We're sorry to hear about the challenges you faced with Genie and will take this into consideration for future improvements. ### 15. Databricks’ Unified Platform Simplifies Scalable Data Pipelines and Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its unified platform that brings together data engineering, analytics, AI, and governance in a single environment. It simplifies building reliable data pipelines, supports scalable processing with Spark and Delta Lake, and enables teams to collaborate more effectively across the entire data lifecycle. **What do you dislike about Databricks?** The quality and depth of some training and learning materials could be improved. In some cases, the content feels too high-level and would benefit from more real-world examples, hands-on exercises, and deeper technical explanations for advanced users. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of managing large-scale data processing, analytics, and AI workloads across multiple tools and platforms. By providing a unified environment for data engineering, data warehousing, governance, and machine learning, it reduces complexity and improves productivity. For me, this means I can build and maintain data pipelines more efficiently, ensure data reliability with Delta Lake, and spend more time delivering business value instead of managing infrastructure. **Official Response from Aunalisa Arellano:** > We're glad to hear that you appreciate the unified platform and its ability to simplify data pipelines and collaboration. We're continuously working to improve our training and learning materials to provide more practical and in-depth content for all users. Thank you for the feedback and for taking the time to leave a review! ### 16. Versatile and Efficient, Yet a Steep Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for ETL in both my personal projects and my company. My favorite feature is the Genie AI assistant. Databricks solved a challenging task for my team in the gas and energy sector by helping us get the most out of our KPIs. It provided preprocessing techniques and job pipelines that made it possible to write raw records to the delta tables, even when the data was riddled with noise. I like how Databricks is a one-stop shop for any on-premises cloud provider. It's very versatile and offers different services that work with Azure and AWS, yet it also has its own data lake service. **What do you dislike about Databricks?** I think the official documentation can use a bit more beginner-friendly information. The way I learned about it was trial and error and word-of-mouth type of information. It was a bit tricky on my end because I’ve never really worked along the lines of data science or data engineering. Interfaces were similar to what I knew, but it was more about how much I knew about the product. **What problems is Databricks solving and how is that benefiting you?** Databricks helps my team in the gas and energy sector by providing preprocessing techniques and job pipelines to extract valuable information from noisy data. It's a versatile one-stop shop for cloud providers like Azure and AWS, offering a data lake service. **Official Response from Jess Darnell:** > Thank you for sharing your experience with Databricks! We're glad to hear that the Genie AI assistant and the versatile services have been beneficial for your ETL projects. ### 17. Scalable, Unified, and Easy to Use, But Needs Dashboard Improvements **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks for several reasons. Its scalability enables faster onboarding of multiple data sources within the same framework. The unified platform is fantastic for collaboration across our analytics, business, and data scientist teams. I appreciate the performance and the governance features it offers. The AI enablement is quite impressive, as it reduces development work and allows businesses to get faster insights using simple plain English with tools like genie. Overall, the combination of these features makes Databricks a valuable enterprise data and AI platform. **What do you dislike about Databricks?** Data governance, it is not having business glossary, databricks dashboard could be better. Two area of improvements are richer in the business glossary experience within the unity catalog and more advanced dashboard capability for business users especially when compared to specialised BI tools like tableau, PowerBI etc. **What problems is Databricks solving and how is that benefiting you?** Databricks helps unify and transform data from various sources, performing robust quality checks and governance. Its scalability aids in faster onboarding, and the unified platform enhances collaboration. AI enablement reduces development work, speeds up insights, and the scalability improves overall performance. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks scalable, unified, and easy to use, and that you're benefitting from using Genie. We appreciate your feedback on the need for dashboard improvements, and we'll definitely consider that for future updates. ### 18. Empowers Collaborative Data Science with Minor Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Arman M. | Software Developer, Computer Software, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I use Databricks for a lot of things. The main ones are making sense out of the data, looking at chunks of data, and doing machine learning. Databricks makes these tasks very easy and helpful, especially for data projects. It's great for collaborating with friends and developing my Python code in notebooks. I like Databricks because it has good capabilities for handling big data and is excellent for working with the data and machine learning. It's also easy to use when working with people, as many can work on a project and share their findings. **What do you dislike about Databricks?** Databricks is very powerful, but there are some things that need improvement. It's hard to learn for beginners when working with Spark and setting up clusters, as this was confusing at first. Sometimes the interface and settings can feel complicated. I think it would be helpful if there were clear setup instructions so new users could get started easily with Databricks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to make sense of data, collaborate with others, and develop Python code. It simplifies data engineering, machine learning, and handling data while allowing multiple people to work on notebooks simultaneously. **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Databricks, including its capabilities for handling big data and machine learning. We understand the challenges of learning to work with Spark and setting up clusters, and we are committed to enhancing our resources to make the onboarding process smoother for beginners. ### 19. Multiservice platform **Rating:** 5.0/5.0 stars **Reviewed by:** jimena m. | Data Analyst, Airlines/Aviation, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Source: Organic Review from User Profile:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** July 24, 2026 **What do you like best about Databricks?** I usually use it to make integrations between different data sources. **What do you dislike about Databricks?** I don't like the Genie Code, it's not good and I prefer to rely on other AI tools. **What problems is Databricks solving and how is that benefiting you?** The main issues we have solved so far are that we have migrated several workflows from another tool to Databricks, and the execution time has decreased considerably. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has helped you with integrating different data sources and improving workflow execution time. We're sorry to hear that you're not satisfied with the Genie Code feature and appreciate your feedback. ### 20. Versatile Platform with Robust Data Governance **Rating:** 4.5/5.0 stars **Reviewed by:** Harthika S. | Senior Data Engineer, Computer Software, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** I personally like the Databricks UI, especially the dark mode. Technically, I find Unity Catalog's built-in lineage and governance very valuable. Auto-loader's incremental file processing with exactly-once guarantees and Delta Lake's ACID reliability are my personal favorites. Delta Lake's ACID transactions ensure our data pipelines either fully succeed or fully roll back, which prevents partial writes from corrupting tables. Time travel in Delta Lake allows us to query previous versions of our table for audits without needing separate snapshots. Unity Catalog's capability to auto-track lineage across our entire pipeline is critical for regulatory audits, and its role-based access control and column masking ensure data access is properly managed across teams. The workspace and notebook setup were straightforward, making the initial setup relatively easy. **What do you dislike about Databricks?** Migrating from hive_metastore to Unity Catalog is painful with limited tooling - UCX helps but it's still a heavy lift. Databricks-to-dbt cloud orchestration lack a clean native handoff, forcing custom API polling code that's fragile and hard to debug. Cost visibility for Serverless SQL warehouse could be more granular - it's hard to attribute DBU spend to specific pipelines or dbt models without digging into system tables manually. **What problems is Databricks solving and how is that benefiting you?** Databricks replaced our fragmented data stack with one platform for ingestion, ETL, analytics, and governance. Unity Catalog handles regulatory lineage needs by auto-tracking data provenance. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that you find the UI, Unity Catalog, and Delta Lake features valuable for your data governance and reliability needs. ### 21. An all-in-one platform **Rating:** 4.0/5.0 stars **Reviewed by:** Pang L. | Machine Learning Engineer, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** March 18, 2026 **What do you like best about Databricks?** It's an all-in-one platform for data engineers, analysts, data scientists, and business users. **What do you dislike about Databricks?** It’s easy to overspend and it is a vendor lock-in. **What problems is Databricks solving and how is that benefiting you?** Data engineering, model training and inference, GenAI. Databricks solves the problem of having fragmented tools across the data and AI lifecycle. Traditionally, teams would need separate platforms for data engineering, analytics, machine learning, and AI — leading to silos, duplicated work, and governance challenges. With Databricks, data engineering pipelines, model training and inference, and GenAI development all live in one unified environment. This means data engineers can build and orchestrate pipelines, data scientists can train and deploy models, and teams can develop and serve GenAI applications — without constantly moving data or context-switching between tools. **Official Response from Janelle Glover:** > Thank you for sharing the specific problems that Databricks is solving for you. We are committed to streamlining the data and AI lifecycle to eliminate silos, duplicated work, and governance challenges. ### 22. Effortless ETL and Governance with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Vivek N. | Small-Business (50 or fewer emp.) **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for ETL, jobs, and streaming applications, and I appreciate its faster, reliable ingestion and proper data governance. It's easier to build ETL jobs and pipelines with Databricks and have a unified governance. I find Autoloader with Unity Catalog particularly helpful. Databricks is also easy to maintain, and it allows for high-speed ingestion without data loss, with less maintenance. The initial setup was easy, which is a big plus. **What do you dislike about Databricks?** Nothing **What problems is Databricks solving and how is that benefiting you?** I use Databricks for faster, reliable ingestion and proper data governance. It makes building ETL jobs and pipelines easier and has a unified governance. Autoloader with Unity Catalog helps maintain high-speed ingestion without data loss and requires less maintenance. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're delighted to hear that our platform has made building ETL jobs and pipelines easier for you, while also providing unified governance and high-speed ingestion. We appreciate your feedback and look forward to continuing to support your data needs. ### 23. Unifies Data Processing with Delta Lake's Reliability **Rating:** 5.0/5.0 stars **Reviewed by:** Tan Suong N. | Cloud Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 29, 2026 **What do you like best about Databricks?** I use Databricks in my enterprise environment and projects to ingest data from multiple sources, transform and clean it at scale, and prepare reliable datasets for analytics and reporting. It allows me to build and manage data pipelines efficiently using Spark, SQL, and notebooks. I love having data ingestion, large-scale processing, analytics, and collaboration all in one place, making my workflow much more streamlined and efficient. I really value the reliability and confidence I get from features like Delta Lake, which make data versioning, recovery, and handling changes much safer, cheaper, and easier in my projects. Delta Lake is one of the main reasons Databricks is so valuable to me because it directly addresses reliability and trust, which are constant challenges in real data projects. The ability to rollback to a previous version if something goes wrong makes me much more confident when developing, testing, or deploying changes to production pipelines. Additionally, the initial setup was relatively straightforward because Databricks integrates well with our existing cloud infrastructure. **What do you dislike about Databricks?** The learning curve can be quite steep at the beginning, especially for users who are new to Spark or large scale data processing concepts. Debugging complex pipelines or job failures can sometimes be time-consuming, when error messages are not very intuitive. As workflows and environments grow, governance and environment management can require extra effort to keep everything well-organized and consistent. Cost management is another challenge, as resource usage can increase quickly if clusters and jobs are not configured or monitored carefully. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to solve fragmentation and inefficiency in my data flow, handling ingestion, transformation, analytics, and collaboration on one platform. It reduces operational overhead, ensures data quality, and offers scalability, improving large data processing without infrastructure worries. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that our platform has helped streamline your workflow and provide reliability and confidence in your data projects. We understand the challenges with the learning curve and debugging, and we're continuously working to improve user experience and provide better error messaging. We appreciate your feedback and are committed to addressing these challenges. ### 24. Scalable, Unified Platform with a Steep Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Nitin P. | Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 29, 2026 **What do you like best about Databricks?** I use Databricks for my office projects, and I really like its ability to unify the entire data workflow in a single platform. It lets me seamlessly collaborate with data scientists and analysts, making it easy to ingest, clean, analyze, and model data. I appreciate its scalability and automation features, which save me time and reduce complexity when working with large datasets. I also like that it offers a scalable compute and storage solution, reducing infrastructure management overhead. The integration of shared notebooks and tools like Databricks Genie helps improve collaboration and speed up development. **What do you dislike about Databricks?** I haven't faced major issues with Databricks itself, but during my initial phase of using the platform, it wasn't very easy to get up to speed with all the features, tools, and configurations. Databricks evolves quickly and in the beginning, it was a bit challenging to match the pace of updates and fully leverage all its capabilities. The initial setup was moderately challenging. While the platform is well documented and user-friendly, getting familiar with all the features, configuring clusters and integrating it with our existing workflows required some learning and experimentation. **What problems is Databricks solving and how is that benefiting you?** Databricks solves scalability and performance issues, centralizing data from multiple sources and reducing silos. It simplifies collaboration among data professionals, offers scalable compute, and integrates advanced analytics, saving time and reducing complexity with large datasets. **Official Response from Janelle Glover:** > It's great to hear that Databricks and Genie have been beneficial in centralizing data and improving collaboration for your team. We understand that the initial setup and learning curve can be challenging, and we are continuously working to address these concerns. ### 25. Versatile Data Platform with Seamless Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Ayobami A. | Senior Consultant - BI, Data & Analytics, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 28, 2026 **What do you like best about Databricks?** What I like most about Databricks is that it's integratable with other platforms. I can literally set up a Databricks workspace using Azure data services from the Azure portal, and I can also use Databricks within AWS. It gives me the opportunity to integrate my Databricks notebooks into other environments and orchestration tools or ETL tools, like Azure Data Factory. **What do you dislike about Databricks?** For now, I noticed when I'm using Azure Databricks, particularly the Azure Databricks cluster, it usually times out, and it's kind of frustrating for me. Most times when I'm working, I just go into another tab. Every time I come back in a minute or two, it's timed out, and I have to sign in again. That experience can be frustrating. I would like that to be looked into. I don't know if it's an issue with Databricks or if it's an issue from the Azure side from the intraident authentication part of things. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to unify my data by managing governance within the Unity catalog, simplifying user access and report sharing. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping you unify your data, manage governance within the Unity catalog, and simplify user access and report sharing. We apologize for the frustration you've experienced with the Azure Databricks cluster timing out. Can you please contact our team via www.databricks.com/support so we can help look into this further? Thank you! ### 26. Lakehouse + Unity Catalog + DLT: Unified Governance and Efficient, Scalable Pipelines **Rating:** 5.0/5.0 stars **Reviewed by:** Héttori T. | Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** The Lakehouse architecture beautifully unifies data warehousing and data lakes. Unity Catalog simplifies governance across the board, and Delta Live Tables (DLT) makes building and monitoring robust, scalable production pipelines incredibly efficient. **What do you dislike about Databricks?** The DBUs cost model can scale rapidly and become quite expensive if cluster policies and auto-scaling boundaries are not strictly monitored and optimized. Budget governance requires constant attention. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of data siloization by merging data warehousing and data lakes into a single Lakehouse architecture. For me, this eliminates the need to maintain separate storage and compute systems for BI and AI, significantly reducing data duplication and architectural complexity while ensuring centralized governance via Unity Catalog. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks, especially the benefits of the Lakehouse architecture, Unity Catalog, and Delta Live Tables. We understand the challenges related to the DBUs cost model and the importance of monitoring and optimizing cluster policies. We're committed to helping our customers effectively manage costs while maximizing the value of our platform. ### 27. Impressive ML Implementation and Fast Complex Query Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Richard C. | Senior Python Backend Engineer, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** September 02, 2026 **What do you like best about Databricks?** I like how the machine learning model is implemented, and I’m impressed by the processing speed when running complex queries on massive datasets. **What do you dislike about Databricks?** At times, schedule delay predictions don’t work reliably in systems that depend directly on data stored in the Lakehouse. **What problems is Databricks solving and how is that benefiting you?** So far, everything has been going well with Databricks. Processing technical data no longer presents major obstacles, since we can now efficiently handle massive files of design and sensor data. **Official Response from Jess Darnell:** > Thank you for sharing your positive experience with our machine learning implementation and query performance. We understand the importance of reliable schedule delay predictions and are working to enhance this aspect. We're pleased to hear that Databricks has been instrumental in efficiently processing your design and sensor data. ### 28. Streamlined DevOps with Enhanced Production Speeds **Rating:** 5.0/5.0 stars **Reviewed by:** Adrian P. | Business Development Executive, Computer Software, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really like the UI, the product itself, and the library that Databricks offers. The conference I'm at now is also great. Our development team has benefitted as it has increased production speeds and minimized working on too many meticulous items. **What do you dislike about Databricks?** When it comes to products, when it comes to community Postgres. They have more integration. I know they have some fake base right now that they came out with. More products when it comes to Postgres. Or extensions that are themed around that'd be awesome. **What problems is Databricks solving and how is that benefiting you?** Databricks solves my infrastructure problems, making automation and working with DevOps easier. It helps increase production speeds and minimizes the effort on meticulous tasks. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the UI, product, and library offered by Databricks, as well as the benefits it has brought to your development team's productivity. ### 29. Databricks Unifies Data Engineering, Science, and Analytics Exceptionally Well **Rating:** 5.0/5.0 stars **Reviewed by:** Naveena P. | Data Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** The ability to converge data engineering, data science, and analytics on a single platform without compromising on governance, performance, or flexibility is still rare in the industry. Databricks executes this exceptionally well. **What do you dislike about Databricks?** Reducing the spinning time of all purpose clusters and job clusters. It would be more usefula nd helpful if it starts as quick as serverless **What problems is Databricks solving and how is that benefiting you?** In enterprise banking, where regulatory compliance, data accuracy, and operational resilience are non-negotiable, Databricks is solving some of our most critical challenges. As a Lead Data Engineer managing end-to-end ETL pipelines, dashboard delivery, monitoring alerts, and data governance for a major banking client, the platform has become the backbone of our modern data architecture. Databricks unifies our fragmented data landscape through Delta Lake and Unity Catalog, giving us ACID-compliant transactions for reliable ETL, automated lineage for audit-ready governance, and fine-grained access controls to protect sensitive PII and financial data—all while enabling seamless schema evolution to handle the constant changes in source systems. This directly translates to faster, more trustworthy reporting: our dashboards in Power BI and Tableau now pull from a single source of truth, eliminating metric disputes between Risk, Finance, and Compliance teams. On the operational side, native alerting integrated with Slack and PagerDuty, combined with Databricks System Tables for observability, lets us proactively catch data quality issues or SLA breaches before they impact business decisions—reducing incident resolution time by over 60%. Ultimately, Databricks isn't just improving our engineering efficiency; it's enabling us to innovate responsibly in a highly regulated environment, delivering trusted insights at scale while keeping auditors confident and stakeholders aligned. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that our platform is helping you unify data engineering, science, and analytics while maintaining governance, performance, and flexibility. ### 30. Comprehensive Platform with Room for Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Sathya R. | Data Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** I find Databricks to be a one-stop solution because it incorporates various functionalities such as orchestrating pipelines. It also has an inbuilt AI called Genie, which helps in building jobs, and other AI-related tasks. I appreciate that compared to other providers like AWS and Azure, Databricks offers specific features that they lack, allowing me to use the database simply and access everything in one place. The initial setup was quite easy because I could use a single stop to directly implement and update tables using the data lakehouse, which is easier compared to others **What do you dislike about Databricks?** I think Databricks could improve on the orchestration part. Even though it has orchestration capabilities for pipelines and jobs, it misses the ease of access that something like Airflow provides, which is specifically designed for orchestration. It would be helpful if Databricks adopted a pattern similar to Airflow's for better orchestration and job linking. I also feel the Genie part could be improved. While the Genie works well, the output duration can be lengthy, usually taking more than five to ten minutes to perform specific tasks. So, I would like to see improvements in that area as well. **What problems is Databricks solving and how is that benefiting you?** I use Databricks as a one-stop solution for various tasks. It orchestrates pipelines and utilizes an inbuilt AI, making it more feature-rich than alternatives like AWS or Azure. This allows me to streamline workflows without relying on multiple providers. **Official Response from Janelle Glover:** > Thank you for sharing your feedback on Databricks. We're glad to hear that you find our platform to be a comprehensive solution with valuable functionalities such as Genie. We appreciate your input on areas for improvement and will take your suggestions into consideration for future updates. ### 31. Seamless Integration, Needs Performance Tuning **Rating:** 4.0/5.0 stars **Reviewed by:** Pandi A. | Lead Data Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** I think the most useful part of Databricks is its single architecture where you can have everything, like a database and dashboard, all in one. Compared to other providers like Azure or AWS, where I would need multiple services, Databricks offers everything in a single service. This simplifies my work because I don't have to manage integration or network level details across different services. The convenience of having everything inside Databricks means I can avoid multiple network updates when connecting with tools like Power BI, which makes it a standout feature for me. Additionally, the initial setup after migrating from Snowflake was pretty easy since Databricks allows us to manage access and security within a single service. **What do you dislike about Databricks?** Yeah, so one thing that needs to be updated is Genie code. If I look at it, Genie code is helpful for generating code but when it does in the back end, it consumes much memory. For example, if I'm opening Databricks in Chrome, it's gonna take at least one or two GB memory at the back end, and that takes a lot of time to generate the response as well. So if we could reduce that, it would be great. Also, on the pipeline stuff, for example, if you take Airflow, Airflow is specifically designed for our position. We use Airflow and I can see, for example, if I have thousands of jobs, I can see each and every job and what's happening. But with Databricks, it's a tough job for me to see the success and failures and to manage the charts. We have multiple options to monitor it in Databricks, but it's hard when compared with Airflow. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us consolidate data from different locations into a single database, simplifying master data management and making data access easier with integrated dashboards, improving our AI-powered sales and prospect tracking. **Official Response from Janelle Glover:** > Thank you for taking the time to provide your feedback. We're pleased to hear that Databricks has helped simplify your data management and improve your AI-powered sales and prospect tracking. We have noted your feedback on Genie and pipeline management, and we will explore ways to enhance these features for a better user experience. ### 32. Databricks: A Unified, Scalable Platform for Faster Collaboration and Innovation **Rating:** 5.0/5.0 stars **Reviewed by:** Jananisree T. | Software Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks stands out because it provides a unified platform that seamlessly combines data engineering, machine learning, and analytics, making collaboration across teams much easier. I especially appreciate how it simplifies working with big data by integrating with popular tools like Apache Spark, offering scalability, and enabling faster experimentation. The collaborative notebooks, strong support for multiple programming languages, and built-in security features make it both powerful and user-friendly. Overall, it helps accelerate innovation by reducing complexity and improving productivity across the entire data lifecycle. **What do you dislike about Databricks?** One drawback of Databricks is that it can feel overwhelming for new users because of its complexity and steep learning curve. The platform offers a wide range of powerful features, but navigating them effectively often requires significant technical expertise. Additionally, costs can escalate quickly if clusters are not managed carefully, and performance tuning sometimes demands deep knowledge of Spark internals. Integration with certain external tools can also be less seamless compared to other platforms. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the challenge of managing and analyzing massive amounts of data by providing a unified platform for data engineering, machine learning, and analytics. It eliminates the need to juggle multiple tools, making workflows more streamlined and collaborative. For me, this means faster access to insights, easier experimentation with models, and reduced complexity in handling big data. The benefit is clear: improved productivity, better collaboration across teams, and quicker decision-making powered by reliable data. **Official Response from Janelle Glover:** > We appreciate your feedback on the benefits of using Databricks for managing and analyzing massive amounts of data. It's great to hear that our platform has contributed to improved productivity, easier experimentation with models, and better collaboration across teams. We acknowledge the challenges with the learning curve and cost management, and we're dedicated to addressing these concerns to better serve our users. ### 33. Unified Lakehouse Architecture for ETL, Analytics, and ML in One Stack **Rating:** 4.5/5.0 stars **Reviewed by:** Charumathi A. | Technical Lead- Data Engineering, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Unified lakehouse architecture: Databricks lets me treat my data lake more like a “lakehouse,” combining data-lake flexibility with data-warehouse-like features such as ACID transactions, schema enforcement, and time travel on Delta tables. As a result, I can handle ETL, ad hoc analytics, and ML on a single stack, rather than juggling separate warehouses, lakes, and Spark clusters. **What do you dislike about Databricks?** The platform can feel heavy and is sometimes slow, especially when working with large notebooks or running long jobs. Databricks can also be expensive to operate, particularly if clusters are left idle or aren’t well optimized. **What problems is Databricks solving and how is that benefiting you?** Faster, collaborative workflows Databricks simplifies big-data complexity by abstracting much of the Spark and cluster management, so I can focus more on logic and less on infrastructure. The built-in notebooks, jobs, and versioning make it easy to prototype quickly, collaborate with analysts and DS, and move code from experimentation into production with less rework. Unified platform for data and AI Databricks reduces the need for separate data-lake, data-warehouse, and ML tools by providing a single lakehouse platform where you can store, transform, and analyze data, and run ML workloads in the same place. This helps cut down on tool sprawl and makes it easier to share data and models across engineering, analytics, and data science teams. **Official Response from Janelle Glover:** > Thank you for sharing what you like best about Databricks! We appreciate your feedback and are continuously working on improving performance and optimizing costs to provide a better experience for our users. ### 34. Simplifies Data Engineering, Needs Better Tool Integration **Rating:** 4.0/5.0 stars **Reviewed by:** Aladdin A. | Senior Solutions Engineer - Cloud & AI Data, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** I like the features of Genie, especially the new junior code, which makes it possible to get SQL-ready scripts by just chatting using natural language. This is fascinating, especially with the governance layer on top of it with Unity. It accelerates both analysts' and engineers' jobs by helping build reports and getting them ready efficiently, especially since it has access to most of the metadata. The documentation is also useful, suggesting SQL code that can be provisioned on the fly. Tying Genie with AI functions such as the ai_query makes it a superpower. **What do you dislike about Databricks?** Honestly, a ton of features that can be improved, especially connectivity with other tools, such as cloud tools, especially like Azure. As a Microsoft employee, I evangelize Databricks, but many of our clients use the Microsoft stacks extensively. Sometimes, these tools feel isolated from the whole stack. There’s still a lot of work to be done to connect models provisioned in Azure and things like unity catalogs or governance that can sit outside of Databricks and Microsoft's stack. This feels like a disconnect, especially in highly regulated environments where on-prem stuff needs to interact with Databricks capabilities. **What problems is Databricks solving and how is that benefiting you?** Databricks simplifies provisioning services, streamlines data engineering, and speeds up workflow creation. It combines tools into one governed platform, making handling big data easier and faster. Its AI layer integrates well, reducing the need for multiple tools. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the features of Genie. We understand your concerns about the connectivity with other tools, especially Azure, and we are continuously working to improve integrations to provide a seamless experience across different platforms. ### 35. Outstanding Experience with This Software **Rating:** 4.0/5.0 stars **Reviewed by:** Kriti K. | CFO, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** January 09, 2026 **What do you like best about Databricks?** Databricks data intelligence is a platform that helps in accommodating all of our business and official data and share it with different team departments so that they can analyse it and create a detailed analytics of past performances and also make required changes on it for future growth. **What do you dislike about Databricks?** One of the major challenge that we face while working with Databricks data intelligence platform is that you cannot use this tool with a single data scientist you will have to keep a team of professionals who can deal with large data and create multiple graphs and analytics according to available information and this complete activity involves lot of financial investment **What problems is Databricks solving and how is that benefiting you?** This software help us in making sure that all the data of different departments are accommodated in a same software so that access can be easier and decisions can be taken much quicker. With the help of this tool data of all the departments like finance, operations, sales and marketing are screend in one time and thoroughly interchecked too. **Official Response from Janelle Glover:** > It's fantastic to hear how Databricks Data Intelligence Platform is benefiting your business by streamlining access to data from different departments and facilitating quicker decision-making. We appreciate your feedback on the financial investment and will take it into consideration as we continue to improve user experience and ensure our platform is accessible for all users. ### 36. Powerful Warehousing, Collaborative, AI Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Banking | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** March 13, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Databricks?** As a growing Data Engineer, the community support and clear documentation of Databricks really helps me to guide through the problems. I've been managing the jobs and pipelines where failures are bound to happen, debugging with the Diagnose this error with AI feature has helped me with fasterthe failure recovery SLA. The UI is neat and makes it very easy to move between notebooks, SQL, and PySpark without much friction. Since I work with a team, collaboration is must. Sharing notebooks and iterating with teammates feels easy. I really like that I can rely on the ABAC policies to setup the Data Quality and Governance. **What do you dislike about Databricks?** I am not hundred percent sure if I would use the term dislike, I think it's just a personal preference. I sometimes feel the compute being used is a lot more than it should be for a simple query. Maybe the shuffle read/write that always gets involved when you're using a delta tables sometimes slows down the job. **What problems is Databricks solving and how is that benefiting you?** Databricks is helping our clients to manage the lakehouse and warehouse architecture in a much more structured way. We use it as the landing layer from S3 and then process data through our medallion architecture (bronze, silver, and gold) before delivering it to the final products. It’s been very effective for orchestrating daily jobs and pipelines. I also really like the asset bundles and how easily everything integrates with Git, which makes version control and deployments much smoother for the team. I am more likely to use Databricks as my go to platform for data lakehouse and warehousing. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We're thrilled to hear that our community support, documentation, and collaboration tools are helping you and your team manage daily jobs and pipelines effectively. We understand your concern about compute usage and shuffle read/write, and we will take your feedback into account as we continue to enhance our platform. We appreciate your support and look forward to continuing to serve your data engineering needs. ### 37. Finally Databricks Data Intelligence Platform has given us stability with Spark jobs. **Rating:** 5.0/5.0 stars **Reviewed by:** Preetham C. | Senior Software Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** January 06, 2026 **What do you like best about Databricks?** I am glad my leg is not completely dead from having to wait for a query (at least the auto termination feature works and saved me money) History is somewhat useful I rely on it to pull up previous scripts I write because I always seem to forget what I did the day before. I think it helps keep finance off my back by saving them money on the AWS bill. To me, it's simply one central location for managing our big data so I do not have to watch 5 monitors. **What do you dislike about Databricks?** If I want to find a query that was run over 30 days ago the search function does not even come close to being able to help me and also installing R Packages is a complete nightmare and the error messages are as clear as mud. And when working with large amounts of data the user interface becomes sluggish and trying to scroll through a notebook is like pushing through the mud. It would be nice if it were easier to install/ manage external spark packages. **What problems is Databricks solving and how is that benefiting you?** We create Machine Learning Models and perform Heavy ETL for the Analytics Department. It does a good job partitioning our data so we can query smaller time frames without having to sit around for hours waiting. It also allows our data team to focus on their actual work rather than constantly fixing broken clusters which is nice. **Official Response from Janelle Glover:** > Thank you for taking the time to share your feedback! We're glad to hear that our platform has provided stability for your Spark jobs and that the auto-termination feature has been beneficial in saving costs. We appreciate your notes on the history feature and the centralization of big data management and will take these concerns into consideration. ### 38. Unified Platform that Enhances Data Team Collaboration **Rating:** 5.0/5.0 stars **Reviewed by:** Andrew W. | Data Scientist, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I like that Databricks makes it easy to build processes on top of a data warehouse in a single unified platform. It really helps in making it easy for data engineers and data scientists to work together without needing to move data between systems. **What do you dislike about Databricks?** Right now it’s clunky to manage Azure Active Directory groups and syncing with Databricks. I think there is a solution coming. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to deliver apps and machine learning models, solving sales promotions effectiveness and labor and product demand issues. It makes it easy for data engineers and data scientists to collaborate without moving data between systems, all in a single platform. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks is helping you deliver apps and machine learning models to solve business challenges. We appreciate your feedback and are committed to providing a seamless collaboration experience for data teams. ### 39. Streamlined Data Management with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** ROHITH S. | Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like the Genie features in Databricks, which allow users to query their requests using natural language. The data processing speed is impressive, often taking just minutes to hours. Our data pipeline's robust processing eliminates manual pipeline tuning, ensuring fresh governed data is available for business consumption. **What do you dislike about Databricks?** Automated governance boundaries could be improved. The initial configuration and platform setup was time-consuming. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to improve data quality, standardize data, and generate business insights. The Genie feature allows querying requests using natural language, making data processing much quicker and more efficient. The robust data pipeline eliminates manual tuning and ensures fresh data is ready for business use. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying Genie and the data processing speed in Databricks. We appreciate your feedback on the automated governance boundaries and initial setup, and we will take this into consideration for future improvements. ### 40. Versatile Platform with Robust Data Engineering, BI, and AI Capabilities **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like that Databricks provides a cloud data lakehouse where I can do both AI and BI, and it allows for federated data using Unity Catalog. The data engineering, Genie Spaces, and data pipelines are all critical to my work. They help us build and deploy a global data lake that pulls data from multiple ERPs and other sources, providing sales, supply chain, and finance insights quickly and easily. **What do you dislike about Databricks?** Clarity around cost. Everything is based on DBUs, which sometimes can be a little unclear what DBUs translate to in clear dollars. But they do provide good dashboards to see actual consumption in dollars. **What problems is Databricks solving and how is that benefiting you?** It provides a cloud data lakehouse where I can do both AI and BI, and have federated data using Unity Catalog. It allows building and deploying a global data lake, pulling data from multiple sources, and providing insights quickly and easily. **Official Response from Janelle Glover:** > We're pleased to hear that Databricks is helping you solve data engineering and analytics challenges, and we appreciate your insights on the benefits that Genie Spaces and Unity Catalog are bringing to your work. ### 41. Scalable and User-Friendly with Impressive Data Processing Speeds **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through a business email account added to their profile **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really like how Databricks has recently opened up all the apps for building dashboards, and the Ginie feature is pretty cool for providing insights over data. It's scalable and easy to use, which saves us time compared to our traditional ways of handling data. The speed at which it scans data is impressive, and building apps is super fast and cool, reducing development time efficiently. **What do you dislike about Databricks?** I believe the interoperability in terms of iceberg where Databricks, Delta tables are very easily known can be used within Snowflake as an iceberg table. Whereas the iceberg table from Snowflake to Databricks is not working yet for us. So that’s the area I feel like Databricks could have done something better or sooner for us. **What problems is Databricks solving and how is that benefiting you?** Databricks mainly solves a lot of data engineering problems, processing large volumes of logs efficiently in our database engineering system. **Official Response from Jess Darnell:** > Thank you for sharing your positive experience with Databricks, including the ease of building dashboards, the Genie features, and the impressive data processing speeds. We understand your concerns about the interoperability with Snowflake and the iceberg tables, and we will take that into account for future enhancements. ### 42. Efficient Yet Complex for New Users **Rating:** 5.0/5.0 stars **Reviewed by:** INBOXME 2. | Mid-Market (51-1000 emp.) **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like all the cool feature apps and Lakebase in Databricks, which make it less time-consuming to build. The 'stand table with UC' and syncing tables with UC and Lakebase enhance the experience, making things smoother. The setup with Azure was pretty straightforward with networking, and overall, it felt smooth and easy to use. **What do you dislike about Databricks?** More complicated for a new user. When starting out, it was challenging to understand clusters, what they are, and how they start. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for data engineering, facilitating data transmission to get business insights. It offers cool feature apps and lakebase, which reduce build time, making it more efficient. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the cool feature apps and Lakebase in Databricks, as well as the smooth setup with Azure. We understand that the platform may seem complex for new users, and we are continuously working to improve our onboarding process and provide more resources for beginners. ### 43. Complete Integration and Easy Configuration with Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Pedro H. | Small-Business (50 or fewer emp.) **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **AI Translated:** This review has been translated from Portuguese using AI. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like how the components of Databricks are integrated into a single platform, covering everything from a simple ETL to job orchestration. This integration makes it much easier because the client doesn't need numerous employees working on various components, especially in the IT area. This really simplifies implementation and allows for more focus on the business area. **What do you dislike about Databricks?** I would say a policy that is a bit more accessible and more transparent associated with the costs. **What problems is Databricks solving and how is that benefiting you?** Databricks solves business problems and integrates components into a single platform, facilitating implementation by focusing on the business area without the need for many employees for different IT tasks. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks’ integrated platform beneficial for your business needs. We appreciate your feedback about cost transparency and accessibility, and we'll consider it for future improvements. ### 44. Seamless Web-Based Analytics with Some Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Small-Business (50 or fewer emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Source: G2 invite:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** July 08, 2026 **What do you like best about Databricks?** I really like that Databricks is integrated in a web browser, which makes it extremely easy to access. Anyone with access to a web browser and authentication to our data system can do self-service analytics. This is a big improvement from when we used Teradata, which was extremely slow and required downloading an application. Now, with Databricks, everything's in the web browser and that's a major plus. **What do you dislike about Databricks?** I believe that Databricks can be improved by maybe having a more useful path for beginners. Sometimes it could be a steep learning curve when learning Databricks, understanding what queries do, understanding what's the difference between notebooks, jobs, workspaces, etc. **What problems is Databricks solving and how is that benefiting you?** Databricks solves our problems by providing everything needed for data transformation, processing, analytics, SQL queries, and machine learning models, all in one place. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks easy to access and that it has improved your analytics process. We appreciate your feedback on the learning curve and will take it into consideration for future improvements. ### 45. Fast and Intuitive, Perfect for Unified Data Management **Rating:** 5.0/5.0 stars **Reviewed by:** David C. | Enterprise (> 1000 emp.) **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for data warehouse and analytics dashboard tasks. I really appreciate how it brings my data into one place, which is super helpful for managing everything efficiently. I like its speed and user interface, which are standout features for me. Additionally, I can use various languages on one notebook, making it really versatile and useful for different programming needs. The initial setup was fairly easy, which was a nice surprise. **What do you dislike about Databricks?** Easier to understand. Maybe add more tutorials. **What problems is Databricks solving and how is that benefiting you?** Databricks brings my data into one place and allows me to use various languages on one notebook, enhancing my data warehouse and analytics experience. **Official Response from Janelle Glover:** > We're thrilled to hear that Databricks has been a valuable tool for your data management and analytics tasks. We appreciate your feedback on the platform's speed, user interface, and versatility. We'll definitely take your suggestion for more tutorials into consideration to enhance the user experience. ### 46. Practical and Efficient for Improving Financial Processes **Rating:** 5.0/5.0 stars **Reviewed by:** Clifford J. | Technical Support, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like that Databricks is very practical and that it has a lot of documentation at hand along with genie code. This helps us improve report delivery times and have information readily available. I also value how Databricks allows less technical users to build ideas that previously only more technical people could develop. Additionally, the initial setup was super easy, which reinforces its practicality. I recommend it whenever I can. **What do you dislike about Databricks?** With the new, improve **What problems is Databricks solving and how is that benefiting you?** Databricks improves report delivery times, provides us with easy access to information, and helps non-technical users develop ideas. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks practical and efficient for improving financial processes. Our documentation and Genie Code are designed to make report delivery times faster and information readily available. We appreciate your recommendation and look forward to continuing to support your needs. Thank you for taking the time to leave a review and choosing Databricks! ### 47. Lakebase is great but needs better monitoring & auditing **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Health, Wellness and Fitness | Enterprise (> 1000 emp.) This reviewer's identity has been verified by our review moderation team. They have asked not to show their name, job title, or picture. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 24, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Databricks?** Bidirectional Sync capabilities in Databricks Lakebase & Lakehouse allows us to establish continuous healthcare intelligence where analytics in Lakehouse and operations in OLTP Lakebase remain tightly connected. We invested heavily in the modern Lakehouse architectures for enterprise data analytics AI and discovery. Clinical systems and patient interaction data flow into governed Databricks Lake houses where we build Care 360 views and healthcare key insights. **What do you dislike about Databricks?** Setting up Sync process requires extensive curation and planning to achieve great outcomes. Cost management is a big concern when multiple teams as multiple departments in my org have various policies. The records are dropped if the PK is null for data in lake houses. There's room for improvement to better handle it in future **What problems is Databricks solving and how is that benefiting you?** Databricks solves ingestion, transformation, governance, and data quality challenges, offering AI and BI tools for instant insights. Bidirectional Data sync (Lakehouse -> Lakebase) processes can operate together as a unified architectural pattern in the Enterprise Healthcare Intelligence Platform. Healthcare enterprises have historically struggled because analytical systems and operational systems evolved independently. Bidirectional Sync introduces a unified model where Analytics, Operational serving, AI activation & Continuous learning coexist within the same governed ecosystem that significantly reduces Data duplication, Pipeline sprawl & Synchronization complexity while improving Operational intelligence, Data freshness, Auditability & Decision latency. Forward Sync operationalizes healthcare intelligence. Reverse Sync reactivates operational knowledge back into the learning system. Together, they establish a continuous intelligence architecture where every healthcare interaction contributes toward improving patient outcomes, operational efficiency and AI effectiveness at enterprise scale. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Genie and Databricks. We appreciate your feedback on setting up Genie. We are continuously working to enhance our semantic model and cost management features for a better user experience. ### 48. Boosts Productivity with Easy Setup, Needs Better Semantic Model **Rating:** 5.0/5.0 stars **Reviewed by:** Frederik S. | Senior Machine Learning Engineer, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really like the Genie feature in Databricks as it enhances my productivity significantly—I'd say it boosts it by 10x. It helps me write code more efficiently, manage workflows better, and apply best practices effectively. The initial setup was also very easy, which was a big plus. **What do you dislike about Databricks?** The semantic model layer in Databricks could be improved. Power BI has more time intelligence, which is hard to replicate in Databricks metric views. Migration is also hard. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for ingestion, storage, transformation, orchestration, and data exposure with governance. It boosts my productivity, especially in writing code, managing workflows, and applying best practices. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' Genie feature to be a significant productivity booster and that the initial setup was easy for you. We appreciate your feedback on the semantic model layer and will take it into consideration for future improvements. ### 49. Databricks Data Intelligence Platform actually works and saves money **Rating:** 5.0/5.0 stars **Reviewed by:** Christopher C. | Sales Operations Manager, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** This review contains authentic analysis and has been reviewed by our team **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: G2 invite on behalf of seller:** Invitation from G2 on behalf of a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** December 21, 2025 **What do you like best about Databricks?** The autoscale works well; it also helped us reduce the cost of using cloud resources. I thought that was going to be a problem since this is the first time we used autoscale. The support has been good enough, they normally appear on time for their scheduled hours to assist us in fixing the problems we create. The ability to save the query history in order of how the queries were written is nice as I often forget what I write a few minutes after writing it. When working with coworkers who need access to your code you can send them the permalink (link) to the code which is better than having to explain it. Since it supports both Spark and Presto within one tool I do not have to jump between tools. **What do you dislike about Databricks?** I hate the way the search function works. I have never found the results from a month ago, and it is annoying. The UI will occasionally lag behind my typing, and if I am in a rush I feel it takes too long. Also it would make much more sense for the tables drawer to remain open when I click on the notebook and instead of closing automatically which is very time consuming as I need to reopen it again. I have had the support team tell me they cannot replicate an issue which is no help at all. **What problems is Databricks solving and how is that benefiting you?** We are able to process large amounts of data using Databricks without having to build out a large ops team to manage our clusters. We can scale up and down so we only pay for the time we are running jobs in the cloud. I use it to see sales numbers on a daily basis although I am not a data engineer. Databricks allows us to run pipelines with Airflow without all of the things crashing right away. Databricks reduces the amount of admin work involved in building and managing clusters. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks Data Intelligence Platform! We're pleased to hear that the platform has helped you save costs and streamline your data processing. We understand your concerns about the search function and UI, and we'll work on addressing these issues to enhance your user experience. We appreciate your feedback! ### 50. Databricks Makes Collaboration and Reliable Data Pipelines Easy **Rating:** 4.0/5.0 stars **Reviewed by:** Raja B. | Senior Solutions Engineer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Seller invite:** Invitation from a seller or affiliate. This reviewer was offered a nominal incentive as thanks for completing this review. **Reviewed Date:** March 28, 2026 **What do you like best about Databricks?** I really enjoy working in the Databricks environment because it makes it easy to collaborate with others through shared notebooks. Delta Lake technology has also been great for ensuring data quality and reliability across our pipelines. It lets us manage data, build pipelines, and run AI/BI workloads all in one place. **What do you dislike about Databricks?** The interface is quite laggy at times, especially when I’m scrolling through a notebook or spinning up a cluster. **What problems is Databricks solving and how is that benefiting you?** Because it’s a unified, end-to-end platform covering everything from data ingestion and transformation to AI and BI insights, it enables faster analysis and helps convert complex datasets into actionable decisions more efficiently **Official Response from Janelle Glover:** > We're thrilled that Databricks has been beneficial for your data pipelines and analysis. We're sorry to hear that you have experienced lags in the interface. We appreciate your feedback on this, and our team will work to address this. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 3 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 3 upvotes - [Is Databricks worth it for data engineers managing big data processing?](https://www.g2.com/discussions/is-databricks-worth-it-for-data-engineers-managing-big-data-processing) - 1 comment, 1 upvote - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 4 comments, 1 upvote - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [View Databricks pricing details and edition comparison](https://www.g2.com/products/databricks/reviews?page=4§ion=pricing&secure%5Bexpires_at%5D=2026-09-28+08%3A53%3A05+-0500&secure%5Bsession_id%5D=e5920299-0239-4d31-88e5-3e23c31e6d55&secure%5Btoken%5D=14bf08fadce13c7e201c3f01680f519095617fb77c7647528606bbcec2634fc4&format=llm_user) ## Databricks Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards - Customizable Reports - Marketing Reports - Sales Reports - Activity Dashboard - Interactive Reports - Customizable Reports - Customizable Reports - Activity Dashboard - Customizable Dashboard **Additional Functionality** - Continuous Integration - Drag & Drop - Backup and Recovery - Multiple Programming Languages Supported - Continuous Deployment - Code Development - For No-Code Development - Version Control - Configurable Workflow - Graphical User Interface - For Low-Code Development - Activity Dashboard - Generative AI - UI Prototyping - Source Control - Software Development - API - Data Import/Export - Web App Development - Custom Development - Code Repository Integration - Data Security - Mobile Development - Access Controls/Permissions - Game Development - Collaboration Tools - Application Security - Alerts/Notifications - Automated Testing - Integrated Development Environment - Offline Access - Debugging - AI Copilot - Reporting/Analytics - Compatibility Testing - Pre-built Templates - Third-Party Integrations - Data Modeling - Customizable Branding - Data Visualization - Code Editing **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Data Cleansing - Data Extraction - Third-Party Integrations - Data Verification - Information Governance - Data Capture and Transfer - AI Copilot - Customizable Reports - Data Migration - Behavior Analytics - Real-Time Reporting - Interactive queries - Audit Trail - Multiple Data Sources - Document Storage - Access Controls/Permissions - User Management - AI/Machine Learning - Master Data Management - Task Scheduling - Data Import/Export - Monitoring - Customer Database - Audit Management - Reporting/Analytics - Data Visualization - Collaboration Tools - Workflow Management - Full Text Search - Compliance Management - Generative AI - Data Quality Control - Data Connectors - Automatic Backup - Activity Tracking - Activity Dashboard - Data Security - Data Analysis Tools - Data Synchronization - Metadata Management - Dashboard Creation - Visual Analytics - Secure Data Storage - Data Integration - Database Support **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations - Configurable Workflow - Rules-Based Workflow **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics - ETL - Extract Transfer Load - Data Capture and Transfer - Real-Time Analytics - Reporting/Analytics **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage - Data Visualization **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Reports** - Reports Interface - Share Reports - Steps to Answer **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Functionality** - Ease of Use - File Management - Multi-Language Support - Customization - Straight-Out-the-Box Functionality - Help Guides - Patching & Updates - Workflow Management - Change Management - Deployment Management - Performance Management - Compliance Management - Lifecycle Management - Task Management - Document Management - Database Support **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails - API Security - Data Security - Authentication **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Traffic Management & Performance - AI Gateways** - Token-Aware Rate Limiting - Semantic Caching - Multi-Model Routing & Fallbacks **Configuration** - Application Performance - Orchestration - Database Monitoring - Anomaly Detection - Network Security **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training - Database Support - Multi-Language **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring - Real-Time Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability - Non-Relational Transformations - Data Extraction **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending - Data Capture and Transfer **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Security** - Access Control - Roles Management - Compliance Management - Deletion Management - Access Controls/Permissions - User Management - Process Management - Audit Management - Metadata Management **Operations** - Metrics - Infrastructure management - Collaboration **Visualization** - Graphs and Charts - Score Cards - Dashboards - Formats - Mobile Dashboards - Public Dashboards - Private Dashboards **Analytics** - Analytics capabilities - Dasboard visualizations **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation - Multimedia Support - Multi-Modal Input Support **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Additional Functionality** - Data Mapping - Monitoring - Charting - Integration Management - Reporting/Analytics - Ad hoc Analysis - Access Controls/Permissions - API - Match & Merge - Real-Time Monitoring - Metadata Management - Pipeline Management - Job Scheduling - Dashboard Creation - Data Storage Management - Multiple Data Sources - Data Import/Export - Generative AI - Data Quality Control - Data Connectors - Customizable Reports - Single Sign On - Version Control - Visual Analytics - Accounting Integration - Real-Time Data - eCommerce Management - AI Copilot - CRM - Data Visualization - SSL Security - Search/Filter - Real-Time Analytics - Data Capture and Transfer - Collaboration Tools - Performance Management - Data Synchronization - Drag & Drop - Data Replication - Activity Dashboard - Database Support - Workflow Management - Alerts/Notifications - Predictive Analytics - Data Migration - Third-Party Integrations - Reporting & Statistics - Data Analysis Tools **Database Administration** - Provisioning - Governance - Auditing **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Maintainence** - Data Quality Management - Policy Management - Data Storage Management **Management** - Cataloging - Monitoring - Governing **Data Updates** - Historical Snapshots - Real-Time Updating **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Availability** - Scalability - Backup - Archiving - Indexing **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **Security** - Data Masking - Authentication And Single Sign-On - Data Anonymization **Performance ** - Scalability **Collaboration** - Sharing - Co-Editing - Devices **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration - Third-Party Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Additional Functionality** - Version Control - Scalability - Personalization - Data Extraction - Webhooks - API - Natural Language Processing - Fallback Handling - Drag & Drop - Multiple LLM Models - Built-in AI Assistant - Automated Testing - Data Governance - Collaboration Tools - Pre-built Templates - Agent Design Tools - Deep Learning - Model Training - Analytics - Single Sign On - Debugging - Deployment Management - Proactive Error Detection **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling - Natural Language Search - Visual Discovery - Data Blending - Data Blending **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks - Data Discovery **Data Management** - Data Replication - Advanced Data Analytics **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **Generative AI** - AI Text Generation - AI Text Summarization **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making - Third-Party Integrations - Active Directory Integration **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity - Multiple Data Sources - Query Builder **Additional Functionality** - AI Copilot - Reporting & Statistics - Data Migration - Archiving & Retention - Data Capture and Transfer - Audit Trail - Data Synchronization - Single Sign On - Customizable Reports - Data Profiling - Authentication - Role-Based Permissions - Tagging - Secure Data Storage - Data Security - HIPAA Compliant - SSL Security - Risk Assessment - Data Mapping - Search/Filter - Self Service Portal - Multiple Data Sources - Document Storage - API - Activity Tracking - Automatic Backup - Visual Analytics **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services - Real-Time Analytics - Reporting/Analytics - Real-Time Analytics - Reporting/Analytics - Visual Analytics **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support - Database Support **Additional Functionality** - Parallel Processing - Ad hoc Analysis - Multiple Data Sources - API - In-Database Processing - Monitoring - Real-Time Reporting - Data Synchronization - Real-Time Monitoring - Data Connectors - Performance Metrics - Ad hoc Reporting - Alerts/Notifications - Access Controls/Permissions - Drag & Drop - Data Visualization - Data Import/Export - Match & Merge - Data Transformation - Secure Data Storage - Data Extraction - AI Copilot - Customizable Reports - Activity Dashboard - Data Migration - In-Memory Processing - Data Mapping **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics - Ad hoc Query - Visual Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management **Data Updates** - Historical Snapshots - Real-Time Updating - Scheduled/Automated Reports - Customizable Reports - Predictive Analytics - Data Management - Real-Time Data **Additional Functionality** - Drag & Drop - Secure Data Storage - Data Import/Export - Customizable Branding - Dashboard Creation - Access Controls/Permissions - Real-Time Reporting - Metadata Management - Forecasting - Data Storage Management - Ad hoc Analysis - Ad hoc Reporting - Data Migration - AI Copilot - Self Service Data Preparation - Search/Filter - Data Mapping - Monitoring - Sentiment Analysis - User Management - Sales Trend Analysis - Charting - Reporting & Statistics - Performance Metrics - Alerts/Notifications - Data Extraction - Widgets - Storytelling - Trend Analysis - Collaboration Tools - Financing Management - Self-service Analytics - KPI Monitoring - Dashboard - API - Data Integration - Scorecards - Data Management - Task Management - Progress Tracking - Data Connectors - Data Visualization - Profitability Analysis - Project Tracking - Strategic Planning - Goal Setting/Tracking - Publishing/Sharing - Predictive Analytics - Real-Time Monitoring - Templates - Mobile Access - Customizable Templates - Workflow Management - Financial Reporting - Audit Management - OLAP - Single Sign On - Data Synchronization **Additional Functionality** - Customizable Branding - Natural Language Processing - Data Extraction - AI Copilot - Ad hoc Reporting - Real-Time Reporting - Publishing/Sharing - Collaboration Tools - Strategic Planning - Self Service Data Preparation - Trend Analysis - Text Analysis - Real-Time Monitoring - Data Synchronization - Widgets - Benchmarking - Performance Metrics - Data Mapping - Generative AI - Trend/Problem Indicators - Customizable Templates - Access Controls/Permissions - Data Import/Export - Profitability Analysis - OLAP - Alerts/Notifications - Search/Filter - Drag & Drop - Data Connectors - Multiple Data Sources - Key Performance Indicators - Dashboard Creation - Role-Based Permissions - Data Mining - Forecasting - Ad hoc Query **Additional Functionality** - KPI Monitoring - Secure Data Storage - Multiple Data Sources - Data Visualization - Single Sign On - Natural Language Search - Search/Filter - Alerts/Notifications - AI Copilot - Real-Time Notifications - Collaboration Tools - Widgets - Performance Metrics - Ad hoc Reporting - Activity Tracking - Reporting/Analytics - Customizable Branding - Data Aggregation - Dashboard Creation - Data Connectors - Customizable Templates - Data Synchronization - API - Forecasting - Interactive Elements - Single Page View - Data Import/Export - Drag & Drop - Access Controls/Permissions - Trend Analysis - Workflow Management - Third-Party Integrations - Functions/Calculations - Historical Reporting - Data Capture and Transfer - Visual Discovery - Real-Time Updates - Real-Time Reporting - Data Mapping - Relational Display - Visual Analytics - OLAP - Ad hoc Query - Reporting & Statistics ## Top Databricks Alternatives - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.2/5.0 (190 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (713 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (354 reviews)