--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1335 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: 1335 scale: '5' date_modified: '2026-06-26' 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:** 1,335 ## About Databricks Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie 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 **What users like:** - Users appreciate the **seamless integration** of Databricks with AWS and its powerful features for data management and security. (288 reviews) - Users find Databricks to offer **exceptional ease of use** , making data integration and management seamless and efficient. (278 reviews) - Users praise the **seamless integration** of Databricks with AWS and Azure, enhancing collaboration and efficiency in data management. (189 reviews) - Users appreciate the **excellent collaboration** in Databricks, facilitating real-time teamwork for data engineers and analysts. (150 reviews) - Users value the **effective data management features** of Databricks, enhancing usability and decision-making with integrated tools. (150 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (148 reviews) - Users value the **integrated analytical features** of Databricks, enhancing operations and providing comprehensive insights on technology. (139 reviews) - Machine Learning (136 reviews) - ML Integration (135 reviews) - Scalability (134 reviews) **What users dislike:** - Users face a **steep learning curve** with Databricks, complicating initial adoption and resource management. (112 reviews) - Users find the **costs to be high** for Databricks, especially when dealing with large data sets. (97 reviews) - Users face a **steep learning curve** with Databricks, making initial adoption challenging and requiring specialized support. (96 reviews) - Users are disappointed by the **missing features** in Databricks, limiting customization and complicating development processes. (69 reviews) - Users find the **complexity** of Databricks challenging due to steep learning curves and integration limitations. (64 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (61 reviews) - Performance Issues (57 reviews) - Poor UI Design (53 reviews) - Difficult Learning (51 reviews) - Users experience a **complex setup** process initially, but support helps to simplify the experience over time. (45 reviews) ## Databricks Reviews ### 1. Great Spark Scaling, But Slow Cluster Boot Times **Rating:** 5.0/5.0 stars **Reviewed by:** Raj P. | Marketing Coordinate, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** I start my day by opening up Databricks notebooks to clean and process raw data logs. The collaborative workspace is easily one of my favorite parts. When my team and I are working on the same notebook, the real-time co-authoring makes debugging incredibly easy. The Managed Apache Spark engine is a lifesaver for training heavy machine learning models because the clusters scale up automatically without me needing to worry about infrastructure. I just write my Python or SQL code, and it handles the heavy lifting in the background. Once my ad-hoc analysis is done, it is seamless to connect the data directly to Power BI so the business teams can view live dashboards. **What do you dislike about Databricks?** The biggest pain point is definitely when I have an urgent report due or a quick request from a manager first thing in the morning, and the cluster takes 5 to 10 minutes to spin up. Sitting there staring at the "Starting Cluster" status when you are on a tight deadline is deeply frustrating. Also, the pricing and DBU (Databricks Unit) model can be confusing to wrap your head around, and costs quickly spiral if you forget to set up auto-termination properly. I also occasionally run into version conflicts when installing custom Python libraries on specific runtimes, which can eat up half an hour just troubleshooting environment errors. **What problems is Databricks solving and how is that benefiting you?** Before using Databricks, our workflow was completely fragmented, with data engineering pipelines running in one place and our data models sitting on local machines, causing massive data redundancy. Switching to the Lakehouse architecture completely centralized everything and streamlined our daily routines. It has removed the traditional wall between data engineers and data analyst also data science, allowing us to collaborate seamlessly on a single platform. My honest assessment is that while it is expensive and the boot times are annoying, it is an incredibly robust platform that is hard to beat for heavy data workloads and serious data science. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on your experience with Databricks. We are thrilled to hear that you are enjoying the collaborative workspace and the Managed Apache Spark engine for scaling up clusters effortlessly. We understand your frustration with slow cluster boot times and the challenges with pricing clarity and version conflicts. Your insights are invaluable to us, and we are continuously working to enhance our platform. We appreciate your patience and understanding as we strive to improve the user experience. If you encounter any issues or have suggestions for improvement, please feel free to reach out to our support team. We are here to assist you and ensure that your data processing tasks are as efficient as possible. Thank you for choosing Databricks for your data workloads and data science needs. ### 2. Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines **Rating:** 4.5/5.0 stars **Reviewed by:** Konjengbam M. | BDR, Financial Services, Mid-Market (51-1000 emp.) **Reviewed Date:** June 10, 2026 **What do you like best about Databricks?** I love this platform for its capability to handle big pool of data efficiently. I love the idea of Data Lakehouse of this platform. The Collaborative work supported by this platform greatly enhances productivity and team work. In our context of using the Databricks SQL query we can easily identify the best match of entrepreneurs needed to avail a specific scheme. This saves both time and increases efficiency. The capacity of this platform to analyze the past performance and trends enable us to map out a highly targeted approach that is really efficient. I love the capability of this platform as it can identify probable stress asset which might be an issue for future investment. This makes us rethink our strategy and approach towards the best and efficient way forward. I feel that the platform have a user friendly interface. I love the integration ability of this platform as it integrates with most of the major platform. This makes this platform more robust and powerful. The onboarding of this platform is also easy as we could easily login with our Google ID. Other than this I love the ability of this platform to create pipeline. The capability of this platform to create agents also ease up tasks. It also enhances capability to handle work load more effectively and efficiently. **What do you dislike about Databricks?** I love most part of this platform but I have to admit that user will need a some training for the user to be more efficient. I feel that having a more technical experience will adapt well with this platform. I also wish that the pricing of this platform was also more moderate. Frankly saying the accuracy of output of this platform depends on how clean the Data is . So there is always a chance of spilling in the unclean data. This will directly impact the result. **What problems is Databricks solving and how is that benefiting you?** Frankly this platform translates into assistance in accurate decision making. The decision made by using this platform directly impacts productivity and averts risk. **Official Response from Jess Darnell:** > We're thrilled to hear that you find Databricks powerful and efficient for handling big data, collaboration, and creating pipelines. We appreciate your feedback on the user-friendly interface, integration capabilities, and easy onboarding process. ### 3. I love databricks **Rating:** 5.0/5.0 stars **Reviewed by:** taka b. | engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. **What do you dislike about Databricks?** One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve challenges related to managing and analyzing large volumes of data across multiple sources. It simplifies ETL pipelines, improves data reliability through Delta Lake, and enables scalable machine learning. This has significantly reduced the time our team spends on data preparation and model training, leading to faster business insights and better decision-making. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks has helped solve challenges related to managing and analyzing large volumes of data for your team, leading to faster business insights and better decision-making. We appreciate your support and are committed to continuously improving our platform. ### 4. Databricks Simplified Our Large-Scale Data Workflows and Team Collaboration **Rating:** 5.0/5.0 stars **Reviewed by:** Kareena M. | Social Media Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** June 04, 2026 **What do you like best about Databricks?** What I personally liked most was how easy it became to handle large-scale data workflows in one place. Earlier we were using separate tools for processing, notebooks, and collaboration which became messy very fast. With Databricks, the team could collaborate on notebooks, run pipelines, and experiment with ML models without constantly switching environments. **What do you dislike about Databricks?** The pricing can become expensive if clusters are not managed properly, especially for smaller teams or startups. There’s definitely a learning curve as well if someone is coming from traditional SQL-only environments. **What problems is Databricks solving and how is that benefiting you?** We mainly used Databricks for centralized data engineering and analytics workflows. It helped us reduce dependency on multiple tools and improved collaboration between analysts and engineering teams. One major improvement was faster ETL processing and better visibility into data pipelines. It also made it easier to experiment with AI/ML use cases because the infrastructure setup was already integrated into the platform. **Official Response from Aunalisa Arellano:** > It's fantastic to hear how Databricks has benefited your team by centralizing data engineering and analytics workflows, reducing dependency on multiple tools, and improving collaboration. We're pleased to hear about the improvements in ETL processing and AI/ML experimentation. ### 5. Databricks in my case: Multiple Integrations, Intuitive UI, and Reliable Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Yelnur K. | Schedule Manager, Airlines/Aviation, Mid-Market (51-1000 emp.) **Reviewed Date:** May 19, 2026 **What do you like best about Databricks?** What I like most about Databricks is its Integrations part. In workplace, we integrate Database within multiple data soucres. Also, I can't complete my review without mentioning UX and UI design, which makes the overall workflow feel intuitive and genuinely user-friendly. When it comes to speed of the processes, it never offended us. It works as expected. Comparitevly from the market pricing, the price of the service is quite reliable for us. There is a help center of the Databricks, if you can't find any answers to your questions, there are specialists that may assist you with your inqurires. As an instance, I can remember the case where we had an issue within exam process, they helped us to solve this problem. **What do you dislike about Databricks?** From dislikes the ai quality of Genie. Guys it could be improved, especially the reasoning part. Also, I can say the case when we had an issue with exam process. Specialists helped us, but it took us little discomforties. Well, **What problems is Databricks solving and how is that benefiting you?** In aviation, we utilize this software for data analysis. We automized a lot of processes, which simple workplace tools can not handle. We also, integrate with multiple tools (names which I can not mention for securirty purposes) Particularly, it helps us to analyze passenger demand by route and season. We combine and analyze big datasets using this software. Overall, a good tool. Out team is satisfied. **Official Response from Sara Steffen:** > Thank you for your detailed feedback. We're pleased to hear that Databricks has been instrumental in automating processes and analyzing big datasets for your aviation needs. We take your feedback about Genie and support processes seriously and are dedicated to making improvements in these areas. ### 6. Lakebase - Good Option for Low Latency Data Serving with Databricks Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Associate Director, Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2026 **What do you like best about Databricks?** I have setup the lakebase which is in sync with lakehouse for enterpise application low latency access, we have enabled the below options - Lakebase using Postgres 17,HTTPS Data API is helping to access database in easier way from Databricks Apps - Integration sync between Lakehouse and Lakebase for API access - It has branching option to maintain schema or feature changes - Restore from previous point/time history - helps reducing recovery effort - Snapshots and backup support - Autoscaling compute and suspend option - Monitoring, logs and query metrics are giving visibility on active queries, performance and database health - OAuth access and postgres role based connection is helping better security and controlled access Lakebase pricing model with autoscaling and scale down option based on available compute pricing **What do you dislike about Databricks?** Lakebase all postgress features not available, so we can't directly migrate any existing postgres directly to lakebase Scale down to zero not happens instantly faced some issues like disconnect from app for short time temporary pauses Custom admin operations in database postgres are limited and not posisble **What problems is Databricks solving and how is that benefiting you?** Lakebase helps serve near real-time inventory data,availability tracking and reducing out-of-stock situation Processed Lakehouse data can be served through Lakebase for customer buying pattern, product recommendation and promotion effectiveness Transactional sales data can be made available quickly for store level dashboards, helping business teams monitor sales trend usings web apps Near real-time operational data availability applications access low latency data for pricing, promotions for TPO-TPM integrations Also we integrated the AI to include the real time context Low latency **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Genie, including its ability to bridge the gap between business and data teams, eliminate data silos, and improve cost and performance visibility. We understand your concerns about the limitations of Agent Mode and the need for further autonomy. We will work on addressing these areas to enhance your overall experience. ### 7. All-in-One Delta Lake Platform That Makes ETL Fast and Cost-Efficient **Rating:** 5.0/5.0 stars **Reviewed by:** Kavipriya S. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 27, 2026 **What do you like best about Databricks?** Delta Lake + Workflows + Unity Catalog in one platform eliminated the need for stitching together separate ingestion, transformation, and governance tools. As a data engineer, I spend more time building pipelines and less time managing infrastructure. The notebook experience and cluster auto-scaling make iterating on complex ETL fast and cost-efficient. **What do you dislike about Databricks?** Cluster spin-up times and cost predictability are still the biggest friction points for me. Cold starts can really slow down ad-hoc work, and DBU costs need close monitoring to avoid unpleasant surprises. The Workflows UI has improved a lot over time, but it still doesn’t feel as flexible as dedicated orchestrators when you’re dealing with more complex DAGs. Even so, I see these as mostly polish items—the platform’s core value easily outweighs them. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses a major fragmentation problem in our data engineering stack. Previously, we relied on separate tools for ingestion, transformation, orchestration, and governance—each with its own learning curve, maintenance overhead, and potential failure points. Now, it’s consolidated into a single platform. In practice, it helps us run large-scale ETL pipelines that process millions of records daily, with Delta Lake improving reliability through ACID transactions, schema enforcement, and time travel for debugging. It also closes the collaboration gap between data engineers and data scientists: we build the pipelines, and they can consume the same tables directly in notebooks without data duplication or sync issues. Unity Catalog resolved a long-standing governance headache by centralizing access control across workspaces. Overall, the result is faster pipeline development, fewer production incidents tied to data quality problems, and far less glue code to maintain. What used to take weeks to build and stabilize now takes days. **Official Response from Jess Darnell:** > We're delighted to hear that Databricks has consolidated your data engineering stack and improved the reliability of your ETL pipelines. We understand your feedback about cluster spin-up times and cost predictability, and we're actively working to optimize these aspects of our platform to provide a better user experience. ### 8. Powerful Low-Latency Telemetry Pipelines with Streaming Tables & Materialized Views **Rating:** 4.0/5.0 stars **Reviewed by:** Jose P. | Head of Network Strategy, Telecommunications, Enterprise (> 1000 emp.) **Reviewed Date:** May 26, 2026 **What do you like best about Databricks?** In a telco environment handling massive data volumes from fixed and mobile networks (GPON, 4g/5g Core, and RAN) ingesting unstructured or semi-structured frequency telemetry incrementally from our virtualized functions like vEPC, vCPE or VHGW) with minimal setup. My team works closely with virtualized network functions and Multi-access Edge Computing. Features like Streaming Tables and Materialized Views help us to build low-latency pipelines that process network performance metrics near real-time, helping us monitor network KPIs and QoS efficiency. Because my team's core experties lies in network deisgn and system virtualization rather than database administration, Predictive Opimization and Liquid Clustering are highly beneficial. Tehy autonomously handle table maintenance, file compaction, and data layout optimization freeing up our resources to focus on network architecture. **What do you dislike about Databricks?** Virtualized network functions, routers, and disaggregated hardware frequently undergo software updagrades, which often introduce sublte changes in telemetry output schemas. When using structured streaming or auto loader these schema drifts cause our streaming queries to fail, requiring a manual restart of the stream to re-plan the schema. When we need to update the logic of a complex network KPI defined within a materialized view, any change to the query triggers a full recomputation of the view. Given the massive scale of telecom transaction datasets, this can result in noticeable compute costs. We rely on a variety of data tools within our ICT ecosystem, not all solutions featured in Partner Connect natively support Unity Catalog. This can crete integration and governance hurdles when we try to connect certain third-party analytics and data preperation tools to our secured data lake. **What problems is Databricks solving and how is that benefiting you?** We ingest continous streas of performance data from virualized network functions and traditional transport layers. By building streaming pipelines, we can monitor virtualized cpres and routers to identify anomalies or degredations in network traffic. Aligning with my interest in Network AI and Machine learning, our data scientists use the patform to develop predictive models. We train models on historical GPON/DSL line failures, mobile cell tower loads, and customer usage patterns to predict network congestion, schedule proactive maintenance and mitigate customer chirn across customer segments. As an evangelist for tech evolution, I use the platform to bridge the gap between our core network engineering teams and business units. By connecting business semantics and establisihng secure Delta Sharing protocols, we provide business analysts and decision makers with giverned, self service access to network insights without risking security compliance. **Official Response from Jess Darnell:** > It's fantastic to hear how Databricks is helping you ingest and process continuous streams of performance data, develop predictive models, and bridge the gap between network engineering teams and business units. We're committed to providing solutions that benefit our users in various aspects of their work. ### 9. Making data systems less messy with a unified Lakehouse approach **Rating:** 5.0/5.0 stars **Reviewed by:** Hunar M. | Data Analyst, Geospatial Intelligence - Data & Analytics, Enterprise (> 1000 emp.) **Reviewed Date:** May 21, 2026 **What do you like best about Databricks?** The ecosystem. What I like most about Databricks is how it removes a lot of the usual mess you run into with data work. Instead of juggling separate tools for engineering, analytics, and ML—and then spending extra time getting them to talk to each other—it brings everything into one place. That alone cuts down a lot of friction and saves time. I also like the Lakehouse idea because it feels genuinely practical: you don’t have to choose between a data lake and a warehouse. You can work with one unified setup and still get performance when you need it. On a day-to-day level, it’s also nice that different teams can collaborate in the same environment without constantly copying data around or rebuilding pipelines. Overall, it keeps things simpler and faster, especially when you’re iterating. **What do you dislike about Databricks?** What I don’t like about Databricks is that it can feel a bit heavy when you’re just trying to do something simple. There’s a lot going on under the hood, and while that’s great for scaling, it also comes with a learning curve. Things like clusters, configurations, and job setup take some time to get comfortable with. Cost is another concern. Usage can creep up quickly if you’re not actively monitoring it, especially when teams can spin up compute freely. And at times, the overall experience feels a little fragmented across notebooks, jobs, and repos, rather than being one smooth, unified flow. So, yes—it’s powerful, but it definitely takes discipline to keep things clean, efficient, and under control. **What problems is Databricks solving and how is that benefiting you?** What Databricks really solves for me is the usual friction that shows up when data systems are spread across too many tools. Instead of running one system for ingestion, another for storage, something else for transformation, and then separate setups again for analytics and ML, it brings most of that into one place. That means I don’t have to keep moving data around or constantly worry about things drifting out of sync. From a solution architecture perspective, that’s a big win because it simplifies the overall design. Rather than stitching together a bunch of systems, you can build around a single Lakehouse setup that supports multiple use cases. It’s easier to scale, easier to govern, and overall just easier to reason about. On a day-to-day basis, it also means I spend less time on infrastructure and plumbing and more time thinking through how to design good data models and pipelines. And because everyone is working from the same data, there’s much less confusion and rework between teams. Overall, it removes a lot of the noise and lets me focus on building solid, scalable data solutions. **Official Response from Jess Darnell:** > We're glad to hear that you find our ecosystem and Lakehouse approach beneficial for simplifying and unifying your data work. We understand your concerns about the learning curve and cost, and we're continuously working to improve the user experience and provide cost-effective solutions. Thank you for sharing your thorough feedback with us. ### 10. Databricks centralizes data, analytics, and AI **Rating:** 5.0/5.0 stars **Reviewed by:** Leonardo Q. | RPA Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 16, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it centralizes data engineering, analytics, and AI in a single platform, which greatly facilitates the workflow on a daily basis. The integration between notebooks, pipelines, and distributed processing makes development faster and more organized, especially in projects with a large volume of data and automations. Another point that I consider very strong is the experience with Apache Spark, integrated in a simplified way. Even in more complex scenarios, the performance is usually excellent, allowing large-scale data processing with good stability and scalability. This greatly helps in integrations, ETLs, and analyses that, in other solutions, would require much more effort. **What do you dislike about Databricks?** Although I quite like the platform, some aspects of Databricks can still be challenging. The main one is the cost, especially in environments with intensive processing or when clusters are not well optimized. Without more rigorous usage control, expenses can increase rapidly. Another aspect is the learning curve, which can be steep for teams that are starting in the distributed data ecosystem. Concepts related to Spark, clusters, optimization, and resource management require time to adapt, especially for those coming from more traditional tools. In UI/UX, although the interface is generally good, some administrative processes and more advanced configurations can seem confusing at first. In certain scenarios, identifying performance or permission issues may also require more technical knowledge. **What problems is Databricks solving and how is that benefiting you?** Databricks has primarily helped to solve problems related to the centralization, processing, and analysis of large volumes of data. Previously, many processes were distributed among different tools, which made integrations, maintenance, and governance difficult. With Databricks, a large part of the data engineering, analytics, and AI workflow can be concentrated on a single platform, bringing more consistency to daily work. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks centralization of data, analytics, and AI to be beneficial for your workflow. We understand the importance of integration and simplification, and we're committed to providing a platform that meets your needs. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [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/databricks-review-10240900?section=pricing&secure%5Bexpires_at%5D=2026-06-26+14%3A03%3A42+-0500&secure%5Bsession_id%5D=c751b327-e69f-49da-bb19-475033c465aa&secure%5Btoken%5D=16f252bd3680c0087ff143e718beea1da8da19a598ad483f863f5a7f8f3af15f&format=llm_user) ## Databricks Integrations - 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[Microsoft SQL Server](https://www.g2.com/products/microsoft-sql-server/reviews) - [Microsoft Teams](https://www.g2.com/products/microsoft-teams/reviews) - [MySQL](https://www.g2.com/products/mysql/reviews) - [ObjectWay SpA](https://www.g2.com/products/objectway-spa/reviews) - [Pega Platform](https://www.g2.com/products/pega-platform/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [PowerBI Portal](https://www.g2.com/products/powerbi-portal/reviews) - [Prophecy](https://www.g2.com/products/prophecy-prophecy/reviews) - [Salesforce Agentforce](https://www.g2.com/products/salesforce-agentforce/reviews) - [SAP Ariba](https://www.g2.com/products/sap-ariba/reviews) - [SAP ECC](https://www.g2.com/products/sap-ecc/reviews) - [Seamless (formally Seamless.AI)](https://www.g2.com/products/seamless-formally-seamless-ai/reviews) - [ServiceNow IT Service Management](https://www.g2.com/products/servicenow-it-service-management/reviews) - [Sisense](https://www.g2.com/products/sisense/reviews) - [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [Spotfire Analytics](https://www.g2.com/products/spotfire-analytics/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/reviews) ## Databricks Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Management** - Data dictionary - Data Replication - Query Language - Data Modeling - Performance Analysis **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **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 **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 **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Maintenance** - Data Migration - Backup and Recovery - Multi-User Environment **Security** - Access Control - Roles Management - Compliance Management **Operations** - Metrics - Infrastructure management - Collaboration **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 **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **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 **Security** - Data Encryption - User Access Control **Maintainence** - Data Quality Management - Policy Management **Management** - Cataloging - Monitoring - Governing **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization **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 **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **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 **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **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 **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Generative AI** - AI Text Generation - AI Text Summarization **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 **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **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 **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top Databricks Alternatives - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (707 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (355 reviews)