# 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:** 746 ## 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 over 60% of the Fortune 500 — rely on Databricks 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 Data Intelligence Platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase and Unity Catalog. ## Databricks Pros & Cons **What users like:** - Users appreciate the **powerful AI features and seamless integrations** of Databricks, enhancing data management and security. (288 reviews) - Users find the **ease of use** in Databricks exceptional, simplifying model hosting and integration processes efficiently. (278 reviews) - Users appreciate the **seamless integrations** of Databricks with AWS and Azure, enhancing collaboration and efficiency in data management. (189 reviews) - Users value the **collaborative environment** of Databricks, enabling seamless real-time teamwork for data engineers and analysts. (150 reviews) - Users value the **effective data management features** of Databricks, enhancing usability and decision-making capabilities significantly. (150 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (148 reviews) - Analytics (139 reviews) - Machine Learning (136 reviews) - ML Integration (135 reviews) - Scalability (134 reviews) **What users dislike:** - Users face a **steep learning curve** with Databricks, making organizational adoption and resource management challenging. (112 reviews) - Users find the **costs for Databricks to be quite high** , especially when dealing with large datasets. (97 reviews) - Users note a **steep learning curve** for Databricks, hindering swift adoption and effective resource management. (96 reviews) - Users find the **missing features** of Databricks limiting, impacting functionality and ease of use for development. (69 reviews) - Users struggle with the **complexity** of learning and troubleshooting in Databricks, which hinders efficient usage and integration. (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) - Complex Setup (45 reviews) ## Databricks Reviews ### 1. Review **Rating:** 5.0/5.0 stars **Reviewed by:** Homero F. | Professor particular, Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2026 ### 2. Powerful Unified Analytics with Seamless Governance and Effortless Scaling **Rating:** 4.5/5.0 stars **Reviewed by:** Akhil S. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its powerful and unified analytics ecosystem. Features like Unity Catalog and Metastore make data governance and access control seamless, while the Lakehouse architecture combines the best of data lakes and warehouses. PySpark support, dbutils, and collaborative workspaces make development efficient, and serverless compute simplifies scaling without infrastructure overhead. **What do you dislike about Databricks?** What I dislike about Databricks is the slow startup time of all-purpose clusters, which can interrupt workflow and reduce productivity. Additionally, Git integration can feel a bit sluggish at times, especially during commits or syncing, making version control less seamless than expected. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of managing end-to-end data workflows by providing a unified platform for data engineering, data science, and analytics. It allows seamless data processing, transformation, and model development within a single environment. This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable. **Official Response from Jess Darnell:** > We're pleased to hear that Databricks is simplifying your data workflows and providing seamless integration with Azure Data Factory. We take note of your concerns about slow startup times and Git integration, and we are committed to optimizing these aspects to ensure a smoother experience for our users. Your input helps us prioritize enhancements that align with our users' needs. ### 3. Databricks: Unified Lakehouse Platform with Powerful Spark Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Tejaswini R. | Data Management Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** i am working as a Data management specialist and using databricks regularly for handling data pipelines, large scale data processing, and governance tasks, i like most is that databricks provides a single unified platform for data engineering , analytics and AI , instead of using multiple tools. everything is available in one place, the lakehouse architecture is very useful because it combines data warehouse and data lake capabilities, so we can manage both structured and unstructured data efficiently. performance is very strong, especially with apache spark, it can process very large datasets quickly. i also like the collaborative notebooks where teams can work together using SQL, python or scala. **What do you dislike about Databricks?** one issue is that it has a steep learning curve, especially for new users who are not familiar with spark or distributed systems. cost management can also be challenging , it clustered are not optimized properly it can become expensive, sometimes too many features and configuration can makes it complex to manage for smaller teams. sometimes the platform feel complex. with many feature and configuration which can be difficult for smaller teams to manages. it it a powerful platform, but complexity and cost control are the main challenges in daily use. **What problems is Databricks solving and how is that benefiting you?** databricks solves the problem of managing large scale data processing and multiple data tools in a single platform, before using databricks data was spread across different system. and we has to use separate tools for ETL, storage and analytics, this made workflow complex and difficult to manage, databricks brings everything together in one place, so we can build data pipeline , process large datasets, and run analytics without switching tools. it also handles big data efficiently using distributed processing, which reduces processing time and improves performance, for me it has made data workflows more organized, reduces manual effort, and improved data reliability. it helps in faster data processing, better collaboration and more efficient data management. **Official Response from Jess Darnell:** > It's great to hear that Databricks has helped centralize your data processing and tools, making your workflows more organized and efficient. We're committed to providing a platform that simplifies data management and improves collaboration for our users. We understand that the learning curve and cost management can be challenging, especially for new users and smaller teams. We're continuously working to improve user experience and provide cost-effective solutions for our customers. ### 4. Seamless, Collaborative Platform That Scales for Data Engineering and ML **Rating:** 4.0/5.0 stars **Reviewed by:** Krish G. | student, Small-Business (50 or fewer emp.) **Reviewed Date:** April 15, 2026 **What do you like best about Databricks?** Databricks' ability to seamlessly integrate everything is what I find most appealing. When working on actual projects, it really makes a big difference that you don't have to switch between several tools for data engineering, analysis, and machine learning. The collaborative element is very noteworthy. Teams may easily collaborate without things becoming messy thanks to the notebooks' fluid and dynamic feel. For significant data work, it resembles Google Docs almost exactly. I also really like how efficiently it manages large amounts of data without making it seem difficult. Even when working with large datasets, the platform feels user-friendly and can be scaled up when necessary. Additionally, it makes perfect sense from an AI/ML standpoint. You are able to construct, **What do you dislike about Databricks?** Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. **What problems is Databricks solving and how is that benefiting you?** The fragmentation issue in the data and AI workflow is primarily resolved by Databricks. In the past, data storage, processing, analysis, and machine learning were usually done using different tools, and getting them all to cooperate was frequently difficult and time-consuming. Databricks eliminates a lot of the friction by combining all of it into a single platform. That makes the developing process much more seamless for me. I don't have to worry about compatibility problems or waste time switching between environments. I can perform transformations, clean data, and create models all in one location, which reduces setup time and maintains organization. It also addresses the difficulty of handling massive amounts of data. I can rely on its distributed computing capabilities to manage demanding workloads rather than worrying about infrastructure or performance optimization from scratch. This allows me to concentrate less on resource management and more on finding a solution to the real issue. Collaboration is another major issue it resolves. Sharing code, findings, and experiments can get disorganized in team environments. Because everything is consolidated with Databricks, it's simpler to work together, monitor changes, and maintain alignment. All things considered, it helps me by cutting down on complexity, saving time, and allowing me to concentrate more on developing solutions—whether they be analytics, machine learning models, or data pipelines—instead of handling the overhead of maintaining numerous tools and platforms. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' seamless integration and collaborative features appealing. We understand that the platform may feel overwhelming initially, but we offer comprehensive resources and support to help users get up to speed. Regarding cost control, we recommend leveraging our documentation and best practices to optimize cluster usage and auto-scaling settings. Your feedback is appreciated and we are committed to continuously improving the user experience! ### 5. Databricks as a Hands On Data Engineer: Solving Real World ETL, Governance, and Lakehouse Challenges **Rating:** 5.0/5.0 stars **Reviewed by:** KAVIN P. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 08, 2026 **What do you like best about Databricks?** I believe the most attractive thing about Databricks lies in its all-in-one nature, which makes data management easier. Previously, when I used several tools for data-related activities, the experience was not great but here everything seems to be interconnected and straightforward. The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view. In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required. There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently. **What do you dislike about Databricks?** One aspect of Databricks that i dislike is its UI. As you spend longer in using the tool, moving between notebooks and clusters becomes annoying at times. The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects. There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines. At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be. **What problems is Databricks solving and how is that benefiting you?** The most important issue that Databricks resolves is the issue of working with large volumes of data and maintaining consistency. Previously, there were separate processes for data engineering, analytics, and machine learning operations, requiring separate tools and made it difficult for me to handle but now these all are in one place, another one critical issue solved by Databricks is the issue of processing large data volumes. Utilizing the Spark, and distributed computing allows it to perform the tasks that were extremely slow on legacy systems I worked with. This has helped speed up my pipeline, although some time the delays occur.Collaboration is also another problem that Databricks addresses. Multiple users can collaborate on the same notebook or data sets. Collaboration previously was confusing, and now it is easy and good and easy and easly understandable and mainly easy sharing notebooks and assets.Scalability is another issue resolved by Databricks; there is no need to pay attention to infrastructure management. Cluster scaling depends on user requirements, saving time. Previously, it was necessary to pay more attention to the configuration of the infrastructure. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' all-in-one nature and interconnectedness beneficial for data management to help your team save time. We appreciate your feedback on the advantages of utilizing notebooks and the efficiency in handling big data. ### 6. Unified Databricks Workspace That Streamlines Collaboration and Complex Data Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Neeraj Kumar N. | AI Data Specialist | Transcription & Annotation Expert | AI Model Training at Sigma AI, Mid-Market (51-1000 emp.) **Reviewed Date:** April 12, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning into one unified workspace. I find collaboration much easier with shared notebooks, and the seamless integration with big data tools saves me time. It simplifies complex workflows while still offering powerful capabilities when I need them. **What do you dislike about Databricks?** One thing I dislike about Databricks is that it can feel expensive, especially for smaller projects or teams. I also find cluster configuration and cost management a bit complex at times. The interface, while powerful, can be overwhelming for beginners, and debugging distributed jobs isn’t always as straightforward as I’d like. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of handling large-scale data processing, analytics, and machine learning in one place. For me, it removes the hassle of managing separate tools and infrastructure. I benefit by working more efficiently, collaborating easily with my team, and turning complex data into useful insights faster, with less operational overhead overall. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' unified workspace and collaboration features valuable for your work. We understand your concerns about cost and complexity, and we're continuously working to improve in these areas. ### 7. Reliable data platform with powerful pipeline support **Rating:** 4.5/5.0 stars **Reviewed by:** Chandhuru B. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 06, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning together in one clean workspace. It saves time, makes collaboration easier, and helps teams move faster with large data. **What do you dislike about Databricks?** What I dislike about Databricks is that Auto Loader can become frustrating when source data changes frequently, especially if column names or datatypes shift without warning. For example, a field like customer_id may suddenly come in as cust_id, or a column that was previously a string may start arriving as an integer, which can cause schema drift and break downstream processing. I also find it inconvenient when schema inference is not fully accurate, such as when nested JSON or semi-structured data is read incorrectly, because it then requires extra manual fixes and maintenance to keep pipelines running smoothly. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of building and managing data pipelines at scale without so much manual effort. It helps with reliable ingestion, schema evolution, and orchestration, so teams can process data faster and keep pipelines more stable even when source files change. For me, that means less time spent fixing broken jobs and more time focusing on transforming and using the data. It also benefits me by making batch and streaming workflows easier to manage in one platform, which is especially useful when data keeps changing. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a reliable platform for data engineering, analytics, and machine learning. We understand the frustration with Auto Loader when dealing with frequently changing source data. We are continuously working to improve schema inference accuracy and handling of nested JSON or semi-structured data to minimize manual fixes and maintenance for our users. ### 8. Databricks: Unified Platform for Data Processing and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Banu Prakash M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks brings everything into one place, making it unnecessary to use different tools for data processing, analytics, and pipeline work. It handles large data well, and we don't have to worry about managing clusters manually. Additionally, Databricks handles collaboration and experimentation well, making it easy to try out new things. **What do you dislike about Databricks?** In my point of view, the one area that can be improved is cost management. If clusters aren't monitored carefully, costs can increase faster than expected. One improvement that would help is better visibility into costs at a more detailed level. More built-in alerts or recommendations when costs start increasing unexpectedly would also be helpful. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us handle large datasets and build data pipelines. It simplifies data processing, transforming, and analysis using Spark and SQL, all in one place. It solves the problem of slow data processing spread across systems, managing infrastructure automatically and facilitating collaboration and experimentation. **Official Response from Janelle Glover:** > We're thrilled to hear that Databricks has been beneficial for handling large datasets and simplifying data processing and analysis for you. We appreciate your feedback on cost management and will explore ways to enhance cost visibility and provide better monitoring tools. ### 9. A Reliable Workhorse for Data Engineering and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Supriya M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** The unified platform approach is what I appreciate most. Having notebooks, data engineering pipelines, ML workflows, and SQL analytics all in one place saves a ton of time instead of juggling multiple tools. The collaborative notebooks make it easy to share work with teammates, and the cluster management has gotten a lot smoother over time. Delta Lake integration is also a huge plus for keeping our data reliable and consistent. **What do you dislike about Databricks?** The cost can get out of hand pretty quickly if you're not careful with cluster sizing and uptime. It's not always obvious how to optimize spending, and the pricing model feels complex. The learning curve for new team members is also steeper than I'd like, especially for people who aren't already familiar with Spark. Sometimes the UI can feel sluggish when working with larger notebooks, and debugging job failures could be more straightforward. **What problems is Databricks solving and how is that benefiting you?** Databricks helps me resolve complex ETL pipeline failures and persistent data quality issues in supply chain analytics by unifying batch and streaming processing from SAP systems with Delta Live Tables. It also removes a lot of the infrastructure management headaches thanks to auto-scaling clusters, so I can stay focused on writing code for multi-terabyte workloads instead of constantly worrying about cluster sizing. For my manufacturing data projects, Databricks accelerates development cycles from weeks to days via collaborative notebooks and DLT pipelines, enabling faster Power BI reporting and stakeholder decisions. Unity Catalog centralizes governance across Azure and SAP sources, preventing schema drift that plagued prior Hive-based lakes. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of the unified platform approach and the time-saving features of Databricks. We understand your concerns about cost management and the learning curve, and we're continuously working to simplify our pricing model and improve the onboarding experience for new team members. It's great to hear how Databricks is helping you resolve complex ETL pipeline failures and accelerating development cycles for your manufacturing data projects. ### 10. All-in-One Powerhouse with Room for Pricing Clarity **Rating:** 4.5/5.0 stars **Reviewed by:** Thoufeeq A. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with. **What do you dislike about Databricks?** I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time. **What problems is Databricks solving and how is that benefiting you?** Databricks consolidates multiple tools into one platform, making it powerful and convenient. I can manage permissions with row-level security and easily run experiments to select the best models, all in one place. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We understand your concerns about the pricing calculator and will take your feedback into consideration to improve the clarity of our documentation. ## 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 ## Databricks Integrations - [Agentforce 360 Platform (formerly Salesforce Platform)](https://www.g2.com/products/agentforce-360-platform-formerly-salesforce-platform/reviews) - [Agentforce Sales (formerly Salesforce Sales Cloud)](https://www.g2.com/products/agentforce-sales-formerly-salesforce-sales-cloud/reviews) - 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[ThoughtSpot](https://www.g2.com/products/thoughtspot/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/reviews) - [Zendesk Sunshine](https://www.g2.com/products/zendesk-sunshine/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 - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (664 reviews) - [Teradata Vantage](https://www.g2.com/products/teradata-teradata-vantage/reviews) - 4.3/5.0 (340 reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,156 reviews)