--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1366 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: 1366 scale: '5' date_modified: '2026-08-14' 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:** 1,366 ## 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 **What users like:** - Users praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. (192 reviews) - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. (154 reviews) - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. (141 reviews) - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. (114 reviews) - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. (112 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 note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. (78 reviews) - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. (71 reviews) - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. (64 reviews) - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. (45 reviews) - Users face **complex setup** challenges initially, though support helps simplify the experience over time. (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. 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.) **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. ### 2. Secured Data Access and Integration with Databricks **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Airlines/Aviation | Mid-Market (51-1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** I like that Databricks provides a unified platform where we can bring together sources from various systems and utilize them collectively to solve problems. This integration capability is quite valuable. **What do you dislike about Databricks?** Ingestion for data sources other than standard sources could be a little bit easier. Initially, it wasn't super easy, but it's come a long way. **What problems is Databricks solving and how is that benefiting you?** Databricks ensures data access through secured channels with Unity Catalog and guarantees that people accessing the data have the right authority. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Unity Catalog to be a game-changer for maintaining security across the Databricks ecosystem. Thanks for taking the time to share your thoughts! ### 3. Unified Platform Enhances Data Collaboration and Processing **Rating:** 4.0/5.0 stars **Reviewed by:** Monazah S. | DevOps Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 12, 2026 **What do you like best about Databricks?** I like the Databricks Data Intelligence Platform because it's a unified, scalable platform for data engineering, analytics, and collaboration. It simplifies the process of building and running data pipelines, handles performance-intensive Spark workloads, and promotes collaboration across teams with shared notebooks and environments. It's great for managing and processing large datasets, improving performance, and providing a consistent platform for turning raw data into insights. The initial setup was easy, thanks to helpful documentation. **What do you dislike about Databricks?** It can improve in areas like startup time for clusters, deeper visibility into performance tuning and clearer documentation for advanced configurations. **What problems is Databricks solving and how is that benefiting you?** I use the Databricks Data Intelligence Platform for large-scale data processing and analytics. It simplifies building reliable data pipelines, handles Spark workloads efficiently, reduces operational overhead, and enables team collaboration with shared notebooks. It's great for turning raw data into actionable insights. **Official Response from Janelle Glover:** > We're glad to hear that you find our platform unified, scalable, and easy to use for managing and processing large datasets. We appreciate your feedback and will take your suggestions for improvement into consideration. ### 4. Powerful Unified Platform for Data and AI, but Complex Setup and Costly for Continuous Use **Rating:** 5.0/5.0 stars **Reviewed by:** ILCHO I. | expert buyer, Airlines/Aviation, Enterprise (> 1000 emp.) **Reviewed Date:** October 16, 2025 **What do you like best about Databricks?** I like that Databricks provides a unified environment for data engineering, analytics, and machine learning. The platform makes it easy to collaborate across teams, manage large-scale data efficiently, and build advanced AI models using the same infrastructure. The integration with major cloud providers and the Lakehouse architecture make data management both flexible and scalable. **What do you dislike about Databricks?** While Databricks is a powerful and flexible platform, it can be complex to set up and manage, especially for teams without strong data engineering expertise. The cost structure can also become expensive for continuous workloads, and performance tuning sometimes requires deep knowledge of Spark and cluster optimization. Additionally, the user interface could be more intuitive for non-technical users. **What problems is Databricks solving and how is that benefiting you?** Databricks helps centralize and manage large volumes of data from different sources in a single, scalable platform. It simplifies data processing, analytics, and machine learning workflows, allowing teams to collaborate efficiently and deliver insights faster. By integrating data engineering, analytics, and AI capabilities, it reduces infrastructure complexity and accelerates the development of data-driven solutions. **Official Response from Aunalisa Arellano:** > We appreciate your positive feedback on Databricks' unified environment for data engineering, analytics, and machine learning. The integration with major cloud providers and the Lakehouse architecture are designed to provide flexibility and scalability in data management. ### 5. Worth the effort **Rating:** 5.0/5.0 stars **Reviewed by:** Shaurya J. | Marketing Manager, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** August 01, 2025 **What do you like best about Databricks?** Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline. **What do you dislike about Databricks?** While Databricks is incredibly powerful, the learning curve can be steep for non-technical users or teams new to distributed computing. The UI, though functional, can sometimes feel a bit clunky compared to more modern data platforms. Additionally, managing costs in a multi-user environment requires careful governance, especially for teams running large-scale compute-heavy jobs. **What problems is Databricks solving and how is that benefiting you?** Databricks is helping us break down data silos by centralizing data engineering, analytics, and machine learning into a unified environment. It simplifies handling large datasets, automates ETL processes, and enables real-time analytics and AI-driven insights. As a result, we’ve significantly improved our data pipeline efficiency, reduced time to insights, and empowered both data scientists and analysts to collaborate more effectively using a single platform. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks Data Intelligence Platform valuable for unifying data engineering, analytics, and machine learning. We appreciate your feedback on the learning curve and UI, and we're continuously working to improve the user experience. Thank you for sharing how Databricks is benefiting your team by centralizing data and improving efficiency. ### 6. Effortless Data Insights and Governance **Rating:** 4.5/5.0 stars **Reviewed by:** Firat S. | Data Analyst, Oil & Energy, Mid-Market (51-1000 emp.) **Reviewed Date:** January 13, 2026 **What do you like best about Databricks?** I like the Databricks Data Intelligence Platform for its data governance capabilities. The platform supports machine learning applications and offers helpful autofilling features. I also find the quick analytics code support to be a valuable aspect. **What do you dislike about Databricks?** I find it problematic that if the tables have two similar attributes and I need to choose another which isn't many-to-many, it can't handle that yet. Also, when I ask for the keys of a table, whether foreign or main, it's not able to provide the correct key. **What problems is Databricks solving and how is that benefiting you?** Databricks Data Intelligence Platform reduces the time to find relationships between tables. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with our platform's data governance and machine learning features. We understand the challenges you've mentioned and will work to enhance our capabilities in handling similar attributes and providing accurate table keys. ### 7. Empowering Data Teams with Unified Intelligence and Performance **Rating:** 4.5/5.0 stars **Reviewed by:** Laxman K. | Software Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 10, 2025 **What do you like best about Databricks?** Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably. **What do you dislike about Databricks?** While powerful, the platform has a learning curve—especially for teams unfamiliar with Spark or distributed computing. Some features (like Unity Catalog or serverless compute) can be region-specific or limited by cloud provider compatibility. Additionally, job debugging and cluster cost management can be challenging without careful monitoring and tagging, particularly in enterprise-scale projects. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the critical problem of data fragmentation by unifying data engineering, data science, analytics, and governance in one platform. Previously, we had to stitch together multiple tools—ETL frameworks, notebooks, ML platforms, and data warehouses. With Databricks, everything from ingestion to model deployment happens in one place, drastically reducing complexity and context-switching. Another key problem it addresses is scalability and performance for big data workloads. The platform’s native support for Apache Spark and Delta Lake enables reliable, fast processing of massive datasets. This helps us run ML pipelines and analytics at scale without worrying about infrastructure bottlenecks. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on Databricks Data Intelligence Platform! We're thrilled to hear that you appreciate our unified approach to data engineering, analytics, and AI/ML. We understand that the learning curve and region-specific limitations can be challenging, and we're continuously working to improve user experience and expand compatibility. Our team is dedicated to providing resources and support to help users navigate these complexities more easily. We would love to connect with you to address any specific concerns you have and explore how we can enhance your experience further. Please feel free to reach out to our support team for assistance. Thank you for choosing Databricks to streamline your data processes and empower your team! ### 8. Excellent ML Features and Data Controls in Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Aashu V. | Data Engineering Manager, Enterprise (> 1000 emp.) **Reviewed Date:** January 08, 2026 **What do you like best about Databricks?** Databricks offers useful features for machine learning engineers, such as the ability to use compute pools to run workloads efficiently. In addition, it includes the Unity Catalog, which is an excellent tool for managing data access controls. **What do you dislike about Databricks?** The product would benefit from having more built-in functions that are specifically optimized for GenAI use cases. **What problems is Databricks solving and how is that benefiting you?** We successfully consolidated data from multiple ERPs onto a single platform, which enabled us to use this unified data for data engineering, reporting, machine learning, and GenAI use cases. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks' ML features and data controls useful for your work. We appreciate your feedback about the need for more built-in functions and will take it into consideration for future updates. ### 9. Report 1100 **Rating:** 5.0/5.0 stars **Reviewed by:** Farzad E. | CyberSecurity Expert, Enterprise (> 1000 emp.) **Reviewed Date:** January 16, 2026 **What do you like best about Databricks?** I started with databricks 6 years ago and received more than 10 certifications. I liked a lot data analytics and fast calculations features of databricks. As well integration to other external tools like Power BI for reporting. **What do you dislike about Databricks?** All features were fine, but more AI-Powered features need to enhace all current features. **What problems is Databricks solving and how is that benefiting you?** Analytics, fast calculations and reporting. **Official Response from Janelle Glover:** > Thank you for being a long-time user of Databricks and for sharing your positive experience with us! We're glad to hear that you appreciate the data analytics and fast calculations features, as well as the integration with external tools like Power BI. We'll definitely take your feedback about enhancing AI-powered features into consideration for future improvements. ### 10. System monitoring, plugin availability **Rating:** 5.0/5.0 stars **Reviewed by:** Aban S. | Senior Consultant, Small-Business (50 or fewer emp.) **Reviewed Date:** May 26, 2025 **What do you like best about Databricks?** Data access is high-speed, and it has highly qualified resources. I like the ability to manage large amounts of business data, its user interface is very useful, and data analysis is a faster process. I like the ability to host modules for so many different parts. **What do you dislike about Databricks?** They require extensive experience to configure, and the quality module could be improved with better features. They require specific administration skills, making it difficult to manage application users. **What problems is Databricks solving and how is that benefiting you?** It helps businesses run their daily operations, offering a wide range of features. It also helps our organization manage databases, providing a secure environment for data. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find our platform useful for managing large amounts of business data and that it provides high-speed data access. We appreciate your feedback about the need for extensive experience to configure and the quality of modules, and we will take that into consideration for future improvements. ### 11. Transformation Journey with Databricks Data Intelligence Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Deepankar B. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 07, 2025 **What do you like best about Databricks?** As a data engineer who has been working with Databricks for the past two years, I can honestly say the platform has completely transformed the way we approach data engineering projects. Before Databricks, me and my team often faced challenges with managing large datasets and ensuring smooth collaboration between data engineers and data scientists. There were times when workflows felt disjointed, and troubleshooting issues across different tools consumed a lot of our time. Databricks has changed all of that. The collaborative notebooks feature, in particular, has been a game-changer. I can now work seamlessly with data scientists in real-time, troubleshooting issues and iterating on solutions much faster. For example, during a recent project, we were able to refine a machine learning model within days, thanks to the ability to easily share notebooks and quickly run experiments together. This level of collaboration used to take weeks with previous tools. The auto-scaling feature has been a lifesaver. I vividly remember struggling with performance issues when processing large datasets on our old infrastructure. Now, Databricks automatically adjusts resources based on workload, so we never have to worry about managing compute power. This has drastically cut down on processing times. For instance, a data transformation job that used to take hours now finishes in a fraction of the time, allowing us to deliver projects faster. Delta Lake has also been invaluable. Before we started using it, data consistency and quality were constant concerns, especially when dealing with large and varied data sources. Now, with Delta Lake, we can trust that our data is not only high quality but also easily accessible and queryable. One particular example was when we had to rebuild a complex dataset pipeline. Delta Lake allowed us to work with incremental data updates, making the process much more efficient and reliable. In short, Databricks has greatly reduced development time and improved the overall quality of our deliveries. It’s helped me streamline complex workflows, improve collaboration across teams, and most importantly, deliver data-driven solutions faster and with greater confidence. **What do you dislike about Databricks?** Cost Optimisation - While I appreciate the granular billing information provided, predicting costs for large projects or shared environments can still feel opaque. Many teams struggle to control runaway costs from idle clusters or suboptimal configurations. Introducing smarter autoscaling and recommendations tailored to our workloads would be invaluable. For instance, alerts for "idle clusters" or "cost hotspots" in our environment could proactively save budgets and improve efficiency. Simplified Governance and Security - Managing access at fine-grained levels can be cumbersome. For example, controlling who can view versus who can execute a notebook or job often requires workarounds. Audit logs are excellent, but making sense of them for actionable insights sometimes feels like solving a puzzle. Enhanced attribute-based access control (ABAC) and more intuitive UI-based controls for permission management would greatly streamline operations. User Experience - The collaborative notebook interface is one of Databricks' standout features, yet there are areas where it could be smoother. Collaboration is sometimes hindered when two users edit the same notebook. Version control feels basic compared to Git-based systems. Debugging within notebooks, especially for non-Python workloads, could use significant improvement. Adding inline commenting, conflict resolution tools, and robust debugging features would take the platform to the next level. A workspace-level activity feed to show what’s happening in shared projects would also be immensely helpful. Workflow Automation - Include AI-driven insights for optimizing workflows (e.g., spotting bottlenecks or inefficiencies). Enable easier integration with external workflow automation tools. **What problems is Databricks solving and how is that benefiting you?** The Databricks Data Intelligence Platform has revolutionized how I handle data challenges by providing a unified, scalable, and collaborative environment. It simplifies processing large datasets, unifies teams across workflows, and ensures robust security and governance, enabling seamless data integration and real-time insights. With tools like Delta Lake and MLflow, it has streamlined pipeline development and machine learning, significantly improving productivity and reducing time to value. By democratizing analytics for technical and non-technical users alike, Databricks fosters a truly data-driven culture. Its flexibility, performance, and end-to-end capabilities have been instrumental in driving impactful results for my organization. **Official Response from Aunalisa Arellano:** > We're delighted to hear that Databricks Data Intelligence Platform has transformed the way you approach data engineering projects. We greatly appreciate your positive feedback on the collaborative notebooks, auto-scaling, and Delta Lake features. We understand your concerns about cost optimization, governance and security, user experience, and workflow automation, and we will consider them as we work to improve our platform. Sincere thanks for taking the time to write thorough feedback about the platform—we love that you understand how Databricks fosters a data-driven culture! ### 12. Databricks makes Teamwork Data Super Easy **Rating:** 4.5/5.0 stars **Reviewed by:** Ethan W. | Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** May 28, 2025 **What do you like best about Databricks?** I like Databricks because it helps everyone work together on big data projects in one place. It’s easy to share ideas, do smart computer stuff, and understand lots of information as a team. **What do you dislike about Databricks?** Sometimes, it’s hard to move our old data into Databricks. If you are not used to it, learning everything can feel tricky at first. **What problems is Databricks solving and how is that benefiting you?** Databricks brings all our data together, making teamwork easier and helping us make smart decisions faster. **Official Response from Aunalisa Arellano:** > We're glad to hear that Databricks is helping your team collaborate on big data projects and making it easier to share ideas and understand information together. ### 13. Databricks - Scalability and Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Tejas B. | Student, Mid-Market (51-1000 emp.) **Reviewed Date:** January 07, 2025 **What do you like best about Databricks?** I really like Databricks Genie, It helps me to identify the error and give suggestions to resolve it. Also If I ask to imrove the current code to faster performance Genie's suggestion are helpful. It helps to implement the ETL logic in effiecient way. **What do you dislike about Databricks?** Most of the features which I use are helpful but some sql functionalities are not supported such as Update table using join. **What problems is Databricks solving and how is that benefiting you?** Switching from on-prem server to Cloud with Databricks are beneficial because of follows: 1. On prem major challenge was it's hard maintain the code version and deployment. Using Databricks it's simpler maintain the versions of code and deploy it on different environment(as it's supports GIT) 2. Easy to scale, We can easily scale up and scale down the cluster configuration which causes cost effiecncy, improve in performance in execution. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has helped you transition from on-prem servers to the cloud, providing benefits in code versioning, deployment, and scalability. We understand the importance of SQL functionalities and will strive to improve and expand our support in this area. ### 14. Easy to implement and it is updating constantly **Rating:** 4.5/5.0 stars **Reviewed by:** Johnny E. | Architecture and Data Engineering Lead, Enterprise (> 1000 emp.) **Reviewed Date:** June 11, 2025 **What do you like best about Databricks?** Because is generating new interesting features and focus on innovation **What do you dislike about Databricks?** It’s difficult to be aligned with all the features that appears. Not a official path **What problems is Databricks solving and how is that benefiting you?** Govern to consolidate and share data. Create models and solutions **Official Response from Aunalisa Arellano:** > We're glad to hear that you find the Databricks Data Intelligence Platform easy to implement and appreciate the constant updates. Thank you for taking the time to share your thoughts! ### 15. Databricks is The data and AI company **Rating:** 4.5/5.0 stars **Reviewed by:** Saurabh G. | Managing Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** January 11, 2025 **What do you like best about Databricks?** Databricks lakehouse platform is unique in 3 ways- Simple- Data only needs to exist once to support all your workloads. Open- It is based on open standards to work with existing tools and avoid propritary formats. Collaborative - DE, Analysts and DS can work together much more easily. **What do you dislike about Databricks?** Speed of innovations and features released. Sometimes features rolled-out without enough support and documentations. **What problems is Databricks solving and how is that benefiting you?** It is helping us in many ways: 1) Faster data processing with optimized features provided via DBR which is additional performace incentives on top of open spark e.g. Dynamic file pruning, low shuffle merge, deletion vectors, AQE etc 2) Unified governance and security - Rise of multi cloud adoption where each cloud has a unique governance model that requires individual familiarty intoduce complexity. solutions are unity catalog and data sharing. It is helping us a lot providing centralized governance **Official Response from Aunalisa Arellano:** > Thank you for sharing what you like about Databricks Data Intelligence Platform! We're glad to hear that the lakehouse platform's simplicity, openness, and collaborative features are benefiting your team. ### 16. Unlocking Insights - Databricks : Unified Lakehouse Platform for Modern Data Intelligence **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Cloud Data & AI Architect, Enterprise (> 1000 emp.) **Reviewed Date:** October 01, 2024 **What do you like best about Databricks?** Mainly 60-70% of day-day actviities goes with Databricks DLT , Autoloader, databricks Workflows usage for Building unified pipelines Also I use for creating the MlOps MlFlow Model serving , feature Store Online store tables for Model deployments, evaualtion & use the Lakeouse flow monitoring. Also used Mosiac AI gateway Soon looking forward of using the Databricks serverless in engineering notebook pipelines also apart from DLT DB-SQL with Delta Photon Optimization and the lIquid clustering solves the Optimize/Z-order manual work of optimizations Also recently migrated coupled oh Hive Metastore of various Line of busienss for Civil quants & Risk applications into Unity Catalog **What do you dislike about Databricks?** Currently not much, slowly everything getting addressed on improvements with Public releases starting from enahnced workflows , Genie, Databricks AI assitant & so Still need to more fine tune the AI assitants to understand the previous error history happened in that cluster & provide suggestions not only in code syntax but also provide alternate options to permanently solve & write teh code in optmized ways without asking in Prompts Vector search need more improvemnt with more precise reterievals to provide context for DBR models & more embedding models need to be served from AI gateway **What problems is Databricks solving and how is that benefiting you?** Used for Migrating Hive Metastore to Unity Catalog ensures better data governance and accessibility across various lines of business, enhancing data management and compliance. Integrated also Snowflake catalog to Unity catalog as external catalog as part of POC tetsing Lakehouse flow monitoring provides insights into data processes, allowing for timely detection and resolution of issue, used DLT for data quality expectation for providing insights For analysis DB-SQL leveraging Delta and Photon optimization, you minimize manual optimization efforts and improve query performance through automatic Z-ordering and clustering. For model building Utilizing MLFlow for model serving and feature store tables allows for smoother model deployment and evaluation, enhancing the overall machine learning lifecycle. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the benefits of Databricks Data Intelligence Platform and that many of your needs are being addressed. We take your suggestions for improvement seriously and will continue to work on enhancing our AI assistants and vector search capabilities. ### 17. They have the most unified and simplified data ecosystem for data governance, Analytics, AI/BI **Rating:** 5.0/5.0 stars **Reviewed by:** Janvier N. | Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** Unity Catalog and MLOps are unmatched products. Having your data, process, and models all in unity catalog is mind blowing. Oh, also, I have never loved ML model serving more **What do you dislike about Databricks?** The free edition isn’t that helpful since you have to use serveless. You can’t set up a custom environment/compute resources which limit the usefulness of databricks free edition **What problems is Databricks solving and how is that benefiting you?** Model serving **Official Response from Aunalisa Arellano:** > We're glad to hear that you find our Unity Catalog and MLOps products unmatched! We appreciate your feedback! ### 18. Databricks keeps making my job easier **Rating:** 5.0/5.0 stars **Reviewed by:** Joe S. | Business Intelligence Development Manager, Small-Business (50 or fewer emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** I like that there are constant updates that abstract from the low level code that I am tired of writing **What do you dislike about Databricks?** Some of these new features are fairly limited in their use **What problems is Databricks solving and how is that benefiting you?** Bringing data in from old oracle systems and helping me use more modern platforms. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks is helping you bring data in from old systems and transition to more modern platforms. We're committed to providing solutions that benefit our users. ### 19. Very likely to recommend data brick intelligence platform **Rating:** 4.0/5.0 stars **Reviewed by:** Bhagirath S. | Report & Dashboard Developer, Enterprise (> 1000 emp.) **Reviewed Date:** September 06, 2024 **What do you like best about Databricks?** i like easy to use interface with strong features supporting and helping in data transformation and implementing data pipeline quickly. i also like it has capability to be used by data analyst using query and result in dashboard , data engineer using notebooks, workflow, CDC, Timetravel, DLT and data scientist with ML model all in one intelligent interface. **What do you dislike about Databricks?** i do not like only 30 days trail period and missing update mail from databricks. **What problems is Databricks solving and how is that benefiting you?** Databrick is solving multiple data tools issues for multiple role, IT unified all strong features by provding data lakehouse with ETL, warehouse , ML feature support on top of external cloud host with GEN AI capability too. I am using databrick in my current engagement and it is helping me as simple and strong tool to ingest and transform data from bronze to gold layer using workflows. **Official Response from Janelle Glover:** > It's great to hear that Databricks Data Intelligence Platform is helping you solve multiple data tool issues and providing unified features for different roles. We're pleased to know that it's simplifying data ingestion and transformation for your current engagement. ### 20. Databricks Data Intelligence Platform: ETL, Scalability, and Job Scheduling **Rating:** 5.0/5.0 stars **Reviewed by:** Ashish G. | Assistant Business Intelligence Developer , Mid-Market (51-1000 emp.) **Reviewed Date:** January 07, 2025 **What do you like best about Databricks?** ETL Pipeline automates batch and real-time data integration and quality data integration. Parallel data processing using multithreading. Scale up and scale down for optimising the cost **What do you dislike about Databricks?** Some SQL functions are not supported like declare, stored procedure, transaction rollback **What problems is Databricks solving and how is that benefiting you?** Fast ETL process, support of genie, Handling growing datasets **Official Response from Aunalisa Arellano:** > We're glad to hear that you are enjoying the ETL automation, scalability, and job scheduling features of Databricks Data Intelligence Platform. ### 21. Great event! **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer & Network Security | Mid-Market (51-1000 emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** The way it helps my team work together and solve issues without wasting time on a lot of infrastructure work **What do you dislike about Databricks?** Missing some visibility and a better orchestration tool **What problems is Databricks solving and how is that benefiting you?** Helps us work with spark easily and govern our data lake **Official Response from Aunalisa Arellano:** > We're thrilled to hear that our platform is helping your team work together and solve issues efficiently. Your feedback about the need for better visibility and orchestration tools is duly noted, and we'll consider it for future updates. It's great to know that our platform is enabling you to work with Spark easily and govern your data lake effectively. Thank you for sharing your experience! ### 22. Feature Parity for Govcloud **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Aviation & Aerospace | Enterprise (> 1000 emp.) **Reviewed Date:** June 12, 2025 **What do you like best about Databricks?** The easy of use for big data, and the potential to use AI **What do you dislike about Databricks?** Gov cloud does not have feature Parity with other access levels. **What problems is Databricks solving and how is that benefiting you?** Solving making dashboards, queries and reports on big data **Official Response from Aunalisa Arellano:** > We appreciate your feedback on the feature parity for GovCloud. We are constantly working to improve and expand our offerings, and your input is valuable in this process. ### 23. Databricks for data world **Rating:** 4.5/5.0 stars **Reviewed by:** Tej P. | DevOps Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 10, 2024 **What do you like best about Databricks?** It provides some key features like workspace, notebook, Apache spark, Delta Lake, and Scalability. It provides a centralized environment where teams can collaborate. We can work on Jupyter Notebook with Apache Spark which provides the engine that powers all processing of dataset. It provides Cross-team collaboration so engineers, analysts, scientists, and ML engineers can work seamlessly on the same platform. It also provides consistency with notebooks, users can transition between tasks and programming languages without the need for context-switching. **What do you dislike about Databricks?** In this Databricks platform, we can't migrate a library from the dev workspace to the prod workspace. **What problems is Databricks solving and how is that benefiting you?** It provide some key features like workspace, notebook, Apache spark, Delta Lake and Scalability. It provide centralized evn where team can collaborate. We can work on jupyter notebook with Apache spark which provide engine that power all processing of dataset. It provide Cross-team collaboration so enengineers, analysts, scientists and ML engineers can work seamlessly in the same platform. Consistency: with notebooks, users can transition between tasks and programming languages without the need for context-switching. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks Data Intelligence Platform is solving problems for you by providing a centralized environment for team collaboration and enabling seamless cross-team collaboration. We appreciate your feedback on the features and the consistency with notebooks. We have taken note of your concern about the inability to migrate libraries between workspaces. ### 24. Great platform for scale and muli-users **Rating:** 4.5/5.0 stars **Reviewed by:** K K. | Digital Designer, Enterprise (> 1000 emp.) **Reviewed Date:** March 10, 2024 **What do you like best about Databricks?** The web interface allows for testing codes and scripts and making quick review tables before the implementation of pipelines. Otherwise the setup and navigation is quite initiative to manage. Finding some configuration takes a trip to the help docs but they are always clear and well linked to keep progress going. **What do you dislike about Databricks?** First-time cloud users might struggle unless they have been exposed to similar tools. My largest issue is with our company's admin and their anti-trust of staff. We have limited access to the full power of the platform, which means that, realistically, it is being used for much less than it is capable of. **What problems is Databricks solving and how is that benefiting you?** It creates a data warehouse for the global team to access the same tables with minimal impact on each other's work performance. It also allows the development and testing of queries to ensure they are optimised and return the expected results. Calculations and analysis flows can be developed to expand the available data. **Official Response from Aunalisa Arellano:** > We appreciate your feedback and are pleased to hear that our platform is benefiting your team by providing a centralized data warehouse and facilitating query development and testing. ### 25. Databricks Review. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** April 04, 2024 **What do you like best about Databricks?** The best thing is I can choose any cloud provider for my infrastructure. The interactive environment called Notebooks which allows to write and execute codes is very easy to use. The magic commands are very much helpful. The UI is good and interactive. **What do you dislike about Databricks?** Nothing as of now but there can be more features added. **What problems is Databricks solving and how is that benefiting you?** I can choose between Single node and Multi node clutser according to our requirement which solves our problem. I can create workflows easily by connecting it to the notebooks and can also mention external parameters. Spot instances are very much cost saving. And the feature called Photon which helps in fast query performance at low cost. **Official Response from Aunalisa Arellano:** > Thank you for your feedback! We are constantly working on adding new features to enhance the platform, so stay tuned for updates. ### 26. Data Engineering with Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Banking | Enterprise (> 1000 emp.) **Reviewed Date:** April 11, 2024 **What do you like best about Databricks?** I like Databricks because it provides all the elements necessary for mass data processing, the different components (notebook, cluster, jobs) are very well integrated and quite easy to use. A lot of training is offered to understand in depth the possibilities offered in Databricks, which makes it much easier to use. **What do you dislike about Databricks?** The Dashboard part integrated into Databricks is quite limited and does not allow the creation of complex and relevant dashboards **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of scalability, it's easy to manage a large amount of data as a smaller one. **Official Response from Aunalisa Arellano:** > Thank you for sharing your positive experience with Databricks and highlighting its ease of use for data processing. We understand your concern about the limited dashboard capabilities and will take this feedback into account for future enhancements. ### 27. Beast When It Comes To Data On Cloud **Rating:** 5.0/5.0 stars **Reviewed by:** Ayush S. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 21, 2023 **What do you like best about Databricks?** 1. ACID compliance on Data Lake which saves not only cost for storage but makes queries faster. 2. Customizable as per budget (by use of correct cluster sizing and other ways) 3. Init Scripts is really a boon if used correctly. **What do you dislike about Databricks?** 1. Clusters often take up a lot of time to start up. 2. Many bugs were encountered personally on the new Unity Catalogue feature. 3. Missing Information Schema on Hive_Metastore. **What problems is Databricks solving and how is that benefiting you?** The primary problem that Databricks Lakehouse Platform is solving is storing and processing big data. With its support of a wide variety of languages like Python, Scala, Sql etc it becomes mighty and helpful to process data. Role-based access management is a blessing for data governance. **Official Response from Aunalisa Arellano:** > Thank you for sharing how Databricks Data Intelligence Platform is helping you with storing and processing big data, as well as providing role-based access management for data governance. Thanks for taking the time to leave a review! ### 28. Unified Platform & Collaborative Workspace for Data & AI/ML team **Rating:** 4.5/5.0 stars **Reviewed by:** Sudarsan M. | Solution Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** January 30, 2023 **What do you like best about Databricks?** Databricks Serverless SQL with Photon Query acceleration for data analyst & business analyst In-built Visualization & dashboards, along with GeoSaptial & Advanced SQL functions Unified Pipeline for Structure streaming batch & real-time ingestion Auto-loader for standard formats of file ingestion & Schema Evolution in-built Delta Live Table for data Engineering Workloads & Pipelines Databricks Multi-task Orchestration job worklfows Unity Catalog Metstaore & its integration with other data catalogs MLFlow for building and tracking ML experiments & Feature Store for centralized feature supply for production/inference models Time Travel & Z-order Optimization **What do you dislike about Databricks?** Need to build a more comprehensive orchestration workflow JOBS panel for a diverse set of pattern design workflows Serverless Cluster for Data Engineering Streaming/Batch pipelines Integrate most IDE features into the notebook Clear documentation on Custom Databricks runtime docker image creation will be helpful Lineage & flow monitoring dashboard can be built automated for non-DLT jobs as well DLT implementation can be extended to other DELTA format supporting warehouse in future **What problems is Databricks solving and how is that benefiting you?** Unified Pipeline for Structure streaming batch & real-time ingestion The schema merge feature helps to track the change in Schema DLT feature helps to build Data Quality Lineage along with automated Pipeline links to the reference LIVE tables Auto-loader helps to build the common ingestion framework for our enterprise **Official Response from Aunalisa Arellano:** > Thank you for your detailed feedback on the Databricks Data Intelligence Platform. We're pleased to know that the platform is benefiting you with its unified pipeline, schema merge feature, DLT for data quality lineage, and auto-loader for ingestion framework. Thanks for taking the time to leave a review! ### 29. Experience as a New User **Rating:** 4.0/5.0 stars **Reviewed by:** Chloe Z. | Business Intelligence Analyst III, Enterprise (> 1000 emp.) **Reviewed Date:** June 27, 2023 **What do you like best about Databricks?** My favorite part abt databricks lakehouse platform would be to have an integrated UI and connecting SQL/DS and ML space. It also has clear instructions on connecting to 3rd party tools. **What do you dislike about Databricks?** The help documents sometimes are little hard to navigate and i would need to go thru google search instead. wish there could also be AI modules in the future to answer questions. **What problems is Databricks solving and how is that benefiting you?** it helps with data exploring thru SQL queries and visualization tool,and the result was very easy to share with business users. It also help build clear ETL pipelines to streamline the process. **Official Response from Aunalisa Arellano:** > Thank you for your positive feedback on the integrated UI and connectivity features of Databricks Data Intelligence Platform. We understand your concerns about the help documents and will work on improving their navigation. Thanks for taking the time to leave a review! ### 30. Excellent for all sorts of data analytics **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** December 22, 2022 **What do you like best about Databricks?** The versatility and scalability are the best features for us. We currently use SQL, R, Python, SQL, Spark and Scala with Databricks. It's impressive how seamless this experience is for different teams with different use cases and skill sets. The interoperability across these languages and accessing data is a blessing and enables us to use a vast array of tools to solve problems. **What do you dislike about Databricks?** More insight into individual job costs would be a helpful feature that is currently lacking. Deploying code is also not as intuitive and the Git integration could be more powerful with an enhanced feature set. **What problems is Databricks solving and how is that benefiting you?** Easy integration across data sources and using a mixed bag of tools to perform advanced analytics. The mixed bag of tools available also means that we treat this as a one-stop solution and are able to serve analytics outputs to various consumers and stakeholders conveniently. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has been beneficial for your team in integrating data sources and using a variety of tools for advanced analytics. Your input is valuable as we continue to enhance our platform. Thanks for taking the time to leave a review! ## 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, 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?filters%5Bsentiment_snippet%5D=1559004&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+12%3A06%3A35+-0500&secure%5Bsession_id%5D=4734a87b-e080-401c-85fe-d125eab30087&secure%5Btoken%5D=47c943540a0d6bb96803ed50fbf53472fa10f44b6e9fe8d2750c94adccc3c85b&format=llm_user) ## Databricks Integrations - 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[Azure Logic Apps](https://www.g2.com/products/azure-logic-apps/reviews) - [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Azure Portal](https://www.g2.com/products/azure-portal/reviews) - [Azure SQL Database](https://www.g2.com/products/azure-sql-database/reviews) - [Base SAS](https://www.g2.com/products/base-sas/reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - [Claude Code](https://www.g2.com/products/anthropic-claude-code/reviews) - [Crunchbase](https://www.g2.com/products/crunchbase/reviews) - [Dash](https://www.g2.com/products/dash-for-brands-ltd-dash/reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - [Dataiku](https://www.g2.com/products/dataiku/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [DigitalOcean](https://www.g2.com/products/digitalocean/reviews) - [Domo](https://www.g2.com/products/domo/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - 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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) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (713 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (356 reviews)