--- title: Azure Databricks Reviews meta_title: 'Azure Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 238 reviews by the users' company size, role or industry to find out how Azure Databricks works for a business like yours. aggregate_rating: rating_value: 4.5 review_count: 238 scale: '5' date_modified: '2026-07-07' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Azure Databricks Reviews **Vendor:** Microsoft **Category:** [Big Data Analytics Software](https://www.g2.com/categories/big-data-analytics) **Average Rating:** 4.5/5.0 **Total Reviews:** 238 ## About Azure Databricks Azure Databricks is a unified, open analytics platform developed collaboratively by Microsoft and Databricks. Built on the lakehouse architecture, it seamlessly integrates data engineering, data science, and machine learning within the Azure ecosystem. This platform simplifies the development and deployment of data-driven applications by providing a collaborative workspace that supports multiple programming languages, including SQL, Python, R, and Scala. By leveraging Azure Databricks, organizations can efficiently process large-scale data, perform advanced analytics, and build AI solutions, all while benefiting from the scalability and security of Azure. Key Features and Functionality: - Lakehouse Architecture: Combines the best elements of data lakes and data warehouses, enabling unified data storage and analytics. - Collaborative Notebooks: Interactive workspaces that support multiple languages, facilitating teamwork among data engineers, data scientists, and analysts. - Optimized Apache Spark Engine: Enhances performance for big data processing tasks, ensuring faster and more reliable analytics. - Delta Lake Integration: Provides ACID transactions and scalable metadata handling, improving data reliability and consistency. - Seamless Azure Integration: Offers native connectivity to Azure services like Power BI, Azure Data Lake Storage, and Azure Synapse Analytics, streamlining data workflows. - Advanced Machine Learning Support: Includes pre-configured environments for machine learning and AI development, with support for popular frameworks and libraries. Primary Value and Solutions Provided: Azure Databricks addresses the challenges of managing and analyzing vast amounts of data by offering a scalable and collaborative platform that unifies data engineering, data science, and machine learning. It simplifies complex data workflows, accelerates time-to-insight, and enables the development of AI-driven solutions. By integrating seamlessly with Azure services, it ensures secure and efficient data processing, helping organizations make data-driven decisions and innovate rapidly. ## Azure Databricks Pros & Cons **What users like:** - Users find Azure Databricks very **easy to use and implement** , benefiting from its seamless integration and user-friendly interface. (9 reviews) - Users value the **robust feature set** of Azure Databricks, enhancing workflows with efficient data integration and analytics. (8 reviews) - Users value the **seamless integrations** of Azure Databricks, enhancing workload management and streamlining data processes. (6 reviews) - Users admire the **speed** of Azure Databricks, noting its impressive performance for large-scale data processing and analytics. (5 reviews) - Users value the **seamless analytics integration** of Azure Databricks, enhancing efficiency across various data workloads. (4 reviews) - Scaling (4 reviews) - Users appreciate the **intuitive user interface** of Azure Databricks, which enhances collaboration for both technical and non-technical users. (3 reviews) - Powerful (3 reviews) - Productivity Improvement (3 reviews) - Serverless Architecture (3 reviews) **What users dislike:** - Users find Azure Databricks' **complexity in setup and management** challenging, especially for newcomers and in optimizing costs. (3 reviews) - Users find the **difficult setup** of Azure Databricks challenging, especially when configuring environments and connecting tools. (3 reviews) - Users face a **steep learning curve** with Azure Databricks, as it can be complicated for newcomers to navigate. (3 reviews) - Users experience **slow performance** with Azure Databricks, particularly during cluster startup and parallel processing. (3 reviews) - Users experience **unclear pricing** with Azure Databricks, making cost management challenging and potentially expensive without proper optimization. (3 reviews) - Users face challenges with **workflow issues** , particularly in monitoring and managing multiple pipeline executions effectively. (3 reviews) - Users find the **complex usability** of Azure Databricks challenging, especially for beginners navigating its extensive features. (2 reviews) - Cost Management (2 reviews) - Users find Azure Databricks **very costly** , especially when clusters are poorly managed or left running unnecessarily. (2 reviews) - Poor Customer Support (2 reviews) ## Azure Databricks Reviews ### 1. Azure Databricks efficient for large data, a bit rough on edges **Rating:** 4.5/5.0 stars **Reviewed by:** Wealth A. | Business Intelligence Analyst/ Designer, Financial Services, Enterprise (> 1000 emp.) **Reviewed Date:** April 24, 2026 **What do you like best about Azure Databricks?** What I like most about Azure Databricks is how it makes working with data feel straightforward without me having to overthink the setup. From my experience, I mostly use it for querying, transforming, and validating data, and it handles large datasets really well without slowing me down. I don’t have to worry too much about performance — I just write what I need, and it runs. I also like the flexibility of switching between SQL and PySpark depending on what I’m doing. It makes it easier to explore data and troubleshoot issues quickly without being stuck in one approach. The notebook environment is another big plus for me. I use it to organize my queries and logic in one place, so I can always go back, adjust things, or reuse parts without starting from scratch. Overall, it just makes my workflow cleaner and more efficient, especially when I’m working with large volumes of data and need quick, reliable results. **What do you dislike about Azure Databricks?** What I dislike about Azure Databricks, based on how I’ve used it, is mostly tied to day-to-day usability. When I’m working with files (especially around /dbfs), I sometimes run into seemingly random errors that aren’t very clear. It takes extra time to figure out what actually went wrong, which is frustrating when I’m just trying to get quick results. Debugging is another area that can slow me down. If a query or transformation doesn’t behave as expected, it isn’t always obvious where the issue is, so I end up spending more time tracing and narrowing things down than I’d like. The notebook environment is useful, but as a single notebook grows, it can get messy and harder to manage. If I’m not careful, it’s easy to lose structure and organization. Cost is also something I’ve had to keep an eye on. Even when I’m only testing or running queries, usage can add up quickly if resources aren’t managed properly. Overall, it works well, but there are still moments where it feels less intuitive than it should—especially when something goes wrong. **What problems is Azure Databricks solving and how is that benefiting you?** Azure Databricks mainly helps me work with large, scattered datasets in a way that’s actually manageable. In my experience, before using it, handling data across different sources or tools could get messy—especially when I needed to query, clean, and validate everything in a consistent way. With Databricks, I can do all of that in one place, which makes my process much simpler. It also takes away a lot of the stress around performance. I don’t have to worry as much about how my queries will scale as datasets grow—I can focus on writing what I need, and it handles the rest. That’s been especially helpful when I’m exploring or validating large volumes of data. Speed is another big plus. I can run queries quickly, test transformations, and iterate without long waits, which keeps my workflow moving and makes me more efficient. Overall, it makes my data work more straightforward and less fragmented. I spend less time jumping between tools or dealing with performance issues, and more time actually understanding and working with the data. ### 2. A powerhouse for scaling ML workflows, but keep a close eye on your billing. **Rating:** 5.0/5.0 stars **Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** June 18, 2026 **What do you like best about Azure Databricks?** I'm a Senior Data Scientist at a mid-sized company where I work a lot with Azure Databricks to connect data in our production machine learning models to the raw data lakes. My team uses it every day to clean huge amounts of user behaviour data, build complex features and develop predictive models, our Customer Churn risk and Product recommendation models.I love how black and white it's (almost) the headache of big data infrastructure is eliminated. Prior to adopting Databricks, creating and maintaining a Spark cluster had been a tedious project, which necessitated significant data engineering support. Now I simply choose my compute size from a dropdown and head straight to writing code in PySpark, Python or SQL in a collaborative notebook. My team's dream has also come true with the native integration of MLflow. Previously, recorded model version, parameters and metrics was stored in handwritten spreadsheet files and pickle files. This is now the case for every experiment, and automatic logging puts comparisons of model runs or reverting back to an earlier version a breeze. It's also cool that it integrates with our Azure Data Lake storage without having to go through security hurdles to mount data onto it. **What do you dislike about Azure Databricks?** The worst thing is that the bill can add up quickly if you're not managing your compute usage. Auto-scaling is a godsend for performance but we do have some serious billing shock-and-tumbles when clusters were scaling up feverishly with some badly written query left running, that could have been avoided. Another one of the frustrations is the StartUp Time for Clusters. The minutes required to spin up a cluster just to run an ad-hoc data check, and then wait for the results can really bring you out of the flow. Lastly, the Spark UI can be a bit confusing and unwelcoming for newer, less technical members of the team who are simply looking to run some simple SQL queries, instead of getting into spark configuration or compute policies. **What problems is Azure Databricks solving and how is that benefiting you?** But the way Databricks has revolutionized managing large-scale data processing and model deployment is truly groundbreaking. One example we can take is our batch scoring program for recommending customers, which happens every week. The original implementation ran on a single large virtual machine and was not only very time consuming (nearing 8 hrs to run), but was also prone to crashing in the middle because of memory restrictions. We moved that pipeline to an Azure Databricks notebook, and then distributed the processing power using PySpark without any crashes, and the time to run it dropped under 45 minutes. It's also helped a lot with our collaboration wall in the works. Our data engineers and data scientists can collaborate in the same data environment on the exact same dataframes without having to wait between each other; the time to have a predictive model in a local prototype running in the real business environment is significantly reduced. ### 3. Comprehensive Data Management and Streamlined Setup **Rating:** 5.0/5.0 stars **Reviewed by:** Tej P. | DevOps Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 20, 2026 **What do you like best about Azure Databricks?** I use Azure Databricks to build and manage data pipelines. It provides all required services in a single place, like data engineering, SQL, and ML features. It helps me simply process large-scale data for enterprise projects, making Azure Databricks a valuable tool for me. The SQL features make it easy to query and analyze data quickly, and the ML capabilities support experimenting with models on the same platform. The initial setup is very easy; you just need to create a resource on the Azure portal by entering the resource group and Databricks workspace name with the rest of the default settings. **What do you dislike about Azure Databricks?** Cost optimization: it can be more optimized by providing the single cost monitoring dashboard by default for the workspace admins, as they have this budget feature in the preview for the account console only. **What problems is Azure Databricks solving and how is that benefiting you?** I use Azure Databricks to build and manage data pipelines, simplifying the processing of large-scale enterprise data. It lets me create scalable ETL pipelines, quickly query data with SQL, and experiment with ML models using Mosaic AI on the same platform. ### 4. All-in-One Data Platform with an Intuitive, User-Friendly Interface **Rating:** 5.0/5.0 stars **Reviewed by:** Mayuri K. | Product Management Fellow, Mid-Market (51-1000 emp.) **Reviewed Date:** May 01, 2026 **What do you like best about Azure Databricks?** It makes data easy and simple to understand even if we are not from technical background, i dont need to swap or switch different app or software now for data eng , analytics or data science all can be done in once now. The interface is very good and user friendly easy to understand tabs given , i have tried uploding a large set of data , uploading experience was very smooth and easy **What do you dislike about Azure Databricks?** Mostly, I got confused during the cluster setup. It was very difficult for me, and even with the settings I’m still struggling with it. **What problems is Azure Databricks solving and how is that benefiting you?** For me its helping mostly in getting faster insights, in tracking the perfomnce of the task assigned and outcomes on it ### 5. Azure Databricks: Scalable, Fast Collaboration with Seamless Azure Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Elisa L. | Consultant Data&AI, Mid-Market (51-1000 emp.) **Reviewed Date:** April 30, 2026 **What do you like best about Azure Databricks?** What I like best about Azure Databricks is how well it combines scalability, speed, and collaboration in a single environment. It makes it easy to work with large datasets, build and run data pipelines efficiently, and support both engineering and analytics tasks without switching between too many tools. I also appreciate how smoothly it integrates with the broader Azure ecosystem, which makes it especially useful for end-to-end data processing and analytics workflows **What do you dislike about Azure Databricks?** One thing I dislike about Azure Databricks is that it can feel complex and not always immediately intuitive, especially at the beginning. The environment is powerful, but that also means there are many concepts, configurations, and moving parts to get used to before it feels really smooth. Another drawback is that, for some tasks, the setup and navigation can feel heavier than expected, which slows down simple workflows. In short, it is a very capable platform, but the learning curve and operational complexity can make it less straightforward than I would like. **What problems is Azure Databricks solving and how is that benefiting you?** Azure Databricks addresses the hassle of juggling separate tools for engineering, analytics, and AI by bringing everything into a single platform. That consolidation reduces friction and helps speed up delivery. For me, it means I can work more efficiently with large datasets, build pipelines, and collaborate in the same environment without constantly switching contexts. It also helps that the platform is built for scalable processing and integrated workflows, so the path from exploration to production feels much smoother and more consistent. ### 6. Azure Databricks: Unified, Scalable Data Platform That Boosts Productivity **Rating:** 5.0/5.0 stars **Reviewed by:** Suraj A. | Data Enigneer, Mid-Market (51-1000 emp.) **Reviewed Date:** February 04, 2026 **What do you like best about Azure Databricks?** What I like best about Azure Databricks is how it simplifies large-scale data processing while still giving flexibility to engineers. From my experience, the biggest advantage is the unified platform I can do data engineering, transformations, performance tuning, and even analytics in one place without jumping across multiple tools. The integration with Spark is seamless, and things like auto-scaling clusters, job scheduling, and notebook collaboration make day-to-day work much more efficient. I also appreciate features like Delta Lake handling ACID transactions, schema evolution, and time travel directly on data lakes makes production pipelines much more reliable. On top of that, optimizations like Adaptive Query Execution, auto-optimize, Z-ordering, and caching really help when working with large datasets. Another thing I like is how well it integrates with the Azure ecosystem whether it’s ADLS, ADF, Key Vault, or Unity Catalog for governance. It reduces a lot of setup overhead and makes deployments smoother across environments. Overall, it lets me focus more on solving data problems and performance tuning rather than worrying about infrastructure management. **What do you dislike about Azure Databricks?** One thing I dislike about Azure Databricks is that cost management can get tricky if clusters and jobs aren’t monitored closely. Because it’s so easy to spin up clusters and run large workloads, costs can increase quickly especially with auto-scaling or multiple parallel jobs running. So it requires good governance and monitoring in place. Another area is debugging and troubleshooting. While notebooks are great for development, debugging production job failures especially intermittent Spark or infrastructure issues can sometimes take time. Logs are available, but tracing the exact root cause across cluster events, Spark UI, and job runs isn’t always straightforward. I’ve also noticed that handling CI/CD and deployments (like moving notebooks, workflows, configs across environments) isn’t as smooth out of the box compared to traditional code repos. It’s improving with Databricks Asset Bundles and Repos, but still needs careful setup. That said, most of these are manageable with best practices cost controls, monitoring, and proper DevOps processes. **What problems is Azure Databricks solving and how is that benefiting you?** Azure Databricks is mainly solving the problem of processing and managing large-scale data efficiently in a unified environment. Before platforms like Databricks, handling big data required setting up separate tools for storage, compute, scheduling, and processing. It involved a lot of infrastructure management and integration effort. Databricks brings all of this together scalable Spark compute, collaborative notebooks, job orchestration, and optimized storage layers in one place. From a data engineering perspective, it solves challenges like processing huge volumes of data, handling complex transformations, and building reliable pipelines. Features like Delta Lake help address data consistency and reliability issues for example, ACID transactions, schema enforcement, and time travel make production data pipelines safer and easier to manage. It also solves performance problems. Optimizations like Adaptive Query Execution, caching, auto-scaling clusters, and partition pruning help process data faster without heavy manual tuning. How it benefits me personally: For me, it reduces the time spent on infrastructure setup and lets me focus more on data logic and optimization. I can quickly develop pipelines, test transformations in notebooks, and deploy jobs to production with better monitoring. It also improves productivity collaboration through shared notebooks, integration with Azure services like ADLS and ADF, and centralized governance through Unity Catalog make day-to-day work smoother. Overall, it helps me build scalable, reliable, and high-performing data solutions faster than traditional big data setups. ### 7. Lakebase Delivers Flexible Postgres Power for AI, Now with Autoscaling **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer & Network Security | Small-Business (50 or fewer emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Azure Databricks?** Lakebase and API gateways. We use Lakebase as our primary database, and it has very strong capabilities for AI workloads. It’s also easy and flexible to work with because it’s a Postgres database. I think the addition of autoscaling databases is a really good improvement; instead of having static Compute Units assigned to each database, they can now scale automatically. I also like that with autoscaling you can set both the minimum and maximum CU, which gives you more control while still keeping things flexible. **What do you dislike about Azure Databricks?** Pricing is still not very clear, things are still measured in Compute units which is really hard to get down for pricing **What problems is Azure Databricks solving and how is that benefiting you?** We needed a platform that could cover our ML and data capabilities end to end. We use Lakebase as our primary data system, and it’s been easy to port work into notebook capabilities since Databricks is very strong with Spark. Their AI gateway also helps ensure we can run AI workloads on our data, which was important for us as well. ### 8. A Powerful and Reliable Platform for Scalable Data Engineering **Rating:** 5.0/5.0 stars **Reviewed by:** NOOR A. | Data Engineer, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** October 18, 2025 **What do you like best about Azure Databricks?** What I like best about Azure Databricks is how seamlessly it integrates with the Azure ecosystem — especially with services like Data Lake, Synapse, and Data Factory. It provides an excellent balance between ease of use and advanced capabilities, allowing both technical and non-technical users to collaborate in a single environment. The notebooks are intuitive and support multiple languages such as SQL, Python, and R, which makes implementation and experimentation smooth. I use it frequently for building and managing data pipelines, running transformations, and developing machine learning models. The platform’s scalability, auto-scaling clusters, and managed Delta Lake features make handling large datasets efficient. Customer support is generally helpful and the platform continues to evolve with frequent updates that add even more useful features. **What do you dislike about Azure Databricks?** Although Azure Databricks is powerful, a few areas could be improved. The initial setup and environment configuration can be slightly complex for new users, and cluster startup times can sometimes be slow. The pricing structure also requires careful monitoring — costs can increase quickly if clusters aren’t optimized or auto-terminated properly. While the interface is robust, it could be more beginner-friendly, and notebook version control could be smoother. Customer support response time can vary depending on the issue severity. Still, once you get accustomed to the environment, it’s a highly capable and dependable platform for daily data workloads and analytics. **What problems is Azure Databricks solving and how is that benefiting you?** Azure Databricks has addressed several major data challenges within our organization. Previously, handling large datasets, integrating various data sources, and executing complex transformations were both time-consuming and prone to errors. With Databricks, I am able to develop scalable ETL pipelines and automate data workflows more efficiently, which has greatly reduced manual work and shortened processing times. The platform also offers a collaborative environment where data engineers and analysts can work together smoothly, enhancing productivity and minimizing miscommunication. Its integration with Azure services such as Data Lake, Data Factory, and Synapse ensures seamless data movement throughout our ecosystem. This has enabled us to deliver reliable, high-quality datasets more quickly for reporting, analytics, and machine learning projects, ultimately supporting better business decisions and greater operational efficiency. ### 9. Powerful platform for Data Engineering. **Rating:** 4.0/5.0 stars **Reviewed by:** Praveen K. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 01, 2026 **What do you like best about Azure Databricks?** Easy big data processing and scalability. **What do you dislike about Azure Databricks?** "Costs can increase with heavy usage." De **What problems is Azure Databricks solving and how is that benefiting you?** Azure Databricks simplifies big data processing, ETL, and analytics, helping me build scalable data pipelines faster and improve productivity. ### 10. Effortless Data Processing and Seamless Azure Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Akshat G. | Programmer Analyst, Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** December 03, 2025 **What do you like best about Azure Databricks?** The platform manages large-scale data processing with impressive smoothness, and its interface becomes quite user-friendly after a short learning curve. Integrating it with other Azure services is straightforward, which significantly speeds up the implementation process. I appreciate the variety of features available for ETL and analytics, allowing us to use it regularly for a range of different workloads. When problems arise, the documentation and support resources are generally sufficient to help resolve issues quickly. **What do you dislike about Azure Databricks?** Sometimes, the platform can seem a little complicated for newcomers, and it may take some time for clusters to start up. Managing costs is not always straightforward, and certain features require additional configuration. While support is generally helpful, response times can occasionally be slow. **What problems is Azure Databricks solving and how is that benefiting you?** This tool enables us to process large datasets efficiently and construct dependable ETL pipelines. By consolidating data cleaning, transformation, and analytics into a single collaborative platform, it streamlines our workflow. The integration with Azure storage and other services is a significant time-saver, and the increased processing speed has a direct positive impact on our reporting and decision-making. ## Azure Databricks Discussions - [When data is small how can I reconfigure cluster to automatically adjust . I don't know which day data coming will be small.](https://www.g2.com/discussions/azure-databricks-when-data-is-small-how-can-i-reconfigure-cluster-to-automatically-adjust-i-don-t-know-which-day-dat) - 1 comment, 1 upvote - [What is the best way to databricks in ADF](https://www.g2.com/discussions/26515-what-is-the-best-way-to-databricks-in-adf) - 1 comment, 1 upvote - [What is Azure Databricks used for?](https://www.g2.com/discussions/azure-databricks-what-is-azure-databricks-used-for) - 2 comments - [Is Azure Databricks PaaS or SAAS?](https://www.g2.com/discussions/is-azure-databricks-paas-or-saas) - 2 comments - [Does Microsoft own Databricks?](https://www.g2.com/discussions/does-microsoft-own-databricks) - 2 comments - [View Azure Databricks pricing details and edition comparison](https://www.g2.com/products/azure-databricks/reviews/azure-databricks-review-4192380?section=pricing&secure%5Bexpires_at%5D=2026-07-08+17%3A52%3A26+-0500&secure%5Bsession_id%5D=5fd6a840-bd9f-493c-9776-2fe33961d527&secure%5Btoken%5D=c466871cf9a90ea199bc1451025a9215dd56e30fc239b93c0b975ac3c687efcd&format=llm_user) ## Azure Databricks Integrations - [Azure Cosmos DB](https://www.g2.com/products/azure-cosmos-db/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Azure Data Lake Store](https://www.g2.com/products/azure-data-lake-store/reviews) - [Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) ## Azure Databricks Features **Data Transformation** - Real-Time Analytics - Data Querying **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **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 Azure Databricks Alternatives - [Alteryx](https://www.g2.com/products/alteryx/reviews) - 4.6/5.0 (834 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (708 reviews) - [Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews) - 4.3/5.0 (414 reviews)