Azure Databricks Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Pricing is still not very clear, things are still measured in Compute units which is really hard to get down for pricing Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I use Azure Databricks for data processing, ETL, and analytics on large datasets. I like its scalability and easy collaboration in one unified platform. I appreciate its fast performance, seamless integration with other Azure services, and user-friendly notebooks. The initial setup was very easy, especially with the guidelines provided on the website. Review collected by and hosted on G2.com.

Cost management, fast cluster startup times, and a more intuitive UI for beginners. Review collected by and hosted on G2.com.

Azure Databricks is a great platform that offers a robust environment for querying large datasets through Apache spark. I especially appreciate how effortless it integrate with Azure storage account and notebooks making big data analytics efficient and scalable. Databricks built in version control combined with smooth GitHub integration makes collaboration and code management easy. It's one of the best setup I have worked with. Review collected by and hosted on G2.com.

The setup and cluster configuration is sometimes confusing and time consuming. Review collected by and hosted on G2.com.

Azure Databricks gave us a unified platform to run our Spark workloads on top of Azure Data Lake Storage Gen2. We could migrate our on-prem Hadoop data and pipelines into the cloud with minimal re-engineering. Its managed clusters, autoscaling, notebooks, and tight integration with Azure services (ADLS, Key Vault, ADF) saved a lot of infrastructure and maintenance effort. PySpark notebooks made development and debugging much easier compared to our previous setup. Review collected by and hosted on G2.com.

Cluster start-up times can still be slow for quick tests. Pricing is consumption-based and can become expensive if clusters are left running or poorly sized. Some enterprise features (e.g., fine-grained security, monitoring) require extra configuration. And compared to on-prem Hadoop, there’s a learning curve for workspace permissions and DevOps automation. Review collected by and hosted on G2.com.

Really like the Jupyter notebook system & Unity Catalog lineage Review collected by and hosted on G2.com.

Genie - Ai Assistant can have better accuracy Review collected by and hosted on G2.com.

The best about azure databricks is very easy to integrate to any cloud, rdbms or any other software services. It is very easy to use and to implement. The frequency of use is so high in my project. There are many number of features in azure databricks. Also it is easy to integrate with gitlab, hive, rdbms etc for any ETC processes. Review collected by and hosted on G2.com.

There is nothing that i dislike about Azure databricks as of 4 yr experience. Review collected by and hosted on G2.com.