Azure Databricks Reviews & Product Details

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.

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.

In Azure Databricks, you can have multiple layers of Security in those tables as well as there use interface is far more better that Teradata Review collected by and hosted on G2.com.

In Azure Databricks Likely the Cost is the of the factory which poeple though as it can be one of factor Review collected by and hosted on G2.com.

Huge data celan transfer. And this is mainly use from banking Review collected by and hosted on G2.com.

This is the complex setup . Do not optimisation Review collected by and hosted on G2.com.

Its like a one stop shop for everything. Review collected by and hosted on G2.com.

Sometimes the use of python with sql gets complicated. Review collected by and hosted on G2.com.

They are improving the existing features and adding new features for easy integration. Review collected by and hosted on G2.com.

Some features are not very intuitive and user friendly live crearting secret scope. Review collected by and hosted on G2.com.