The best thing for my use case is integration.

My client has hired internal admins for a lot of policy settings and for security reasons my team cannot be given access to the more sensitive parts.

So they have integrated it with tools like control-m and postman to run jobs and monitor pipelines. Review collected by and hosted on G2.com.

The UX needs improvement.

For example, sometimes a pipeline in progress already displayed in front is not present in search results.

And while monitoring or error-checking a pipeline, the table for the pipeline details extends beyond the screen and we have to use sloppy horizontal scrolling to view items.

For a running pipeline, it is near impossible to see both the running tasks and the ADF pipeline diagram at the same time when using a relatively smaller screen (15.6' and smaller) because of how they are placed overlappingly. Review collected by and hosted on G2.com.

Analytics

Data validation

Bigdata insights Review collected by and hosted on G2.com.

I haven't disliked anything but seems like we can better UI performance. Review collected by and hosted on G2.com.

Scalable and efficient data processing: Azure Databricks allows for efficient and scalable data processing, making it ideal for organizations with large amounts of data.

Support for multiple languages: The platform supports multiple programming languages, including Python, Scala, SQL, and R, making it a flexible solution for different types of data analytics.Advanced machine learning capabilities: Azure Databricks provides advanced machine learning capabilities, including built-in algorithms, libraries, and APIs, allowing organizations to easily build and deploy machine learning models. Review collected by and hosted on G2.com.

Steep learning curve: While the platform offers an intuitive interface, some users have reported that the advanced features and functions of Azure Databricks can have a steep learning curve.Limited customization options: Some users have reported that there are limited customization options for the platform, which can make it difficult to fully integrate with existing systems and workflows.Limited support for legacy systems: Some users have reported that the platform may not support older or legacy systems, making it difficult to integrate with existing systems and workflows. Review collected by and hosted on G2.com.

They are virtually identical. It’s the same product on different clouds. On Azure, Databricks is a first-party service that you engage with Microsoft to consume. On AWS you consume it from Databricks directly. Review collected by and hosted on G2.com.

Two things that could be improved upon : 1. Database querying: The database querying can be faster. The load time for some operations even with smaller scale data can be more faster. This is despite us optimizing our queries. 2. Nascent platform : DataBricks Lakehouse as a platform is still nascent and there are lots of opportunities. For example - once the platform has all cloud platforms in one domain, it could be easier, if we could also query from all these databases with a single SQL language. There are also more growth opportunities like bringing in data visualization, business intelligence and this could help the platform grow and become even more useful. Review collected by and hosted on G2.com.

The interface is the best thing about it. Different cells can be run separately unlike any local ide. Very comfortable. Review collected by and hosted on G2.com.

Not much. Every thing is designed properly. Review collected by and hosted on G2.com.

To process data from different sources to single platform for making informative notes Review collected by and hosted on G2.com.

Requires basic training of databricks for using it Review collected by and hosted on G2.com.

Compatible for sql and its robust speed. Review collected by and hosted on G2.com.

Nothing to dislike the product is extremely good. Review collected by and hosted on G2.com.

Cloud-based integration tool having AI capabilities Review collected by and hosted on G2.com.

I don't see any major issues or concerns with the Azure Databricks Review collected by and hosted on G2.com.

It is user friendly and the UI is so simple. Also the flexibility to move from one language to another in consecutive cells comes very handy. Notebooks to SQL Warehouse, all is good. Review collected by and hosted on G2.com.

One major issue which I feel while using Databricks is the cluster restart while dealing with Big Data. The driver stops unexpectedly and this can be an area of improvement. Review collected by and hosted on G2.com.

Jupyter Notebook with multiple language support within the same notebook

An integrated environment with DBFS and tables and clusters.

External integrations/connections are smooth enough, like connecting to AWS bucket or Azure Lake.

Markdown within the notebooks. Review collected by and hosted on G2.com.

It can allow customizable color themes.

Copy-pasting cells/outputs from one notebook to another.

Hints for best practices can be provided.

Error reporting can be much better. Review collected by and hosted on G2.com.