Always trustworthy
January 30, 2026

Always trustworthy

Anonymous | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Databricks

Azure Databricks is primarily used by our insight and analytics team. They use this for machine learning and reporting. We use Azure Databricks as our data lake into Braze. This helps with all the data we need which is very important for reporting and metrics on our customer base. This reduces data silos for us.

Pros

  • Stops data silos
  • Collaborative
  • Single workspace

Cons

  • Quite expensive
  • Simple tasks can be difficult
  • Hard to learn
  • Hard to find people who know how to use it.
  • Works well with Braze.
  • Cost management not fully developed.
Great for what we use day to day and does what we need it to do. Cost management is not fully developed across the UX and gets expensive very quickly for developing projects. Integrated very well with our Microsoft stack and can be worked on collaboratively which works well for us.
I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.

Do you think Azure Databricks delivers good value for the price?

Yes

Are you happy with Azure Databricks's feature set?

Yes

Did Azure Databricks live up to sales and marketing promises?

Yes

Did implementation of Azure Databricks go as expected?

Yes

Would you buy Azure Databricks again?

Yes

Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.

Azure Databricks Feature Ratings

6.3
Connect to Multiple Data Sources
7
Extend Existing Data Sources
6
Automatic Data Format Detection
5
MDM Integration
7
8.0
Visualization
8
Interactive Data Analysis
8
8.0
Interactive Data Cleaning and Enrichment
7
Data Transformations
8
Data Encryption
10
Built-in Processors
7
6.8
Multiple Model Development Languages and Tools
3
Automated Machine Learning
8
Single platform for multiple model development
8
Self-Service Model Delivery
8
7.5
Flexible Model Publishing Options
7
Security, Governance, and Cost Controls
8

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