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Databricks Lakehouse Platform: A 2-year user review
March 09, 2023

Databricks Lakehouse Platform: A 2-year user review

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User

Overall Satisfaction with Databricks Lakehouse Platform (Unified Analytics Platform)

I use Databricks Lakehouse Platform to build a data-science based solutions that adress many problems in my business. This includes: increment our data in the lake house and use Databricks Lakehouse Platform computational capabilities to analyze and feature engineer our data, build different machine learning model and track different experiment and finally register our trained model that can be used by the business.
  • MLFLOW Experiment
  • MLFLOW Registry
  • Databricks Lakehouse Platform Notebook
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
  • Databricks Lakehouse Platform Notebook
  • MLFLOW Experiment
  • MLFLOW Registry
  • Process huge amount of data
  • Building ML models that deals with big data

Do you think Databricks Lakehouse Platform delivers good value for the price?

Yes

Are you happy with Databricks Lakehouse Platform's feature set?

Yes

Did Databricks Lakehouse Platform live up to sales and marketing promises?

Yes

Did implementation of Databricks Lakehouse Platform go as expected?

Yes

Would you buy Databricks Lakehouse Platform again?

Yes

Well Suited: Dealing with big data and being able to train different models that address many problems in my business. In addition to its computational capabilities, using Databricks Lakehouse Platform allowed us to do all development in one platform. Less Appropriate: Having a small dataset that doesn't need parallel processing. Local development is easier to develop and track so if no parallelization is needed (data is not big or parallelized computations is not required), I prefer local development.