Databricks Lakehouse Platform for all your analytics requirements
May 15, 2022

Databricks Lakehouse Platform for all your analytics requirements

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Databricks Lakehouse Platform (Unified Analytics Platform)

We used Databricks Lakehouse platform for running all our Machine Learning workloads as well as storing large amounts of data in our data lake backend. The data stored in the databricks lakehouse was used to train state-of-the-art ML and Deep Learning models on text and image datasets. Databricks' Spark jobs as well as Delta Lake Lakehouse backend is well equipped for these kinds of tasks.

Pros

  • Very well optimized Spark Jobs Execution Engine.
  • Time travel in Databricks Lakehouse Platform allows you to version your datasets.
  • Newly integrated Analytics feature allows you to build visualization dashboards.
  • Native integration with managed MLflow service.

Cons

  • Running MLflow jobs remotely is extremely cluttered and needs to be simplified.
  • All the runnable code has to stay in Notebooks which are not very production-friendly.
  • File management on DBFS can be improved.
  • Time travel for Dataset versioning.
  • Seamless, managed Spark Engine.
  • Deployment on AWS and Azure.
  • Integration with Managed MLflow.
  • Dataset version management became much easier.
  • Spark jobs execution became much faster compared to self managed clusters.
  • Cluster management simplified.
  • Our big data cluster became much secure due to integration with SAML.
  • Production code management became a bit complicated because only notebooks are allowed to be executed.
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

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

Yes

Are you happy with Databricks Data Intelligence Platform's feature set?

Yes

Did Databricks Data Intelligence Platform live up to sales and marketing promises?

Yes

Did implementation of Databricks Data Intelligence Platform go as expected?

Yes

Would you buy Databricks Data Intelligence Platform again?

Yes

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

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