Databricks Lakehouse Platform for all your analytics requirements
Rating: 8 out of 10
IncentivizedUse Cases and Deployment Scope
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.
Likelihood to Recommend
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.
Vetted Review
2 years of experience