Databricks — One unified platform for data and AI

There are a lot of tools available in the current era to support the data scientist to delivery meaningful insights to the business. So, business management can take a hard decision to grow their business. Another thing is that data which is growing day by day in a tremendous manner and many tools are unable to process this data or take a huge time for the data processing and this is the big problem for the data scientists.

Databricks is developed by the same team who created the Spark. So, Databricks is a sort of a spark managed service. It connects to cloud to do business stuffs. In databricks, everything connects to a single spark driver and we can work simultaneously in the same Notebook and allows setting up of high-performance clusters which perform computing using its in-memory architecture.

When do we need DataBricks?

DataBricks is mainly for big data analytics. Databricks is good when we have lots of data and want to do nontrivial transformations on a distributed architecture. Databricks also also makes it easy to leverage machine learning libraries and the whole R or Python ecosystem.

Databricks seems an ideal choice when the notebook interactive experience is a must, when data engineers and data scientists must work together to get insights from data and adapt smoothly to…