In the previous lesson, I touched on Hadoop and Spark and their roles in handling big data. Running Spark jobs involves significant administrative tasks. Users must manage and configure the Apache Spark cluster, which includes configuration, virtual machine creation, network security, storage, cluster management, and more.

This is where Databricks comes in. Founded by the creators of Apache Spark, Databricks offers a web-based platform for working with Spark. It provides automated cluster management and Jupyter-style notebooks.

If you follow this URL, you can see the benefits and a comparison between Apache Spark and Databricks. As you can see, there are numerous advantages to using Databricks. For example, under the “Runtime” section, you can see benefits like running multiple versions of Spark and auto-scaling compute resources. This means you can automatically add or remove virtual machine instances based on load changes.

Databricks also offers an integrated workspace, enabling collaboration with others and boosting productivity.

I will provide this link in the next lecture so you can review it in your own time. Databricks is a very popular solution, and for good reason.