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Databricks Data Science and Engineering: Basic Tools and Infrastructure

by Kishan Iyer

This course will teach you how to make the best use of Databricks assets such as notebooks, clusters, and repos to simplify the development and management of your big data applications.

What you'll learn

Building robust and high-performing big data applications requires a well-configured environment. On the Databricks platform, this means setting up various assets such as clusters, notebooks, and repos in order to get the most out of the platform and make development and analysis work as smooth as possible.

In this course, Databricks Data Science and Engineering: Basic Tools and Infrastructure, you'll explore exactly how this can be accomplished.

First, you'll begin by creating and then making use of clusters, tables, files, and notebooks, and explore how all of these can be combined in order to build and run a simple application.

Next, you'll move on to the use of Databricks repos, which allow us to record changes to notebooks and related files in a workspace, and can be linked with an external Git repository. Then, you'll delve into how this linking can be performed, and explore how file additions, modifications, and removals can be performed and viewed on repos.

Finally, you'll move on to jobs which represent the execution of a task on Databricks - how job executions can be configured and scheduled, and how notifications at various stages of a job can be sent.

When you're finished with this course, you'll have skills and knowledge of Databricks resources such as clusters, notebooks, repos, and jobs, as well as their configurations, which will help you create a Databricks environment that is optimized for building and running applications and can help you get the most out of your data.

About the author

I have a Masters in Computer Science from Columbia University and have worked previously as a developer and DevOps engineer. I now work at Loonycorn which is a studio for high-quality video content. My interests lie in the broad categories of Big Data, ML and Cloud.

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