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In a previous post, I talked about Azure Databricks and what it is. In review, Azure Databricks is a managed platform for running Apache Spark jobs. As it’s managed, that means you don’t have to worry about managing the cluster or running performance maintenance to use Spark, like you would if you were going to deploy a full HDInsight Spark cluster.
Databricks provides a simple to operate user interface for data scientist and analysts when building models, as well as a powerful API that allows for some automation. You also can run role-based access control with Active Directory for better user integration at a more granular scale. You don’t have to tear down an HDInsight cluster to use Spark jobs as you can pause (or start) your resources on demand and scale up/out as needed.
In this post, I’ll run through some key Databricks terms to give you an overview of the different points you’ll use when running Databricks jobs:
Keep an eye out for future Azure Every Day posts, where we’ll discuss more about inter-operability, as well as how Databricks is being used in the industry. If you’d like to learn more about Azure Databricks and how you can use it in your organization or if you’re interested in any Azure product or service, click the link below or contact us—we love Azure and we’re here to help.
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