[ FreeCourseWeb.com ] Running Spark on Azure Databricks
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Video: .MP4, AVC, 1920x1080, 30 fps | Audio: English, AAC, 44.1 KHz, 2 Ch | Duration: 42m | 569 MB
Instructor: Guy Hummel
Apache Spark is an open-source framework for doing big data processing. It was developed as a replacement for Apache Hadoop’s MapReduce framework. Both Spark and MapReduce process data on compute clusters, but one of Spark’s big advantages is that it does in-memory processing, which can be orders of magnitude faster than the disk-based processing that MapReduce uses. Not only does Spark handle data analytics tasks, but it also handles machine learning.
In 2013, the creators of Spark started a company called Databricks. The name of their product is also Databricks. It’s a cloud-based implementation of Spark with a user-friendly interface for running code on clusters interactively.
Microsoft has partnered with Databricks to bring their product to the Azure platform. The result is a service called Azure Databricks. One of the biggest advantages of using the Azure version of Databricks is that it’s integrated with other Azure services. For example, you can train a machine learning model on a Databricks cluster and then deploy it using Azure Machine Learning Services.
In this course, we will start by showing you how to set up a Databricks workspace and a cluster. Next, we’ll go through the basics of how to use a notebook to run interactive queries on a dataset. Then you’ll see how to run a Spark job on a schedule. After that, we’ll show you how to train a machine learning model. Finally, we’ll go through several ways to deploy a trained model as a prediction service.
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