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  • Book Overview & Buying Amazon Redshift Cookbook
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Amazon Redshift Cookbook

Amazon Redshift Cookbook - Second Edition

By : Shruti Worlikar, Harshida Patel, Anusha Challa
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

By: Shruti Worlikar, Harshida Patel, Anusha Challa

Overview of this book

Amazon Redshift Cookbook offers comprehensive guidance for leveraging AWS's fully managed cloud data warehousing service. Whether you're building new data warehouse workloads or migrating traditional on-premises platforms to the cloud, this essential resource delivers proven implementation strategies. Written by AWS specialists, these easy-to-follow recipes will equip you with the knowledge to successfully implement Amazon Redshift-based data analytics solutions using established best practices. The book focuses on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. The book covers recipes to help you take full advantage of Redshift's columnar architecture and managed services. You'll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, helping you minimize the operational effort that you invest in managing regular ETL pipelines and ensuring the timely and accurate refreshing of your data warehouse. By the end of the Redshift book, you'll be able to implement a Redshift-based data analytics solution by adopting best-practice approaches for solving commonly faced problems. *Email sign-up and proof of purchase required
Table of Contents (17 chapters)
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14
Other Books You May Enjoy
15
Index

Managing Amazon Redshift ML

Amazon Redshift ML enables Amazon Redshift users to create, deploy, and execute ML models using familiar SQL commands. Amazon Redshift has built-in integration with Amazon SageMaker Autopilot, which chooses the best ML algorithm based on your data using automatic algorithm selection. It enables users to run ML algorithms without the need for expert knowledge in ML. On the other hand, ML experts such as data scientists have the flexibility to select algorithms such as XGBoost and specify the hyperparameters and preprocessors. Once the ML model is deployed in Amazon Redshift, you can run the prediction using SQL at scale. This integration completely simplifies the pipeline required to create, train, and deploy the model for prediction.

Amazon Redshift ML allows you to create, deploy, and predict using the data in the data warehouse as follows:

Figure 12.4 – Amazon Redshift ML capabilities

Figure 12.4 – Amazon Redshift ML capabilities

Getting ready

To complete this recipe...

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Amazon Redshift Cookbook
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