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Amazon Redshift Cookbook

Amazon Redshift Cookbook

By : Shruti Worlikar, Arumugam, Patel
4.8 (9)
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Amazon Redshift Cookbook

Amazon Redshift Cookbook

4.8 (9)
By: Shruti Worlikar, Arumugam, Patel

Overview of this book

Amazon Redshift is a fully managed, petabyte-scale AWS cloud data warehousing service. It enables you to build new data warehouse workloads on AWS and migrate on-premises traditional data warehousing platforms to Redshift. This book on Amazon Redshift starts by focusing on Redshift architecture, showing you how to perform database administration tasks on Redshift. You'll then learn how to optimize your data warehouse to quickly execute complex analytic queries against very large datasets. Because of the massive amount of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of Redshift's columnar architecture and managed services. As you advance, you’ll discover how to deploy fully automated and highly scalable extract, transform, and load (ETL) processes, which help minimize the operational efforts that you have to invest in managing regular ETL pipelines and ensure the timely and accurate refreshing of your data warehouse. Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform. By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems.
Table of Contents (13 chapters)
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Streaming data to Amazon Redshift via Amazon Kinesis Firehose

Streaming datasets are continuous datasets that can originate from sources such as internet of things (IoT) devices, log files, gaming systems, and so on. Ingesting streamed data into Amazon Redshift allows the running of near-real-time analytics that can be combined with the historical/operational data to produce actionable reporting—for example, in a manufacturing shop, analyzing the data from several IoT sensors can help predict the failure of machinery and enable you to take preventive action.

In this recipe, we will simulate a streaming dataset using the www.amazon.com product review data to be ingested into Amazon Redshift using Amazon Kinesis Firehose. Amazon Kinesis Firehose provides out-of-the-box integration to capture the streaming dataset and land it into an Amazon Redshift table.

Getting ready

To complete this recipe, you will need the following:

  • An Amazon Redshift cluster deployed in...

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