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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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Building a data lake catalog using AWS Lake Formation

The data lake design pattern has been widely adopted in the industry. Data lakes help to break data silos by allowing you to store all of your data in a single, unified place. You can collect the data from different sources and data can arrive at different frequencies—for example, clickstream data. The data format can be structured, unstructured, or semi-structured. Analyzing a unified view of the data allows you to derive more value and helps to derive more insight from the data to drive business value.

Your data lake should be secure and should meet your compliance requirements, with a centralized catalog that allows you to search and easily find data that is stored in the lake. One of the advantages of data lakes is that you can run a variety of analytical tools against them. You may also want to do new types of analysis on your data. For example, you may want to move from answering questions on what happened in the...

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