Set up a petabyte-scale, cloud-based data warehouse that is burstable and built to scale for end-to-end analytical solutions
Key Features
- Learn how to translate familiar data warehousing concepts into Redshift implementation
- Use impressive Redshift features to optimize development, productionizing, and operation processes
- Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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 this Redshift book, you'll be able to implement a Redshift-based data analytics solution by adopting best-practice approaches for solving commonly faced problems.
What you will learn
- Integrate data warehouses with data lakes using AWS features
- Create end-to-end analytical solutions from sourcing to consumption
- Utilize Redshift's security for strict business requirements
- Apply architectural insights with analytical recipes
- Discover big data best practices for managed solutions
- Enable data sharing for data mesh and hub-and-spoke architectures
- Explore Redshift ML and generative AI with Amazon Q
Who this book is for
This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, including data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, as well as cloud concepts and familiarity with Redshift is beneficial.
Table of Contents
- Getting Started with Amazon Redshift
- Data Management
- Loading and Unloading Data
- Zero-ETL Ingestions
- Scalable Data Orchestration for Automation
- Platform Authorization and Security
- Data Authorization and Security
- Performance Optimization
- Cost Optimization
- Lakehouse Architecture
- Data Sharing with Amazon Redshift
- Generative AI and ML with Amazon Redshift
Shruti Worlikar is a cloud professional with technical expertise in data and analytics across cloud platforms. Her background has led her to become an expert in on-premises-to-cloud migrations and building cloud-based scalable analytics applications. Shruti earned her bachelor's degree in electronics and telecommunications from Mumbai University in 2009 and later earned her master's degree in telecommunications and network management from Syracuse University in 2011. Her work history includes work at JPMorgan Chase, MicroStrategy, and Amazon Web Services (AWS). She is currently working in the role of Sr. Manager, Analytics Specialist SA, at AWS, helping customers to solve real-world analytics business challenges with cloud solutions and working with service teams to deliver real value. Shruti is the DC Chapter Director for the non-profit Women in Big Data (WiBD) and engages with chapter members to build technical and business skills to support their career advancements. Originally from Mumbai, India, Shruti currently resides in Northern Virginia, with her husband and two kids.
Harshida Patel is a principal analytics specialist solution architect at AWS, enabling customers to build scalable data lake and data warehousing applications using AWS analytical services. She has presented Amazon Redshift deep-dive sessions at re:Invent. Harshida has a bachelor's degree in electronics engineering and a master's in electrical and telecommunication engineering. She has over 15 years of experience in architecting and building end-to-end data pipelines in the data management space. In the past, Harshida has worked in the insurance and telecommunication industries. She enjoys traveling and spending quality time with friends and family, and she lives in Virginia with her husband and son.
Anusha Challa is a seasoned professional with over 15 years of expertise in architecting data and analytics solutions across on-premises and cloud environments. She has provided guidance to hundreds of Amazon Redshift customers, empowering them to design scalable and robust end-to-end data warehouse architectures. Anusha speaks at various AWS events, such as re:Invent and AWS Summits, where she shares best practices for using Amazon Redshift and other AWS analytics services. She has a bachelor's degree in computer science and a master's degree with a specialization in Machine Learning. Based in Chicago, Anusha enjoys reading books and traveling during her free time.
