دانلود کتاب آشپزی آمازون ردشیفت(Amazon Redshift)، ویرایش دوم، دستور پخت‌هایی برای ساخت راه‌حل‌های مدرن انبار داده

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 the 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. 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. Finally, you’ll learn emerging trends in utilizing Redshift data warehouses to enable AI/ML use cases. By the end of this Redshift book, you’ll be able to implement a Redshift-based data analytics solution and will have understood the best-practice solutions to commonly faced problems. This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. Basic knowledge of data warehousing, database systems, and cloud concepts and familiarity with Redshift would be beneficial.
What this book covers:
Chapter 1, Getting Started with Amazon Redshift, discusses how Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. An Amazon Redshift data warehouse comes in two deployment options: provisioned clusters (with one leader node and multiple compute nodes) and serverless (with automatic provisioning and scaling). Amazon Redshift integrates with lakehouse architecture for unified access to structured and semi-structured data. This chapter provides hands-on guidance for creating and connecting to Amazon Redshift resources through various methods. Chapter 2, Data Management, discusses how a data warehouse system has very different design goals compared to a typical transaction-oriented relational database system for online transaction processing (OLTP). Amazon Redshift is optimized for the very fast execution of complex analytic queries against very large datasets. Because of the massive amounts of data involved in data warehousing, designing your database for analytical processing lets you take full advantage of the columnar architecture and managed service. This chapter delves into the different data structure options to set up an analytical schema for the easy querying of your end users. Chapter 3, Loading and Unloading Data, looks at how Amazon Redshift has in-built integrations with data lakes and other analytical services and how it is easy to move and analyze data across different services. This chapter discusses scalable options to move large datasets from a data lake based out of Amazon S3 storage, as well as AWS analytical services such as Amazon DynamoDB, ingesting from transactional sources using AWS DMS, cataloging with AWS Glue, and streaming via Amazon Kinesis Data Firehose. Chapter 4, Zero-ETL Ingestions, introduces AWS zero-ETL as a revolutionary suite of fully managed integrations that streamline data analytics processes. This chapter explores how zero-ETL eliminates traditional ETL complexities by automatically replicating data from operational sources to analytical destinations, enabling real-time insights without the need for complex data pipeline management. It covers various zero-ETL integration methods, including native database integrations, ingestion from SaaS applications, streaming data ingestion, and near-real-time ingestion from Amazon S3 using auto-copy. These solutions significantly reduce time to insight, ensure data consistency, and allow organizations to scale their data operations efficiently while maintaining separation between transactional and analytical workloads, ultimately enabling faster, data-driven decision-making with reduced operational overhead and technical complexity. Chapter 5, Scalable Data Orchestration for Automation, explores AWS’s comprehensive suite of native services for workflow integration and automation. The chapter focuses on ETL process workflows for data warehouse refreshes, demonstrating how different tasks can be managed independently using purpose-built services. It covers various orchestration methods, including query scheduling, event-driven applications, workflow orchestration, and pipeline management. The chapter emphasizes how these tools enable the efficient management of complex data pipelines originating from various sources, supporting downstream applications such as machine learning pipelines, analytics dashboards, and business reports. Chapter 6, Platform Authorization and Security, explores Amazon Redshift’s comprehensive security features designed to meet the requirements of security-sensitive organizations within the AWS Shared Responsibility Model. The chapter covers essential security aspects, including infrastructure security, data encryption, authentication, and metadata security. The chapter emphasizes how these built-in features provide a robust security framework for protecting data while maintaining fine-grained access controls for underlying data structures. Chapter 7, Data Authorization and Security, focuses on Amazon Redshift’s granular data access control mechanisms for protecting sensitive information. The chapter explores key security features focusing on fine-grained access control. These features work together to create a comprehensive security framework that ensures users can only access and modify data according to their authorization level, providing precise control over data visibility and manipulation rights. Chapter 8, Performance Optimization, examines how Amazon Redshift, being a fully managed service, provides great performance out of the box for most workloads. Amazon Redshift also provides you with levers that help you maximize the throughputs when data access patterns are already established. Performance tuning on Amazon Redshift helps you manage critical SLAs for workloads and easily scale up your data warehouse to meet/exceed business needs. Chapter 9, Cost Optimization, discusses how Amazon Redshift is one of the best price-performant data warehouse platforms on the cloud. Amazon Redshift also provides you with scalability and different options to optimize the pricing, such as elastic resizing, pause and resume, Reserved Instances, and using cost controls. These options allow you to create the best price-performant data warehouse solution.