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Amazon redshift cookbook : recipes for building modern data warehousing solutions /
Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions Key Features Discover how to translate familiar data warehousing concepts into Redshift implementation Use impressive Redshift features to optimize developmen...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Amazon redshift cookbook : |b recipes for building modern data warehousing solutions / |c Shruti Worlikar, Thiyagarajan Arumugam, Harshida Patel. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, Limited, |c 2021. | |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Amazon Redshift -- Technical requirements -- Creating an Amazon Redshift cluster using the AWS Console -- Getting ready -- How to do it ... -- Creating an Amazon Redshift cluster using the AWS CLI -- Getting ready -- How to do it ... -- How it works ... -- Creating an Amazon Redshift cluster using an AWS CloudFormation template -- Getting ready -- How to do it ... -- How it works ... -- Connecting to an Amazon Redshift cluster using the Query Editor -- Getting ready | |
| 505 | 8 | |a How to do it ... -- Connecting to an Amazon Redshift cluster using the SQL Workbench/J client -- Getting ready -- How to do it ... -- Connecting to an Amazon Redshift Cluster using a Jupyter Notebook -- Getting ready -- How to do it ... -- Connecting to an Amazon Redshift cluster using Python -- Getting ready -- How to do it ... -- Connecting to an Amazon Redshift cluster programmatically using Java -- Getting ready -- How to do it ... -- Connecting to an Amazon Redshift cluster programmatically using .NET -- Getting ready -- How to do it ... -- Connecting to an Amazon Redshift cluster using the command line | |
| 505 | 8 | |a Getting ready -- How to do it ... -- Chapter 2: Data Management -- Technical requirements -- Managing a database in an Amazon Redshift cluster -- Getting ready -- How to do it ... -- Managing a schema in a database -- Getting ready -- How to do it ... -- Managing tables -- Getting ready -- How to do it ... -- How it works ... -- Managing views -- Getting ready -- How to do it ... -- Managing materialized views -- Getting ready -- How to do it ... -- How it works ... -- Managing stored procedures -- Getting ready -- How to do it ... -- How it works ... -- Managing UDFs -- Getting ready -- How to do it ... -- How it works ... | |
| 505 | 8 | |a Chapter 3: Loading and Unloading Data -- Technical requirements -- Loading data from Amazon S3 using COPY -- Getting ready -- How to do it ... -- How it works ... -- Loading data from Amazon EMR -- Getting ready -- How to do it ... -- Loading data from Amazon DynamoDB -- Getting ready -- How to do it ... -- How it works ... -- Loading data from remote hosts -- Getting ready -- How to do it ... -- Updating and inserting data -- Getting ready -- How to do it ... -- Unloading data to Amazon S3 -- Getting ready -- How to do it ... -- Chapter 4: Data Pipelines -- Technical requirements | |
| 505 | 8 | |a Ingesting data from transactional sources using AWS DMS -- Getting ready -- How to do it ... -- How it works ... -- Streaming data to Amazon Redshift via Amazon Kinesis Firehose -- Getting ready -- How to do it ... -- How it works ... -- Cataloging and ingesting data using AWS Glue -- How to do it ... -- How it works ... -- Chapter 5: Scalable Data Orchestration for Automation -- Technical requirements -- Scheduling queries using the Amazon Redshift query editor -- Getting ready -- How to do it ... -- How it works ... -- Event-driven applications using Amazon EventBridge and the Amazon Redshift Data API | |
| 520 | |a Discover how to build a cloud-based data warehouse at petabyte-scale that is burstable and built to scale for end-to-end analytical solutions Key Features Discover how to translate familiar data warehousing concepts into Redshift implementation Use impressive Redshift features to optimize development, productionizing, and operations processes Find out how to use advanced features such as concurrency scaling, Redshift Spectrum, and federated queries Book DescriptionAmazon 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. What you will learn Use Amazon Redshift to build petabyte-scale data warehouses that are agile at scale Integrate your data warehousing solution with a data lake using purpose-built features and services on AWS Build end-to-end analytical solutions from data sourcing to consumption with the help of useful recipes Leverage Redshift's comprehensive security capabilities to meet the most demanding business requirements Focus on architectural insights and rationale when using analytical recipes Discover best practices for working with big data to operate a fully managed solution Who this book is for 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 will be beneficial. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Data warehousing. | |
| 650 | 0 | |a Cloud computing. | |
| 650 | 6 | |a Entrepôts de données (Informatique) | |
| 650 | 6 | |a Infonuagique. | |
| 650 | 7 | |a Cloud computing |2 fast | |
| 650 | 7 | |a Data warehousing |2 fast | |
| 700 | 1 | |a Arumugam, Thiyagarajan, |e author. | |
| 700 | 1 | |a Patel, Harshida, |e author. | |
| 776 | 0 | 8 | |i Print version: |a WORLIKAR, SHRUTI. ARUMUGAM, THIYAGARAJAN. PATEL, HARSHIDA. |t AMAZON REDSHIFT COOKBOOK. |d [Place of publication not identified] : PACKT PUBLISHING LIMITED, 2021 |z 1800569688 |w (OCoLC)1249072610 |
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