Book : Serverless Analytics With Amazon Athena Query...

-Titulo Original : Serverless Analytics With Amazon Athena Query Structured, Unstructured, Or Semi-structured Data In Seconds Without Setting Up Any Infrastructure

-Fabricante :

Packt Publishing

-Descripcion Original:

Get more from your data with Amazon Athenas ease-of-use, interactive performance, and pay-per-query pricing Key Features Explore the promising capabilities of Amazon Athena and Athenas Query Federation SDK Use Athena to prepare data for common machine learning activities Cover best practices for setting up connectivity between your application and Athena and security considerations Book Description Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure. This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. Youll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, youll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, youll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, youll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server. By the end of this book, youll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of todays ML modeling exercises. What you will learn Secure and manage the cost of querying your data Use Athena ML and User Defined Functions (UDFs) to add advanced features to your reports Write your own Athena Connector to integrate with a custom data source Discover your datasets on S3 using AWS Glue Crawlers Integrate Amazon Athena into your applications Setup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data Catalog Add an Amazon SageMaker Notebook to your Athena queries Get to grips with using Athena for ETL pipelines Who this book is for Business intelligence (BI) analysts, application developers, and system administrators who are looking to generate insights from an ever-growing sea of data while controlling costs and limiting operational burden, will find this book helpful. Basic SQL knowledge is expected to make the most out of this book. Table of Contents Your First Query Introduction to Amazon Athena Key Features, Query Types, and Functions Metastores, Data Sources, and Data Lakes Securing Your Data AWS Glue and AWS Lake Formation Ad Hoc Analytics Querying Unstructured and Semi-Structured Data Serverless ETL Pipelines Building Applications with Amazon Athena Operational Excellence - Maintenance, Optimization, and Troubleshooting Athena Query Federation Athena UDFs and ML Lake Formation - Advanced Topics About the Author Anthony Virtuoso works as a Principal Engineer at Amazon and holds multiple patents in distributed systems, software defined networks, and security. In his eight years at Amazon, he has helped launch several Amazon Web Services, the most recent of which was Amazon Managed Blockchain. As one of the original authors of Athena Query Federation, youll often find him lurking on the Athena Federation GitHub repository answering questions and shipping bug fixes. When not at work, Anthony obsesses over a different set of customers, namely his wife and two little boys, aged 2 and 5. His kids enjoy doing science experiments with dad, like 3D printing toys, building with Lego, or searching the local pond for tardigrades. Mert Turkay Hocanin is a Principal Big Data Architect at Amazon Web Services within the AWS Glue and AWS Lake