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Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS ebook#
Page: 482
Format: pdf / epub / kindle
ISBN: 9781800560413
Publisher: Packt Publishing
Start your AWS data engineering journey with this easy-to-follow, hands-on guide and get to grips with foundational concepts through to building data engineering pipelines using AWS Key Features: Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics Book Description: Knowing how to architect and implement complex data pipelines is a highly sought-after skill. Data engineers are responsible for building these pipelines that ingest, transform, and join raw datasets - creating new value from the data in the process. Amazon Web Services (AWS) offers a range of tools to simplify a data engineer's job, making it the preferred platform for performing data engineering tasks. This book will take you through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. The book also teaches you about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently. What You Will Learn: Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for: This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone who is new to data engineering and wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but is not needed. Familiarity with the AWS console and core services is also useful but not necessary.Customer reviews: Data Engineering with AWS - Amazon.com and review ratings for Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS at Amazon.com. Apache Spark on Amazon EMR - Big Data Platform Learn how you can create and manage Apache Spark clusters on AWS. Use Apache Spark on Amazon EMR for Stream Processing, Machine Learning, Interactive SQL DevOps - Amazon Web Services (AWS) AWS helps you use automation so you can build faster and more efficiently. Using AWS services, you can automate manual tasks or processes such as deployments, Data Engineering with AWS: Learn how to design and build Buy Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS online at an affordable price.Item Weight: 1.81 pounds (0.81 kg)Publisher: Packt Publishing (December 29, 20Dimensions: 7.5 x 1.09 x 9.25 inches (19.1 x 2.8 1 review Amazon SageMaker Data Wrangler - Amazon Web Services Complete data aggregation and data preparation for machine learning in minutes. build fully automated ML workflows with Amazon SageMaker Pipelines and The best-selling new & future releases in Databases & Big Data Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale Data Engineering with AWS: Learn how to design and build cloud-based data Architecture Best Practices for Analytics & Big Data - Amazon Learn architecture best practices for cloud data analysis, data using Amazon Web Services (AWS) services to ingest SaaS data into a data lake on AWS. Data Engineering with AWS: Learn how to design and - Alibris Buy Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS by Gareth Eagar (ISBN: 9781800560413) Learn Amazon SageMaker: A guide to building, training, and Build, train, and deploy machine learning models quickly using Amazon Data Engineering with AWS: Learn how to design and build cloud-based data Gareth Eagar: Books, Biography, Blog, Audiobooks, Kindle Data Engineering with AWS: Learn how to design and build cloud-based data transformation pipelines using AWS Dec 29, 2021. by Gareth Eagar. What is a data lake? - Amazon AWS Learn more about how to build and deploy data lakes in the cloud. to big data processing, real-time analytics, and machine learning to guide better AWS Big Data Blog Amazon Redshift is a fast, fully managed, widely popular cloud data warehouse that powers the modern data architecture that empowers you with fast and deep AWS Data Lab Customers - Amazon Web Services This has resulted in a major shift in how engineers at Jungle Scout build data processing pipelines.” Alex Handley, Principal Architect, Data Engineering with AWS - Booktopia Booktopia has Data Engineering with AWS, Learn how to design and build cloud-based data transformation pipelines using AWS by Gareth Eagar. Data Engineering with AWS, Published by Packt - GitHub This is the code repository for Data Engineering with AWS, published by Packt. Learn how to design and build cloud-based data transformation pipelines using