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Data Engineering with AWS

Data Engineering with AWS

By : Eagar
4.7 (24)
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Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
By: Eagar

Overview of this book

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken 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. You’ll also learn 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.
Table of Contents (19 chapters)
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1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

Tips and tricks to optimize Amazon Athena queries

When raw data is ingested into the data lake, we can immediately create a table for that data in the AWS Glue data catalog (either using a Glue crawler or by running DDL statements with Athena to define the table). Once the table has been created, we can start exploring the table by using Amazon Athena to run SQL queries against the data.

However, raw data is often ingested in plaintext formats such as CSV or JSON. And while we can query the data in this format for ad hoc data exploration, if we need to run complex queries against large datasets, these raw formats are not efficient to query. There are also ways that we can optimize the SQL queries that we write to make the best use of the underlying Athena query engine.

Amazon Athena's cost is based on the amount of compressed data that is scanned to resolve the query, so anything that can be done to reduce the amount of data scanned improves query performance and reduces...

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