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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

Understanding the impact of data democratization

At a high level, business drivers have not changed significantly over the past few decades. Organizations are still interested in understanding market trends, customer behavior, increasing customer retention, improving product quality, and improving speed to market. However, the analytics landscape, the teams and individual roles that deliver business insights, and the tools that are used to deliver business value have evolved.

Data democratization – the enhanced accessibility of data for a growing audience of users, in a timely and cost-efficient manner – has become a standard expectation for most businesses. Today's varied data consumers expect to be able to get access to the right data promptly using their tool of choice to consume the data.

In fact, as datasets increase in volume and velocity, their gravity will attract more applications and consumers. This is based on the concept of data gravity, a term...

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