Book Image

Data Engineering with AWS

By : Gareth Eagar
Book Image

Data Engineering with AWS

By: Gareth 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)
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

Meeting the needs of data analysts with structured reporting

While business users make use of data to make decisions related to their job in an organization, a data analysts' full-time job is all about the data – analyzing datasets and drawing out insights for the business.

If you look at various job descriptions for data analysts, you may see a fair amount of variety, but some elements will be common across most descriptions. These include the following:

  • Cleansing data and ensuring data quality when working with ad hoc data sources.
  • Developing a good understanding of their specific part of the business (sometimes referred to as becoming a domain specialist for their part of the organization). This involves understanding what data matters to their part of the organization, which metrics are important, and so on.
  • Interpreting data to draw out insights for the organization (this may include identifying trends, highlighting areas of concern, and performing...