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

Building data lakes to tame the variety and volume of big data

Along with the rise of new data types and increasing data volumes, we have seen an increase in the ways that organizations look to draw insights from data. Machine learning in particular has become a popular tool for analytics, enabling organizations to automatically extract metadata from unstructured data sources, which can then be analyzed with traditional analytic tools:

  • Creating automated transcripts of call center audio recordings
  • Using natural language processing (NLP) algorithms to extract sentiment data from text
  • Identifying objects, people, and expressions in an image

As we saw in the previous section, enterprise data warehouses have been the go-to repositories for storing highly structured tabular data sourced from traditional run-the-business transactional applications. But the lack of a well-defined tabular structure makes unstructured and semi-structured data unsuitable for storing in...