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

Imagining the future – a look at emerging trends

Technology seems to progress at an increasing velocity. For decades, relational databases from vendors such as Oracle were the primary technology for managing all data. Today, there is a wide range of different database types that can be used, depending on the use case (such as graph databases for highly connected datasets, or NoSQL databases for low-latency reading and writing for very large tables).

It was also not all that long ago that Hadoop MapReduce was the state-of-the-art technology for processing very large datasets, but today, most new projects would choose Apache Spark over a MapReduce implementation. And even Apache Spark itself has progressed from its initial release, with Spark 3.0 being released in June 2020. We have also seen the introduction of Spark Streaming, Spark ML, and Spark GraphX for different use cases.

No one can tell for certain what the next big thing will be, but in this section, we will...