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

Summary

In this chapter, you learned more about the broad range of AWS ML and AI services and had the opportunity to get hands-on with Amazon Comprehend, an AI service for extracting insights from written text.

We discussed how ML and AI services can apply to a broad range of use cases, both specialized (such as detecting cancer early) and general (business forecasting or personalization).

We examined different AWS services related to ML and AI. We looked at how different Amazon SageMaker capabilities can be used to prepare data for ML, build models, train and fine-tune models, and deploy and manage models. SageMaker makes building custom ML models much more accessible to developers without existing expertise in ML.

We then looked at a range of AWS AI services that provide prebuilt and trained models for common use cases. We looked at services for transcribing text from audio files (Amazon Transcribe), for extracting text from forms and handwritten documents (Amazon Textract...