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

Exploring AWS services for AI

While Amazon SageMaker simplifies building custom ML models, there are many use cases where a custom model is not required, and a generalized ML model will meet requirements.

For example, if you need to translate from one language into another, that will most likely not require a customized ML model. Existing, generalized models, trained for the languages you are translating between, would work.

You could use SageMaker to develop a French to English translation model, train the model, and then host the model on a SageMaker inference endpoint. But that would take time and would have compute costs associated with each phase of development (data preparation, notebooks, training, and inference).

Instead, it would be massively simpler, quicker, and cheaper to use an AI service such as Amazon Translate, which already has a model trained for this task. This service provides a simple API that can be used to pass in text in one language and receive a...