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Book Overview & Buying
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Table Of Contents
Data Engineering on AWS - The Complete Training
By :
Data Engineering on AWS - The Complete Training
By:
Overview of this book
This course begins by laying the foundation of data analytics and introducing AWS data engineering services. You’ll start with AWS Glue, learning to catalog, transform, and manage data using workflows, job bookmarks, and quality checks, followed by visual data preparation with Glue Databrew. Next, you’ll move into data warehousing with Amazon Redshift, from cluster creation to serverless deployment and performance tuning.
As the journey continues, the focus shifts to real-time data processing with Amazon Kinesis and MSK, covering stream management, Flink applications, and Kafka integration. You’ll then explore big data processing using Amazon EMR, understanding MapReduce, Spark, and cost-effective serverless execution. The course then guides you through building data lakes using AWS Lake Formation and querying them efficiently with Amazon Athena.
In the final stages, you’ll visualize data using Amazon QuickSight and orchestrate pipelines through Step Functions and AppFlow. You’ll also gain experience with AWS data migration tools like DMS and DataSync. The course concludes with extended AWS services including Lambda, S3, EC2, and DynamoDB, empowering you to design and manage complete, scalable data platforms in the cloud.
Table of Contents (14 chapters)
Introduction: Data Is the New Oil
Know Your Trainer
Getting Started with Data Analytics
AWS Glue: Catalog and Process Your Data
Amazon Redshift: A Data Warehouse in AWS
Processing Streaming Data on Amazon Kinesis and Amazon MSK
Running Big Data Workloads on Amazon EMR
Building Data Lakes on AWS
Query Your Data Using Amazon Athena
Visualize Your Data Using Amazon QuickSight
Orchestrating Your Data Pipeline
Data Migration Services in AWS
Going Beyond AWS Analytics Services
Final Note
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