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Introduction to Data Engineering on AWS: Data Sourcing and Storage

Introduction to Data Engineering on AWS: Data Sourcing and Storage

1h 51mIntermediate2022-10-07

Authors

Dipali Kulshrestha

Dipali Kulshrestha

AWS-Certified Software Programmer and Cloud Architect

Course details

Businesses need data experts—now more than ever before. As data-driven decision-making has risen to boardroom prominence, the role of the data expert has become essential to understanding and scaling a business. In this course—the first in a two-part series—instructor Dipali Kulshrestha shows you how to get started on your professional journey and grow your career as a data engineer with AWS.

Get an introduction to the field of data engineering and why it’s so important in today’s business world. Explore a variety of data types, data lakes, data sources, and how to use built-in AWS components such as DynamoDB, Kinesis, and S3 to store and manage your streams. Find out how to leverage the full power of an end-to-end data engineering pipeline, from selecting and configuring ingestion patterns, to storing data for analytic processing with S3. Test your new skills along the way in the hands-on data challenges at the end of each section.

Skills covered

Data EngineeringAmazon Web Services (AWS)AmazonCloud ServicesCloud PlatformsCloud ComputingData ScienceOne-Off

Concepts

0. Introduction

  • 01 - Data engineering with AWS

1. Data Engineering

  • 02 - Overview of data engineering
  • 03 - Importance of data engineering
  • 04 - Types of data
  • 05 - Overview of data lakes
  • 06 - Introduction to data engineering pipeline

2. Data Sources

  • 07 - Overview of data sources
  • 08 - DynamoDB overview
  • 09 - Understanding DynamoDB partitions and streams
  • 10 - Lab - Set up CLI to create DynamoDB tables
  • 11 - Lab - Create DynamoDB tables via Python script
  • 12 - Challenge - DynamoDB
  • 13 - Solution - DynamoDB

3. Ingestion

  • 14 - Data ingestion overview and design considerations
  • 15 - Ingestion technology landscape
  • 16 - Kinesis Data Streams overview
  • 17 - Kinesis producers and consumers
  • 18 - Kinesis Data Firehose
  • 19 - Demo Firehose

4. Storage

  • 20 - Data storage overview and design considerations
  • 21 - Storage technology landscape
  • 22 - Elastic cache overview
  • 23 - Amazon S3 overview
  • 24 - Amazon S3 storage classes and lifecycle rules
  • 25 - Amazon S3 Versioning and encryption
  • 26 - Amazon S3 security and event notifications
  • 27 - Challenge - Amazon S3
  • 28 - Solution - Amazon S3

Conclusion

  • 29 - Next steps

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