Data Engineering with AWS Part 1

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Dipali Kulshrestha

1:50:51

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01 - Introduction

01 - Data engineering with AWS.mp4

01:07

02 - 1. Data Engineering

01 - Overview of data engineering.mp4

02:09

02 - Importance of data engineering.mp4

03:38

03 - Types of data.mp4

04:56

04 - Overview of data lakes.mp4

05:28

05 - Introduction to data engineering pipeline.mp4

01:41

03 - 2. Data Sources

01 - Overview of data sources.mp4

01:16

02 - DynamoDB overview.mp4

07:46

03 - Understanding DynamoDB partitions and streams.mp4

04:27

04 - Lab Set up CLI to create DynamoDB tables.mp4

03:51

05 - Lab Create DynamoDB tables via Python script.mp4

07:29

06 - Challenge DynamoDB.mp4

03:23

07 - Solution DynamoDB.mp4

00:42

04 - 3. Ingestion

01 - Data ingestion overview and design considerations.mp4

02:55

02 - Ingestion technology landscape.mp4

04:34

03 - Kinesis Data Streams overview.mp4

03:34

04 - Kinesis producers and consumers.mp4

03:45

05 - Kinesis Data Firehose.mp4

05:37

06 - Demo Firehose.mp4

07:25

05 - 4. Storage

01 - Data storage overview and design considerations.mp4

02:10

02 - Storage technology landscape.mp4

01:32

03 - Elastic cache overview.mp4

02:46

04 - Amazon S3 overview.mp4

04:32

05 - Amazon S3 storage classes and lifecycle rules.mp4

02:08

06 - Amazon S3 Versioning and encryption.mp4

06:13

07 - Amazon S3 security and event notifications.mp4

11:07

08 - Challenge Amazon S3.mp4

00:49

09 - Solution Amazon S3.mp4

03:01

06 - Conclusion

01 - Next steps.mp4

00:50

Description

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.

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Amazon AWS

Dipali Kulshrestha

Instructor's Courses

AWS Solution Architect, Specializing in DevOps and Data Engineering with versatile experience in database technologies and Application Modernization. Demonstrated a history of creating Simple to implement and Easy to Maintain Solutions for complex business problems.

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