5 Beginner AWS Data Engineering Projects - Course Curriculum

5 Modules

27 Lessons

Self-paced Learning

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Module 1: Project 1: Building Serverless ETL Pipelines with AWS Glue

6 lessons

Course Overview

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Project Overview

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1.1 Create S3 Buckets and Upload Sample Data

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1.2 Create Glue Database and Crawler

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1.3 Create and Run the ETL Job

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1.4 Verify Results and Cleanup

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Module 2: Project 2: Visual Data Cleaning with AWS Glue DataBrew

5 lessons

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2.1 Create S3 Bucket and Upload Sample Data

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2.2 Create DataBrew Dataset and Profile Data

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2.3 Create and Apply Data Cleaning Recipe

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2.4 Run Job and Cleanup

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Module 3: Project 3: Building Real-time Streaming Pipelines with Amazon Kinesis

5 lessons

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3.1 Create Kinesis Stream and S3 Bucket

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3.2 Create Stream Processing Lambda

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3.3 Send Test Events and Monitor

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3.4 Verify Results and Cleanup

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Module 4: Project 4: Building a Cloud Data Warehouse with Redshift Serverless and Spectrum

5 lessons

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4.1 Create Redshift Serverless and Load Data

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4.2 Create Tables and Optimize Schema

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4.3 Run Analytics Queries

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4.4 Use Redshift Spectrum and Cleanup

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Module 5: Project 5: Orchestrating Data Workflows with AWS Step Functions

6 lessons

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5.1 Create Lambda Functions for Pipeline Steps

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5.2 Create SNS Topics for Notifications

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5.3 Create the Step Functions State Machine

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5.4 Test the Pipeline End-to-End

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5.5 View Execution History and Cleanup

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