AWS ETL Fundamentals

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Overview

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The AWS ETL Fundamentals course is designed to provide learners with a strong foundation in AWS data integration, Extract, Transform, and Load (ETL), analytics, streaming, and business intelligence services. This course introduces the core AWS services used to ingest, catalog, transform, process, govern, and visualize data, enabling organizations to build scalable and efficient data pipelines in the cloud. The course covers key concepts such as AWS Glue, AWS Glue Data Catalog, crawlers, classifiers, ETL jobs, data quality, workflows, and AWS Glue DataBrew for data preparation and transformation. Learners will also explore Amazon Athena for serverless querying, AWS Lake Formation for data governance, Amazon EMR for big data processing, Amazon Kinesis for real-time data streaming, Amazon Managed Service for Apache Flink, Amazon Managed Streaming for Apache Kafka (MSK), Amazon OpenSearch Service for search and analytics, and Amazon QuickSight for business intelligence and data visualization. This course is structured into two modules, each containing lessons and video lectures. Learners will engage with approximately **5–7 hours** of video content, combining conceptual understanding with practical demonstrations of AWS analytics and ETL services. Each module includes quizzes to reinforce learning and validate understanding of key concepts. **Course Modules:** Module 1: Foundations of AWS Analytics and ETL Module 2: Data Analytics, Streaming, and Visualization By the end of this course, a learner will be able to: * Understand the core AWS services used for data integration, ETL, and analytics. * Build and automate ETL pipelines using AWS Glue and its associated services. * Catalog, prepare, and manage data using AWS Glue Data Catalog, crawlers, and data quality features. * Query, process, and govern data using Amazon Athena, AWS Lake Formation, and Amazon EMR. * Implement real-time data streaming solutions using Amazon Kinesis, Amazon Managed Service for Apache Flink, and Amazon MSK. * Analyze operational and business data using Amazon OpenSearch Service. * Create interactive dashboards and visualizations using Amazon QuickSight. * Apply AWS best practices for building scalable, governed, and efficient data pipelines. This course is ideal for data engineers, data analysts, cloud engineers, solutions architects, ETL developers, and IT professionals who want to build a strong foundation in AWS data engineering and analytics services. It also provides the foundational knowledge required for implementing modern data pipelines, supporting analytics workloads, and preparing for AWS data engineering and analytics-focused roles.

Syllabus

Foundations of AWS Analytics and ETL

In this section, you'll build a strong foundation in AWS analytics and ETL services, learning how to discover, catalog, transform, and prepare data for analytics workloads on AWS. You'll begin by exploring AWS analytics services, including AWS Glue, the AWS Glue Data Catalog, and Amazon Redshift Spectrum, gaining an understanding of how these services work together to support modern data integration and analytics solutions. As you progress, you'll dive into AWS Glue and learn how to automate data discovery and transformation using crawlers, classifiers, databases, tables, and ETL jobs. Through guided demonstrations, you'll gain hands-on experience creating Glue crawlers, running ETL jobs, validating outputs, and managing data pipelines. The section further introduces AWS Glue Data Quality, AWS Glue Workflows, AWS Glue API capabilities, and AWS Glue DataBrew. You'll learn how to improve data quality, orchestrate ETL processes, identify sensitive data, and prepare datasets while handling personally identifiable information (PII) using DataBrew. By the end of this section, you'll have a solid understanding of AWS Glue and related analytics services, enabling you to build, automate, and manage scalable ETL and data preparation workflows on AWS.

Data Integration and ETL with AWS Glue

In this module, you'll build a strong foundation in AWS Glue and its capabilities for data integration, discovery, transformation, and preparation. You'll explore how AWS Glue simplifies ETL (Extract, Transform, and Load) workflows by automating data cataloging, schema discovery, and data processing, enabling organizations to efficiently prepare data for analytics and business intelligence. You'll begin by learning about AWS Glue Crawlers and Classifiers, understanding how they automatically discover data sources and populate the AWS Glue Data Catalog. Through guided demonstrations, you'll gain hands-on experience creating Glue Crawlers, running ETL jobs, validating job outputs, and managing Glue Databases, Tables, and Jobs to build scalable data pipelines. As you progress, you'll explore advanced AWS Glue capabilities, including AWS Glue Data Quality, AWS Glue Workflows, and AWS Glue APIs for identifying and managing sensitive data. You'll also learn how AWS Glue DataBrew simplifies data preparation, cleansing, and transformation while handling personally identifiable information (PII) to improve data quality and governance. By the end of this module, you'll have a solid understanding of AWS Glue and its ecosystem, enabling you to discover, catalog, transform, govern, and prepare data while building automated, scalable, and reliable ETL workflows on AWS.

Data Analytics, Streaming, and Visualization

In this section, you'll build a strong foundation in AWS data analytics, streaming, and visualization services, learning how to query, process, analyze, and visualize data across modern AWS data platforms. You'll begin by exploring Amazon Athena and AWS Lake Formation, gaining an understanding of how to securely query data stored in Amazon S3, govern data lakes, and integrate Athena with AWS Glue Data Catalog and Hive Metastore for efficient analytics. As you progress, you'll discover Amazon EMR and learn how to process large-scale datasets using managed big data frameworks. You'll also explore Amazon Kinesis, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK) to understand how AWS enables real-time data ingestion, stream processing, and analytics. The section further introduces Amazon OpenSearch Service and Amazon QuickSight, enabling you to search, analyze, visualize, and gain actionable insights from structured and streaming data through interactive dashboards and business intelligence solutions. By the end of this section, you'll have a solid understanding of AWS analytics, streaming, and visualization services, enabling you to build scalable data analytics pipelines and generate meaningful business insights on AWS.

Streaming Analytics and Business Intelligence on AWS

In this section, you'll build a strong foundation in AWS streaming analytics and business intelligence services, learning how to ingest, process, analyze, and visualize real-time data on AWS. You'll begin by exploring Amazon Kinesis and its streaming capabilities, gaining an understanding of how organizations collect and process continuous data from applications, websites, IoT devices, and other event sources. As you progress, you'll explore Amazon Managed Service for Apache Flink and Amazon Managed Streaming for Apache Kafka (Amazon MSK), learning how to build scalable stream-processing applications and process real-time data using managed Apache Flink and Apache Kafka services. Through practical demonstrations, you'll understand how these services work together to deliver low-latency analytics and event-driven architectures. The section further introduces Amazon OpenSearch Service and Amazon QuickSight. You'll learn how to perform search and log analytics using OpenSearch Service and create interactive dashboards and visualizations with Amazon QuickSight to transform raw data into meaningful business insights that support informed decision-making. By the end of this section, you'll have a solid understanding of AWS streaming analytics and business intelligence services, enabling you to build scalable real-time data processing solutions, analyze streaming data, and create impactful visualizations for modern analytics workloads on AWS.