AWS ETL Fundamentals

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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.

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