AWS Data Engineering: Building Analytics Pipelines

Managing large-scale data flows requires a secure, scalable, and efficient infrastructure. This text-based course guides you through the process of building modern data pipelines using the AWS analytics ecosystem. You will transition from understanding basic cloud storage to designing end-to-end data pipelines that ingest, process, store, and query data. By studying clear architectural explanations and analyzing structured code examples, you will gain the practical knowledge needed to orchestrate complex data workflows in the cloud. What you'll learn: - Understand core cloud data concepts, security configurations, and identity management using AWS S3 and IAM. - Build scalable batch and real-time ingestion pipelines using AWS Lambda, Kinesis, and Glue. - Process large-scale datasets with PySpark on AWS EMR and manage metadata catalogs using AWS Glue. - Query data directly in S3 using Athena and perform high-performance data warehousing operations in Redshift. - Apply modern data lakehouse design patterns, including transactional table formats like Apache Iceberg on AWS. - Configure automated data pipeline deployments using fundamental Infrastructure-as-Code concepts. The curriculum begins with foundational cloud storage and security definitions before progressing to data ingestion, distributed processing with Spark, serverless querying, and data warehousing. You will study step-by-step written guides and SQL/Python code snippets designed to build your engineering confidence. This course is designed for aspiring data engineers, database administrators, and software developers who are new to AWS analytics services. No prior cloud engineering experience is required. Start reading today to build your foundation in cloud data engineering.