Data Lake Architecture on AWS with .NET and S3

Managing large-scale datasets requires more than just a standard database; it requires a robust data lake architecture. This course provides a clear path to understanding how various cloud services work together to store, transform, and analyze data efficiently. You will transition from understanding basic storage to implementing a complete data pipeline that moves information from local sources into a structured, searchable cloud environment. By focusing on serverless components, you will learn to build systems that scale automatically while keeping costs low. What you'll learn: - Understand the core principles of data lake architecture and cloud storage fundamentals. - Configure S3 buckets and Kinesis Data Firehose for reliable, real-time data ingestion. - Master AWS Glue for automated data cataloging and schema discovery. - Query large datasets using standard SQL with Athena without managing any servers. - Implement the AWS SDK for .NET to interact with cloud services programmatically using modern C# patterns. - Apply Parquet file formatting to optimize storage costs and improve query performance. - Practice moving data from local SQL databases to cloud-based RDS environments. The course begins with foundational terminology and service definitions before moving into the logic of data movement and transformation. You will read through practical implementation scenarios and code-based examples to bridge the gap between local databases and cloud-native analytics. This course is designed for beginner developers and data enthusiasts with a basic understanding of C# who want to explore cloud data engineering. No prior cloud experience is required to start. Start building your foundational knowledge of cloud-based data engineering today.