Serverless Data Pipelines

Build practical skills to design, deploy, and operate cloud-native data pipelines using serverless services and event-driven architectures. In this course, you will learn how modern organizations process data without managing servers, using scalable services such as AWS Lambda, Amazon S3, Amazon SQS, Amazon CloudWatch, AWS Step Functions, and Amazon DynamoDB. You will begin by exploring the foundations of serverless computing and event-driven data pipelines. You will understand how serverless systems differ from traditional ETL workflows, how events trigger processing, and how queues support asynchronous communication between services. Next, you will learn how to build serverless ingestion and transformation workflows. You will process uploaded JSON and CSV files, apply lightweight ETL logic using Python, generate processed outputs, and address common challenges such as validation errors, inconsistent records, and schema changes. The course then focuses on reliability, monitoring, and production deployment. You will explore retry handling, dead letter queues, FIFO queues, deduplication, CloudWatch alarms, workflow orchestration, persistent storage, cost optimization, and operational best practices. Finally, you will apply your learning through hands-on demonstrations and assessments that help you design reliable, scalable, and production-ready serverless data pipelines. By the end of this course, you will be able to: -Design event-driven serverless data pipeline architectures -Build ingestion workflows using AWS Lambda, Amazon S3, and Amazon SQS -Process and transform JSON and CSV data using Python -Implement retry handling, dead letter queues, and failure recovery patterns -Monitor, optimize, and deploy production-ready serverless pipelines This course is ideal for learners who want to build practical cloud data engineering skills and understand how serverless services support scalable, reliable, and cost-efficient data processing. Join us to develop the te