Serverless ELT Pipeline for Scalable Data Processing Using AWS Glue and AWS Lambda

Abstract: The Serverless ELT (Extract, Load, Transform) pipeline leveraging AWS Glue and AWS Lambda offers a modern, fully managed approach for handling large-scale data processing workloads. By adopting a serverless architecture, this solution eliminates the need for manual server provisioning, configuration, and maintenance, allowing organizations to focus solely on data operations and insights. AWS Lambda serves as the orchestration layer, automating the triggering of data ingestion workflows from multiple sources including relational databases, APIs, and object storage systems. This event-driven execution ensures that data pipelines operate efficiently, respond to changes in real-time, and scale seamlessly without manual intervention. Once ingested, data is loaded into Amazon S3, forming a centralized, durable, and secure data lake. AWS Glue then handles automated data cataloging, schema discovery, and transformation using its distributed Apache Spark-based processing engine. The serverless nature of this pipeline provides significant operational and cost advantages through pay-as-you-go pricing, automated logging, monitoring, and error handling features that reduce operational overhead and enhance system reliability.

Keywords: AWS Lambda, AWS Glue, Serverless Architecture, ELT Pipeline, Amazon S3, Cloud Computing, Data Transformation, Apache Spark, Amazon CloudWatch, Python.