This blog post explains how to build and optimize a serverless data pipeline on AWS, from data ingestion to business insights. It demonstrates using real-world Helsinki public transport data stored in DynamoDB tables, transforming it into a centralized S3 data lake using AWS Glue Jobs, and analyzing it with Amazon QuickSight. The article covers key AWS components including AWS Glue for ETL processing, Amazon Athena for SQL queries, and QuickSight’s SPICE engine for fast data visualization. It also provides optimization strategies for AWS Glue performance and outlines how to automate the entire pipeline using AWS Step Functions and CloudWatch Event Triggering. The solution ensures QuickSight datasets are refreshed in the correct order immediately after Glue Workflows complete, keeping business insights as current as possible while managing costs effectively.