This blog post discusses optimizing data pipelines in cloud environments using AWS services. It describes a typical serverless data pipeline that includes AWS Glue for ETL processes, Amazon S3 for data lake storage, and Amazon QuickSight for analysis. The post outlines strategies to improve performance, including scaling cluster capacity, parallelizing tasks, and using efficient data formats. It also explains how to enhance data delivery using AWS Glue Workflows and optimize insights with QuickSight’s SPICE caching. The article concludes by demonstrating how to automate the entire pipeline using AWS Step Functions and CloudWatch Event Triggering, ensuring timely and ordered updates of QuickSight datasets for up-to-date insights.