This blog post discusses optimizing data pipelines in cloud environments, focusing on AWS services. It outlines a typical serverless data pipeline using AWS Glue, Amazon S3, and Amazon QuickSight. The article explains how to extract, transform, and load data from various sources into a centralized data lake using AWS Glue Jobs. It then describes strategies for optimizing the ETL process, including scaling cluster capacity, using the latest AWS Glue version, and minimizing data scans. The post also covers improving data insights with Amazon QuickSight’s SPICE feature for faster data access. Finally, it demonstrates how to automate and optimize the entire pipeline using AWS Step Functions and CloudWatch Event Triggering, ensuring timely updates of QuickSight datasets for up-to-date business analytics.