This blog post discusses optimizing data pipelines in cloud environments using AWS services. It highlights the challenge of scattered data across various storage formats and locations, and proposes building a centralized data lake for efficient business intelligence. The article uses real-world open data sets on Helsinki region public traffic to demonstrate the process.

The post outlines a typical serverless data pipeline on AWS, utilizing services such as AWS Glue, Amazon S3, Amazon Athena, and AWS QuickSight. It provides strategies for optimizing data delivery to the data lake, including scaling cluster capacity, using the latest AWS Glue version, and minimizing data scans.

The article also addresses optimizing the data insights experience with Amazon QuickSight, emphasizing the importance of up-to-date and instantly available data. It introduces SPICE, QuickSight’s in-memory caching solution, for improved performance.

Finally, the post explores automating and optimizing the entire data pipeline using AWS Step Functions and CloudWatch Event Triggering, ensuring efficient and timely data processing and analysis.