Summary

In this chapter, we looked at a critical part of a data engineer’s job–designing and orchestrating data pipelines. First, we examined some of the core concepts around data pipelines, such as scheduled and event-based pipelines, and how to handle failures and retries.

We then looked at four different AWS services that can be used for creating and orchestrating data pipelines. This included AWS Data Pipeline (now in maintenance mode), AWS Glue workflows, Amazon MWAA, and AWS Step Functions.

Then, in the hands-on section of this chapter, we built an event-driven pipeline. We used two AWS Lambda functions for processing, and an Amazon SNS topic for sending out notifications about failures. Then, we put these pieces of our data pipeline together into a state machine orchestrated by AWS Step Functions. We also looked at how to handle errors.

So far, we have looked at how to design the high-level architecture for a data pipeline and examined services for...