September 2025
Intermediate to advanced
106 pages
2h 32m
English
Overview
"Extract, transform, load" (ETL) is at the center of every application of data, from business intelligence to AI. Constant shifts in the data landscape—including the implementations of lakehouse architectures and the importance of high-scale real-time data—mean that today's data practitioners must approach ETL a bit differently.
This updated technical guide offers data engineers, engineering managers, and architects an overview of the modern ETL process, along with the challenges you're likely to face and the strategic patterns that will help you overcome them. You'll come away equipped to make informed decisions when implementing ETL and confident about choosing the technology stack that will help you succeed.
- Discover what ETL looks like in the new world of data lakehouses
- Learn how to deal with real-time data
- Explore low-code ETL tools
- Understand how to best achieve scale, performance, and observability
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