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Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.

You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions.

You'll learn:

What a data pipeline is and how it works
How data is moved and processed on modern data infrastructure, including cloud platforms
Common tools and products used by data engineers to build pipelines
How pipelines support analytics and reporting needs
Considerations for pipeline maintenance, testing, and alerting

Data Pipelines Pocket Reference: Moving and Processing Data for Analytics