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Synopsis
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
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About the Author
James is the Director of Data Infrastructure at HubSpot as well as the founder and Principal Consultant at Data Liftoff. He has more than 10 years of experience leading data teams and building data infrastructure at Wayfair, O'Reilly Media, and Degreed. James has a BS in Computer Science from Northeastern University and an MBA from Boston College.
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Data Pipelines Pocket Reference : Moving and Processing Data for Analytics
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Data Pipelines Pocket Reference : Moving and Processing Data for Analytics
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