Data pipelines are the foundation for success in data analytics and machine learning. Moving data from diverse sources and processing it to provide context is the difference between having data and 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 critical decision points when implementing pipelines, such as data pipeline design patterns, data ingestion implementation, data transformation, the orchestration of pipelines, and build versus buy decision making. 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 machine learning and analytics needs. Considerations for pipeline maintenance, testing, and alerting.