Data pipelines pocket reference moving and processing data for analytics
Data pipelines are the foundation for success in data analytics and machine learning. Moving data from many diverse sources and processing 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 the...
| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Beijing :
O'Reilly
[2021]
|
| Edition: | 1st edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630834906719 |
| Summary: | Data pipelines are the foundation for success in data analytics and machine learning. Moving data from many diverse sources and processing 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 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 |
|---|---|
| Item Description: | Includes index. |
| Physical Description: | 1 online resource (200 pages) |
| ISBN: | 9781492087786 9781492087809 9781492087830 9781492087823 |
