Densmore, James
Data Pipelines Pocket Reference: Moving and Processing Data for Analytics
- ISBN 13:
- 9781492087830
- author:
- Densmore, James
- format:
- Paperback / softback
- publisher:
- O'Reilly Media, Inc, USA
- language:
- English
- Publication Year:
- 2021
- Pages:
- 200
- Dimensions:
- 17.8 x 10.8 x 1.5 centimeters (0
- Genre:
- Computers, Computer Science, Data Modeling,
- Condition:
- New
- Availability:
- Item usually sent within 10 working days
Description
Data Pipelines Pocket Reference: A Practical Guide to Moving and Processing Data for Analytics Data pipelines are the backbone of successful data analytics, enabling you to extract value from diverse data sources. This pocket reference explains how data pipelines work, covering common considerations and key decision points when implementing them. You'll learn about batch versus streaming data ingestion, build versus buy, and how to apply foundational concepts to open source frameworks, commercial products, and homegrown solutions. You'll discover how data pipelines support analytics and reporting needs, and how to maintain, test, and alert your pipelines. This book addresses the most common decisions made by data professionals, providing a comprehensive understanding of modern data infrastructure, including cloud platforms. By reading this book, you'll gain a practical understanding of data pipelines and how to build and maintain them, ensuring you can extract value from your data and drive business insights.
Related Products
Related Products
Data Science for Business: What you need to know about data mining and data-analytic thinking
Provost, Foster
Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)
Chen, Daniel
Data Analytics & Visualization All-In-One for Dummies
Hyman, Jack A.
Data Governance: The Definitive Guide: People, Processes, and Tools to Operationalize Data Trustworthiness
Eryurek, Evren
Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & Machine Learning)
Theobald, Oliver