Practical DataOps
Delivering Agile Data Science at Scale
A comprehensive guide to applying DevOps principles to data analytics and machine learning. Learn how to build high-performing data teams, implement continuous delivery for data pipelines, and create a culture of collaboration and continuous improvement.
What You'll Learn
Practical strategies and techniques for transforming your data organization
Chapter Overview
A structured journey through the principles and practices of DataOps
Introduction
Setting the stage for DataOps and why it matters.
The Problem
Understanding the challenges organizations face with data delivery.
Data Strategy
Building a data strategy that delivers measurable value.
Lean Thinking
Applying Lean principles to eliminate waste in data processes.
Agile Collaboration
Fostering effective collaboration between data teams.
Measurement & Feedback
Establishing metrics and feedback loops for continuous improvement.
Building Trust
Creating trusted data products and earning stakeholder confidence.
DevOps for DataOps
Applying DevOps practices to data pipelines and analytics.
Organizing for DataOps
Structuring teams and organizations for DataOps success.
Tools & Platforms
Selecting and implementing the right technology stack.
The DataOps Data Science Factory
Building a scalable, repeatable approach to data science.
International Editions
Practical DataOps is also available in translation
実践DataOps
- Publisher:
- Shoeisha (翔泳社)
- Published:
- May 2024
- Translated by:
- Daisuke Maruyama, Kazuo Matsuda, Tetsuya Seki
- ISBN:
- 978-4-7981-7924-7
DataOps实践手册：敏捷精益的数据运营
- Publisher:
- China Machine Press (机械工业出版社)
- Published:
- October 2022
- Translated by:
- Ma Huan et al.
- ISBN:
- 978-7-111-71606-8
Where to Buy
Available in paperback, Kindle, and digital formats