Practical Dataops: Delivering Agile Data Science at Scale

Atwal, Harvinder



Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles.


This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. 



What You Will Learn

  • Develop a data strategy for your organization to help it reach its long-term goals
  • Recognize and eliminate barriers to delivering data to users at scale
  • Work on the right things for the right stakeholders through agile collaboration
  • Create trust in data via rigorous testing and effective data management
  • Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes
  • Create cross-functional self-organizing teams focused on goals not reporting lines
  • Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products


Who This Book Is For


Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.


《實用的 DataOps》向您展示如何從各種原始數據來源優化數據供應鏈,以獲得機器學習模型或其他數據導向輸出。本書通過採用精益思維和敏捷軟件開發原則,提供了一種消除浪費和改善數據生產者、數據消費者和組織其他部門之間協作的方法。

本書幫助您通過數據管理和 DevOps 實踐來提高分析應用程序開發的速度和準確性,這些實踐可以安全地擴展數據訪問權限,並通過自動化、測試和集成快速增加可重現的數據產品的數量。本書還展示了如何收集反饋並監控性能,以管理和持續改進您的流程和輸出。

- 為您的組織制定數據戰略,以幫助實現長期目標
- 辨識並消除交付大規模數據給用戶的障礙
- 通過敏捷協作為正確的利益相關者進行工作
- 通過嚴格的測試和有效的數據管理建立對數據的信任
- 通過監測部署和測量結果建立學習和持續改進的文化
- 建立以目標為重心而不是報告線的跨職能自組織團隊
- 在支持人工智能、機器學習和其他分析數據產品的情況下構建強大可靠的數據管道

- 數據科學和高級分析專家
- CIO、CDO(首席數據官)、首席分析官
- 商業分析師、商業團隊負責人和 IT 專業人員(數據工程師、開發人員、架構師和數據庫管理員),他們支持數據團隊,希望大幅提高組織從數據中獲得的價值。本書對於希望克服交付時間長、數據質量差、維護成本高以及在將數據科學輸出和機器學習應用投入面向客戶的生產環境中遇到的擴展困難等挑戰的數據專業人士尤其適用。


Harvinder Atwal is a data professional with an extensive career using analytics to enhance customer experience and improve business performance. He is excited not just by algorithms, but also by the people, processes, and technology changes needed to deliver value from data. He enjoys the exchange of ideas, and has spoken at O'Reilly Strata Data Conference London, ODSC London, and Data Leaders Summit Barcelona.
Harvinder currently leads the Group Data function responsible for the entire data life cycle, including: data acquisition, data management, data governance, cloud and on-premise data platform management, data engineering, business intelligence, product analytics, and data science at Moneysupermarket Group. Previously, he led analytics teams at Dunnhumby, Lloyds Banking Group, and British Airways. His education includes an undergraduate degree from University College London and a master's degree in Operational Research from Birmingham University's School of Engineering.


Harvinder Atwal 是一位資料專業人士,擁有豐富的職業生涯,利用分析來增強客戶體驗和提升業務績效。他對算法以及從數據中獲得價值所需的人、流程和技術變革都感到興奮。他喜歡交流思想,曾在 O'Reilly Strata Data Conference London、ODSC London 和 Data Leaders Summit Barcelona 發表演講。

Harvinder 目前領導 Moneysupermarket Group 的 Group Data 部門,負責整個數據生命周期,包括:數據獲取、數據管理、數據治理、雲端和本地數據平台管理、數據工程、商業智能、產品分析和數據科學。在此之前,他曾在 Dunnhumby、Lloyds Banking Group 和 British Airways 領導分析團隊。他的教育背景包括倫敦大學學院的學士學位和伯明翰大學工程學院的運籌學碩士學位。