Fundamentals of Data Observability: Implement Trustworthy End-To-End Data Solutions

Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work.

Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need.

• Learn the core principles and benefits of data observability
• Use data observability to detect, troubleshoot, and prevent data issues
• Follow the book's recipes to implement observability in your data projects
• Use data observability to create a trustworthy communication framework with data consumers
• Learn how to educate your peers about the benefits of data observability

快速檢測、排除故障並預防各種數據問題，透過數據可觀察性（data observability）的一系列最佳實踐，使數據團隊能夠更清楚地了解數據及其使用情況。如果你是一位依賴數據質量的數據工程師、數據架構師或機器學習工程師，本書將向你展示如何在日常工作中專注於引入數據可觀察性的實際方面。

作者Andy Petrella幫助你建立正確的習慣，以識別和解決數據問題，例如數據漂移和質量不佳，從而阻止它們在數據應用、流程和分析中的傳播。你將學習引入數據可觀察性的方法，包括建立一個生成和收集所需所有信息的框架。

本書的內容包括：
- 學習數據可觀察性的核心原則和好處
- 使用數據可觀察性來檢測、排除故障並預防數據問題
- 按照本書的指南在數據項目中實施可觀察性
- 使用數據可觀察性來建立與數據使用者之間的可信通信框架
- 學習如何向同事們介紹數據可觀察性的好處