Fuzzy Data Matching with SQL: Enhancing Data Quality and Query Performance (Paperback)

If you were handed two different but related sets of data, what tools would you use to find the matches? What if all you had was SQL SELECT access to a database? In this practical book, author Jim Lehmer provides best practices, techniques, and tricks to help you import, clean, match, score, and think about heterogeneous data using SQL.

DBAs, programmers, business analysts, and data scientists will learn how to identify and remove duplicates, parse strings, extract data from XML and JSON, generate SQL using SQL, regularize data and prepare datasets, and apply data quality and ETL approaches for finding the similarities and differences between various expressions of the same data.

Full of real-world techniques, the examples in the book contain working code. You'll learn how to:

• Identity and remove duplicates in two different datasets using SQL
• Regularize data and achieve data quality using SQL
• Extract data from XML and JSON
• Generate SQL using SQL to increase your productivity
• Prepare datasets for import, merging, and better analysis using SQL
• Report results using SQL
• Apply data quality and ETL approaches to finding similarities and differences between various expressions of the same data

如果你手上有兩組不同但相關的數據，你會使用哪些工具來找出匹配的數據？如果你只能使用 SQL SELECT 來訪問數據庫，你會怎麼做？在這本實用的書中，作者Jim Lehmer提供了使用SQL進行異質數據的導入、清理、匹配、評分和思考的最佳實踐、技巧和訣竅。

數據庫管理員、程序員、業務分析師和數據科學家將學習如何識別和刪除重複數據、解析字符串、從XML和JSON中提取數據、使用SQL生成SQL、規範化數據和準備數據集，以及應用數據質量和ETL方法來找出相同數據的不同表達之間的相似性和差異。

這本書充滿了實際的技巧，書中的例子包含了可運行的代碼。你將學習如何：
- 使用SQL識別並刪除兩個不同數據集中的重複數據
- 使用SQL規範化數據並實現數據質量
- 從XML和JSON中提取數據
- 使用SQL生成SQL以提高生產力
- 使用SQL準備數據集以進行導入、合併和更好的分析
- 使用SQL報告結果
- 應用數據質量和ETL方法來找出相同數據的不同表達之間的相似性和差異。