Fuzzy Data Matching with SQL : Enhancing Data Quality and Query Performance

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. Full of real-world techniques, the examples in the book contain working code.

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 SQLRegularize data and achieve data quality using SQLExtract data from XML and JSONGenerate SQL using SQL to increase your productivityPrepare datasets for import, merging, and better analysis using SQLReport results using SQLApply data quality and ETL approaches to finding similarities and differences between various expressions of the same data