Fuzzy Data Matching with SQL
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Author_Jim Lehmer
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SQL fuzzy data approximate string matching ETL data quality data analysis Jupyter
Product details
- ISBN 9781098152277
- Dimensions: 178 x 232mm
- Publication Date: 13 Oct 2023
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
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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
James Lehmer has been "in computers" for over three decades in various software development roles - programmer, systems programmer, software engineer, team lead, and software architect. He has worked on a variety of operating systems with a number of programming languages. James currently works in a Windows shop coding primarily in C#, but with his background in cross-platform development, he often gets tapped to deal with any *IX boxes that enter his environment.
