Fuzzy data matching with SQL : enhancing data quality and query performance / Jim Lehmer.
Material type:
- text
- unmediated
- volume
- 1098152271
- 9781098152277
- 005 23/eng/20231010
- QA76.73.S67 L44 2023
Item type | Current library | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|
![]() |
RLKU Library & Information Resource Center | 005 LEH (Browse shelf(Opens below)) | Available | 13271 | |
![]() |
RLKU Library & Information Resource Center | 005 LEH (Browse shelf(Opens below)) | Available | 13272 | |
![]() |
RLKU Library & Information Resource Center | 005 LEH (Browse shelf(Opens below)) | Available | 13273 | |
![]() |
RLKU Library & Information Resource Center | 005 LEH (Browse shelf(Opens below)) | Available | 13274 | |
![]() |
RLKU Library & Information Resource Center | 005 LEH (Browse shelf(Opens below)) | Available | 13275 |
Browsing RLKU Library & Information Resource Center shelves Close shelf browser (Hides shelf browser)
Includes bibliographical references and index.
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.
There are no comments on this title.