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Data Wrangling with SQL

You're reading from  Data Wrangling with SQL

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781837630028
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Raghav Kandarpa Raghav Kandarpa
Profile icon Raghav Kandarpa
Shivangi Saxena Shivangi Saxena
Profile icon Shivangi Saxena
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1:Data Wrangling Introduction
2. Chapter 1: Database Introduction 3. Chapter 2: Data Profiling and Preparation before Data Wrangling 4. Part 2:Data Wrangling Techniques Using SQL
5. Chapter 3: Data Wrangling on String Data Types 6. Chapter 4: Data Wrangling on the DATE Data Type 7. Chapter 5: Handling NULL Values 8. Chapter 6: Pivoting Data Using SQL 9. Part 3:SQL Subqueries, Aggregate And Window Functions
10. Chapter 7: Subqueries and CTEs 11. Chapter 8: Aggregate Functions 12. Chapter 9: SQL Window Functions 13. Part 4:Optimizing Query Performance
14. Chapter 10: Optimizing Query Performance 15. Part 5:Data Science And Wrangling
16. Chapter 11: Descriptive Statistics with SQL 17. Chapter 12: Time Series with SQL 18. Chapter 13: Outlier Detection 19. Index 20. Other Books You May Enjoy

Introduction to query optimization

Query optimization is the process of improving the performance of a SQL query by reducing the time it takes to execute and retrieve the data. The goal of query optimization is to make the query run faster, consume fewer resources, and return the desired results with minimum overhead. Query optimization becomes critical when dealing with large amounts of data and when the data needs to be processed quickly:

Figure 10.1 – Query optimization

Figure 10.1 – Query optimization

In this section, we’ll explain the importance of query optimization by mentioning the following points:

  • Scalability: As the data grows, the queries may take longer to run, leading to slower performance and longer wait times for the users. Query optimization helps in ensuring that the queries scale well and continue to perform efficiently as the data grows.
  • Resource utilization: Query optimization helps in reducing the resources required to run the query. This...
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