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Data Wrangling with SQL

Data Wrangling with SQL

By : Raghav Kandarpa, Shivangi Saxena
4.6 (25)
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Data Wrangling with SQL

Data Wrangling with SQL

4.6 (25)
By: Raghav Kandarpa, Shivangi Saxena

Overview of this book

The amount of data generated continues to grow rapidly, making it increasingly important for businesses to be able to wrangle this data and understand it quickly and efficiently. Although data wrangling can be challenging, with the right tools and techniques you can efficiently handle enormous amounts of unstructured data. The book starts by introducing you to the basics of SQL, focusing on the core principles and techniques of data wrangling. You’ll then explore advanced SQL concepts like aggregate functions, window functions, CTEs, and subqueries that are very popular in the business world. The next set of chapters will walk you through different functions within SQL query that cause delays in data transformation and help you figure out the difference between a good query and bad one. You’ll also learn how data wrangling and data science go hand in hand. The book is filled with datasets and practical examples to help you understand the concepts thoroughly, along with best practices to guide you at every stage of data wrangling. By the end of this book, you’ll be equipped with essential techniques and best practices for data wrangling, and will predominantly learn how to use clean and standardized data models to make informed decisions, helping businesses avoid costly mistakes.
Table of Contents (21 chapters)
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1
Part 1:Data Wrangling Introduction
4
Part 2:Data Wrangling Techniques Using SQL
9
Part 3:SQL Subqueries, Aggregate And Window Functions
13
Part 4:Optimizing Query Performance
15
Part 5:Data Science And Wrangling

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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Data Wrangling with SQL
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