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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

Subqueries and CTEs

This chapter explores the vital concepts of subqueries and common table expressions (CTEs) in SQL for data manipulation. We cover their usage within SELECT statements, including filtering, aggregation, and joining data. Advanced topics such as nested and correlated subqueries are discussed, along with the introduction of CTEs to break down complex transformations. Performance considerations and optimization techniques are emphasized. Practical examples demonstrate the application of subqueries and CTEs in real-world data-wrangling tasks, showcasing their versatility and power. By the end, you’ll have a comprehensive understanding of leveraging these features for efficient data analysis and decision-making.

In this chapter, we will cover the following main topics:

  • Introduction to subqueries
  • Using subqueries in SELECT statements
  • Nested subqueries
  • Correlated subqueries
  • Using subqueries in INSERT, UPDATE, and DELETE statements
  • Common...
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Data Wrangling with SQL
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