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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

SQL data types

Each value stored in a database has a fixed data type that defines its properties, storage size, and what data size that variable can hold. Every time we perform data manipulation in SQL, we need to ensure that the data type is compatible with the manipulation operation.

Data types are categorized into the following six types, which are explained in more detail here:

Figure 3.1 – SQL data types

Figure 3.1 – SQL data types

Numeric data types

Overall, there are four different numerical data types that differ by the level or type of information we are trying to store, such as the number of characters/decimal points.

Nominal data type

Numbers such as phone numbers and customer IDs use the nominal data type because number operations such as add, subtract, and compare cannot be performed on this data type. They are for informational purposes rather than analysis. Nominal data is also called qualitative or categorical.

Note

Only calculations based...

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Data Wrangling with SQL
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