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  • Book Overview & Buying SQL for Data Analytics
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SQL for Data Analytics

SQL for Data Analytics - Fourth Edition

By : Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik
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SQL for Data Analytics

SQL for Data Analytics

By: Jun Shan, Benjamin Johnston, Haibin Li, Matt Goldwasser, Upom Malik

Overview of this book

SQL remains one of the most essential tools for modern data analysis and mastering it can set you apart in a competitive data landscape. This book helps you go beyond basic query writing to develop a deep, practical understanding of how SQL powers real-world decision-making. SQL for Data Analytics, Fourth Edition, is for anyone who wants to go beyond basic SQL syntax and confidently analyze real-world data. Whether you're trying to make sense of production data for the first time or upgrading your analytics toolkit, this book gives you the skills to turn data into actionable outcomes. You'll start by creating and managing structured databases before advancing to data retrieval, transformation, and summarization. From there, you’ll take on more complex tasks such as window functions, statistical operations, and analyzing geospatial, time-series, and text data. With hands-on exercises, case studies, and detailed guidance throughout, this book prepares you to apply SQL in everyday business contexts, whether you're cleaning data, building dashboards, or presenting findings to stakeholders. By the end, you'll have a powerful SQL toolkit that translates directly to the work analysts do every day. *Email sign-up and proof of purchase required
Table of Contents (21 chapters)
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1
Part 1: Data Management Systems
6
Part 2: Data Presentation and Manipulation
12
Part 3: Advanced Topics on Analytics
19
Other Books You May Enjoy
20
Index

Applying data analysis using SQL

Given all the preceding discussions, you are now familiar with the common data processing flow. This includes ingesting source files into staging areas, cleansing and transforming raw data, loading transformed data into the data warehouse, and retrieving results using complex queries for downstream analysis. All these can be achieved using SQL. In the following exercises, you will practice your knowledge by applying SQL to each step. The end goal of the exercises is to move the data from a source system file to a data warehouse that is usable for data analysis tools.

Exercise 14.1: Copying from a file into the staging table

The first step of data processing is to ingest the data from the source into the staging area. Source data usually comes in from transactional systems as text files. So, you will start by copying a file into a staging table in this exercise.

ZoomZoom has a smaller transactional system that runs in parallel with the main...

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SQL for Data Analytics
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