Introduction to SQL Using Google BigQuery

This is the repository for the LinkedIn Learning course Introduction to SQL Using Google BigQuery. The full course is available from LinkedIn Learning.

As more and more companies adapt to cloud platforms—which are tailor-made for analytics and machine learning—many companies are also moving away from Amazon Web Services and trying alternative cloud vendors. Google Big Query has been gaining steam in the market in recent years, and provides some game-changing technology in the modern data landscape. In this course, instructor Danny Ma shows data professionals how to be productive and effective using GBQ and use the power of the cloud to analyze data at scale. If you have some SQL experience and want to learn what’s available on a modern cloud platform, join Danny in this course to see how BigQuery can help jumpstart your data career.

See the readme file in the main branch for updated instructions and information.

Instructions

Download or clone the entire repository to find the datasets and exercise files for this course. This repository only has a single main branch and should contain all updated files.

You can find 3 raw CSV files in the data/ folder which can be used with this course. Instructions to upload the data into BigQuery can be found inside the course videos.

Each folder corresponds with a movie with an exercise file. The naming convention is CHAPTER#_MOVIE#. As an example, the folder named 02_03 corresponds to the second chapter and the third video in that chapter.

Installing

1. To use these exercise files, you must have an active Google Cloud Project which has BigQuery enabled. Visit this guide to setup a free BigQuery Sandbox instance.
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree or download it manually as a ZIP file
3. Follow course instructions to upload the CSV files from the data/ folder into tables in a new dataset called wisdom_pets
4. View the individual solution.sql files within each chapter and movie folder for the relevant solutions
5. Copy and paste the code directly into the BigQuery console and run the SQL queries to see the expected results.