Book Image

Data Exploration and Preparation with BigQuery

By : Mike Kahn
Book Image

Data Exploration and Preparation with BigQuery

By: Mike Kahn

Overview of this book

Data professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.
Table of Contents (21 chapters)
Free Chapter
1
Part 1: Introduction to BigQuery
4
Part 2: Data Exploration with BigQuery
10
Part 3: Data Preparation with BigQuery
14
Part 4: Hands-On and Conclusion

Data loading from CSV upload

In the previous two chapters, we loaded data from a local file and from Google Cloud Storage. For this example, we will load data again using local CSV files. If you have not already, download the two datasets in the Technical requirements section to your local workstation so you can load them into BigQuery.

Follow these steps to load our example datasets into BigQuery:

  1. Open the BigQuery console: https://console.cloud.google.com/bigquery.
  2. Select your project and create a new dataset:

Figure 13.3 – Create a new dataset in an expanded project in the BigQuery console

  1. Give the dataset the dataset ID ch13, keep it set to multi-region US, and leave all options as the default. Click Create dataset.
  2. Click the three dots on your newly created dataset and click Create table, as shown in the following screenshot.

Figure 13.4 – Create a table in the newly created...