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

Exercise and use case overview

The sample data in this exercise is representative of data sources that would be used in transportation or fleet usage data analytics. The queries in this chapter can be replicated and reused with actual business data. As transportation data is rich in location and geography information, we will use the BigQuery GIS and geospatial analytics (https://cloud.google.com/bigquery/docs/geospatial-intro) approaches described in the Google Cloud docs [2]. See the following diagram for our example data source and some of the columns that will be used to derive insights.

Figure 12.2 – Transportation data and visualization tool approaches

Reviewing Figure 12.2, you can see the gps-data table and the columns that we will use in this hands-on example. We will do some basic data preparation in this example to get to the insights quicker and will change the TIME column to DATE data type so it can be more easily displayed in charts...