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 exploration, analysis, and visualization

In this section, we will begin by exploring our data sources, doing some initial analysis, and then creating some visualizations. We will show you how to create meaningful insights from advertising, analytics, and sales data.

Let’s begin by finding the minimum and maximum date in each of our tables. This will let us know what date range we can compare across our three data sources:

SELECT min(date), max(date)
FROM `ch11.jewelry_sales_data`

We get the following result:

Figure 11.9 – Date range analysis

Running this query across our three tables shows the three datasets have a date range overlap between 2022-05-01 and 2022-12-10. We will use this window to correlate and measure ads, website visits, and sales data.

Continuing our exploration, the following query will return the unique keywords that we are using in our advertising campaigns:

SELECT DISTINCT(keywords)
FROM `ch11.jewelry_ads_data...