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Data Exploration and Preparation with BigQuery

Data Exploration and Preparation with BigQuery

By : Mike Kahn
5 (17)
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Data Exploration and Preparation with BigQuery

Data Exploration and Preparation with BigQuery

5 (17)
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)
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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

These sample data sources outlined in the technical requirement section are representative of data sources you would use in marketing and advertising analytics. The three data sources contain jewelry store advertising, analytics, and sales data. The queries and approaches in this solution can be replicated and used for similar use cases, with actual business data. See the following diagram of the tables and some of the column associations.

Figure 11.2 – The advertising and sales datasets and their relationships

Reviewing Figure 11.2, you can see some of the possible relationships between the tables. The Ads Data and Google Analytics Data tables both have a DATE column (time and date, respectively). This can help us correlate ad keywords and site visits, possibly showing the effectiveness of advertising campaigns. The datetime column on the eCommerce Data table could then be used to determine whether an ad placement...

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