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

Summary

This chapter covered various aspects of querying data, enabling you to extract valuable insights efficiently. It emphasized the importance of understanding SQL query syntax and the structure of BigQuery queries. You were introduced to essential concepts such as SELECT statements, filtering data with WHERE clauses, aggregations with GROUP BY, and sorting results with ORDER BY.

This chapter also explored advanced querying techniques such as JOIN to combine data from multiple tables and subqueries to extract data subsets. The opportunity to save and share your queries improves data team productivity, and tools to optimize and troubleshoot queries allow you to be efficient with your analysis usage. With practical examples and best practices, you now have the skills to write complex queries and analyze data effectively in BigQuery.

In the next chapter, we will cover another approach to querying and exploring data in BigQuery: using notebooks. We will cover the various Google...