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

In this chapter, we described BigQuery and gained an understanding of the Google services that deliver the petabyte-scale trillion-row analytic serverless data warehouse. We discussed tools for the administration of BigQuery resources and went over IAM, including how to secure access, down to the table level.

We discussed cost and how to control costs via pricing models and query and table best practices. In the latter part of this chapter, we described how to extend data in BigQuery with BigQuery ML (BQML), public datasets, and external connections. By learning how BigQuery works, data analysts will be ready to gain skills to use this powerful data warehouse to reduce time to insights and derive more business value from large-scale datasets.

In the next chapter, we will go further into BigQuery organization and design. You will master BigQuery resource hierarchy and schema design practices.