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 went through the power and versatility of using Jupyter notebooks for data exploration. We went over the different notebook options available for usage in Google Cloud including Vertex AI Workbench instances and Colab notebooks. Each service has a specific target user and both are collaborative and have the same general purpose – extending your BigQuery data exploration with Python and JupyterLab. With practical examples and step-by-step instructions, the chapter equips you with the necessary knowledge to effectively explore and prepare data, using notebooks in conjunction with BigQuery.

In the next chapter, we will delve into exploring and visualizing data to enable you further, with even more options to explore data in BigQuery. Take a look at the links in the Further reading section for additional information related to this chapter.