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

Assessing dataset integrity

Dataset integrity refers to the quality and consistency of data within a dataset. It is the assurance that the data is accurate, complete, reliable, and free from errors or inconsistencies. Understanding your data’s integrity is important for ensuring the quality and usability of data and determining to what degree you will need to cleanse or transform data. A dataset with poor integrity can lead to incorrect analysis, inaccurate reports, and misinformed business decisions. There are several ways to assess dataset integrity. In this section, we will discuss techniques and considerations for assessing dataset integrity in BigQuery.

The shape of the dataset

Understanding your dataset’s shape helps you form a baseline expectation for the quality of results you will receive from queries. Consider Figure 9.3. Your dataset may be taller than wide, indicating a lot of rows and few columns. This may present a situation where you want to join...