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

Understanding data distributions

The distribution of a dataset is how the data is spread out or clustered around certain values or ranges. By understanding the distribution of a dataset, we can gain insights into the characteristics and patterns of the data, which can be useful in making informed decisions and predictions. Understanding the distribution of data is important for identifying patterns, detecting outliers, and making informed decisions about how to explore data.

Data distributions can be examined through descriptive statistics, which provide summary measures that help us understand the central tendency, variability, and shape of the data. Measures such as mean, median, mode, range, and standard deviation provide a snapshot of the dataset’s overall characteristics. BigQuery offers SQL functions that allow us to compute these descriptive statistics efficiently. Some of the most commonly used functions are as follows:

  • COUNT(*): This function returns the...