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

What is data exploration?

Data exploration is the process of examining, analyzing, and visualizing data in order to discover patterns, relationships, and insights. It involves the use of various query and data visualization techniques to understand the underlying structure of the data, identify trends, and gain a deeper understanding of the data. Data exploration is essential for making informed decisions about your data.

Data exploration is typically one of the first steps in data analysis and is used to get a sense of what the data contains, to identify potential problems or errors in the data, and to form hypotheses about the relationships between columns and tables. By exploring data, analysts can better understand the characteristics of the data, such as its distribution, variance, and range, and identify any outliers or anomalies that may need to be addressed. This process can also help identify opportunities for further analysis or data collection.

Data exploration can...