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

Future directions

As we continue the final chapter of Data Exploration and Preparation with BigQuery, it is essential to turn our attention to the horizons of what lies ahead. The field of data exploration, preparation, and analytics is in a state of constant evolution, and BigQuery continues to be at the forefront of these innovations.

BigQuery is becoming more of a platform for all data services. For example, you can run SQL and Spark workloads from BigQuery using serverless DataProc. Data quality, profiling, and lineage on BigQuery tables can be done with Dateplex. Remote machine learning models that run on Vertex AI can be executed directly from BigQuery Studio with SQL. BigQuery as a platform will continue to evolve. In this section, we will explore the future directions and emerging trends that will shape the path of data analysis and decision-making with BigQuery.

More integration with AI and ML

AI and ML are increasingly becoming integral to data analytics. BigQuery...