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

Evaluating ETL and ELT approaches for data integration

ETL and ELT are two main approaches to integrating, loading, and preparing data in BigQuery. When building a data analytics practice, to provide ongoing data value, you will want to decide on one of these approaches.

In ETL, data is extracted from a data source, transformed, and then loaded into a data warehouse or other target system. The transformation step is often complex and time-consuming, as it involves cleaning, validating, and standardizing the data. There are SaaS tools that automate and manage ETL pipelines, and there are many options today to create your own ETL pipelines by joining multiple services before the data arrives in BigQuery.

The other primary data integration approach is ELT. ELT is when data is extracted from a data source and loaded directly into a target system. Any transformation steps are then performed in the target system. This approach is often faster than ETL as the transformations can be...