From the Preface

Enterprises are becoming increasingly data driven, and a key component of any enterprise’s data strategy is a data warehouse—a central repository of integrated data from all across the company. Traditionally, the data warehouse was used by data analysts to create analytical reports. But now it is also increasingly used to populate real-time dashboards, to make ad hoc queries, and to provide decision-making guidance through predictive analytics. Because of these business requirements for advanced analytics and a trend toward cost control, agility, and self-service data access, many organizations are moving to cloud-based data warehouses such as Google BigQuery.

In this book, we provide a thorough tour of BigQuery, a serverless, highly scalable, low-cost enterprise data warehouse that is available on Google Cloud. Because there is no infrastructure to manage, enterprises can focus on analyzing data to find meaningful insights using familiar SQL.

Our goal with BigQuery has been to build a data platform that provides leading-edge capabilities, takes advantage of the many great technologies that are now available in cloud environments, and supports tried-and-true data technologies that are still relevant today. For example, on the leading edge, Google’s BigQuery is a serverless compute architecture that decouples compute and storage

This enables diverse layers of the architecture to perform and scale independently, and it gives data developers flexibility in design and deployment. Somewhat uniquely, BigQuery supports native machine learning and geospatial analysis. With Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, Cloud AI Platform, and many third-party integrations, BigQuery interoperates with both traditional and modern systems, at a wide range of desired throughput and latency. And on the tried-and-true front, BigQuery supports ANSI-standard SQL, columnar optimization, and federated queries, which are key to the self-service ad hoc data exploration that many users demand.

Who Is This Book For?

This book is for data analysts, data engineers, and data scientists who want to use BigQuery to derive insights from large datasets. Data analysts can interact with BigQuery through SQL and via dashboarding tools like Looker, Data Studio, and Tableau.

Data engineers can integrate BigQuery with data pipelines written in Python or Java and using frameworks such as Apache Spark and Apache Beam. Data scientists can build machine learning models in BigQuery, run TensorFlow models on data in BigQuery, and delegate distributed, large-scale operations to BigQuery from within a Jupyter notebook.