Valliappa Lakshmanan, Google Cloud Platform technology leader, and Jordan Tigani, director of engineering on the BigQuery team, provide best practices for modern data storage in a serverless, auto-scaling public cloud. If you want to explore parts of BigQuery that you're not familiar with or prefer to focus on specific tasks, this reference is a must.
Google BigQuery The Definitive Guide PDF Download
Businesses are increasingly data-driven, and a key part of any business data strategy is a data warehouse - a central repository of integrated data from across the enterprise. Traditionally, the data warehouse has been used by data analysts to create analytical reports. But it's also increasingly used to populate real-time dashboards, perform ad hoc queries, and provide decision advice through predictive analytics. Due to these business demands for advanced analytics and a trend towards cost control, agility and self-service data access, many organizations are turning to data warehouses based on the cloud like Google BigQuery.
In this book, we provide a comprehensive tour of BigQuery, a low-cost, highly scalable, serverless enterprise data warehouse available on Google Cloud. Since there is no infrastructure to manage, businesses can focus on analyzing data to find meaningful information using familiar SQL.
Our goal with BigQuery was to create a data platform that delivers cutting-edge functionality, leverages the many great technologies now available in cloud environments, and supports proven data technologies that are still relevant today. For example, at the cutting edge of technology, Google's BigQuery is a serverless compute architecture that decouples compute and storage
This allows different levels of the architecture to function and evolve independently and gives data developers flexibility in design and deployment. Quite 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 interacts with traditional and modern systems, with a wide range of desired throughputs and latencies. And on a proven level, BigQuery supports ANSI standard SQL, columnar optimization, and federated queries, which are critical to the self-service ad hoc data mining required by many users.
Who is this book for?
This book is intended for data analysts, data engineers, and data scientists who want to use BigQuery to derive information from large data sets. Data analysts can interact with BigQuery through SQL and through dashboard tools such as 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 create machine learning models in BigQuery, run TensorFlow models on data in BigQuery, and delegate large-scale distributed operations to BigQuery from a Jupyter notebook.
Google BigQuery: The Definitive Guide book reviews
Everything I need to know about BQ seems to be covered in this great book, written in a concise manner. Just what I needed as a BQ newbie, but note the book goes way beyond beginner topics!
Exactly the kind of quality I expect from this author. I bought several books and was very impressed. The quality of the work and the examples are among the best
The best BigQuery query book, although it isn't exhaustive. It is written by the team that develops it on Google. In my opinion, the first chapter does not provide relevant information and we should start reading it from chapter 6, the chapter on architecture. So you can read any chapter properly.
Complete, detailed and written with good examples. Update on the new feature set. Compulsory reading.
As a regular BigQuery user this book has been fantastic. Lots of tips, ideas and tricks from the BigQuery experts themselves. I have read it from start to finish and now use it as a daily reference.