BigQuery

In the modern age of data warehouses, BigQuery provides the highest level of scalability and efficiency. It follows the Software as a Service paradigm (SaaS) that allows users to process and analyze big data with modern skills like SQL.

The top-level container in BigQuery is a dataset. This container is used to control access to a group of tables and views that contain data and each dataset belongs to a project. They can be created in regions across the world as provided by Google Cloud. Tables and views can be created by a variety of sources, including other Google Cloud services, developer libraries for several languages, and manually from the user interface. Views give developers the ability to create a final data model based on a complicated query that can be used for data analytics and data science so non-technical users can use basic SQL to get the results they need.

On top of a fully managed, serverless data warehouse, BigQuery offers embedded machine learning and data analytics with BigQuery ML and BI Engine. BigQuery ML gives data engineers and scientists the power to create machine learning models with standard SQL queries and apply them on petabytes of data. BI Engine is an in-memory analytics service that integrates with Google Data Studio and gives analysts the ability to build interactive dashboards with the computing power and scalability of BigQuery. These services emphasize the power BigQuery gives to its users by just writing SQL.

Despite its extensive features, BigQuery has a simple user interface for creating, querying and maintaining a scalable data warehouse. It provides comprehensive support for technical users while still providing a user-friendly environment for non-technical users. It enables organizations to build the data models they need to gain valuable insights from their data.