BigQuery 101: A Beginner's Guide to Google's Cloud Data Warehouse

Google BigQuery is a powerful and widely used cloud-based analytics tool designed for data-driven enterprises. It was created in 2010 by a team of engineers at Google's Seattle office to leverage the company's advanced internal storage, computing, and analytics tools. Its key features include its scalable design, serverless architecture, and ability to quickly process large datasets using standard SQL. BigQuery is built on top of Dremel, a revolutionary internal Google tool that enables engineers and product managers to perform interactive ad-hoc analysis of read-only nested data on web-scale applications. It also relies on Colossus, a system optimized for reading large volumes of structured data using Capacitor – Google’s columnar storage format – and efficient compression. BigQuery's decoupled storage and computation architecture leverage column-based partitioning to minimize the amount of data read from disk by slot workers. It allows for unlimited data manipulation language (DML) statements, including insert, update, or delete a large number of rows in a table in a single job. BigQuery's storage pricing model is based on the amount of data stored and whether the storage is considered active or long-term. The platform also offers partitioning and clustering features to help improve query performance and make managing and analyzing data easier. Data can be ingested into BigQuery using various methods, including batch ingestion from Cloud Storage, streaming ingestion through tabledata.insertAll function or the BigQuery Storage Write API, and utilizing the Data Transfer Service for importing data from external sources. Querying data in BigQuery is compatible with standard SQL and legacy SQL, and it offers a range of functions and operators for tasks such as text manipulation, date/time calculations, mathematical operations, and JSON extraction. Additionally, BigQuery ML allows users to build and evaluate machine learning models within the platform without needing extensive programming or knowledge of ML frameworks.