BigQuery
Google Cloud's fully managed, serverless analytical data warehouse for querying and processing large datasets.
BigQuery lets teams analyze large marketing, product and finance datasets with SQL without managing servers, and it connects natively to Google Analytics 4 and Google Ads exports, which is useful for unified reporting.
An e-commerce company streams its GA4 export into BigQuery, joins it with order data from its ERP and schedules a daily query that builds a channel revenue table read by Looker Studio, with tables partitioned by date to limit scan costs.
On-demand pricing charges per byte scanned, so unfiltered queries on large tables can become expensive. It is built for analytics, not high-frequency transactional writes, and data modeling, governance and access control remain the team's job.
BigQuery separates storage from compute. Data is stored in a columnar format and queried with GoogleSQL by a distributed engine that allocates processing slots as needed. Partitioning and clustering reduce the data each query scans. It supports batch loads, streaming, external tables, BigQuery ML for in-database models and pricing either on demand per byte processed or by reserved capacity.
Systems Architecture
Google Cloud Documentation — BigQuery overview — https://docs.cloud.google.com/bigquery/docs/introduction; Google Cloud Documentation — Overview of BigQuery storage — https://docs.cloud.google.com/bigquery/docs/storage_overview