Google launched Continuous Queries for BigQuery

SQL Statements that can analyze, process, and perform Machine Learning

Photo by Yuheng Ouyang on Unsplash

Google launched Continuous queries that let you build long-lived, continuously processing SQL statements that can analyze, process, and perform machine learning (ML) inference on incoming data in BigQuery in real time[1].

Continuous queries in BigQuery enable real-time analysis of streaming data. They allow you to output processed results directly into a BigQuery table or export them to services like Pub/Sub or Bigtable. These queries can handle data written to standard BigQuery tables through various methods, including[1][2]:

• The BigQuery Storage Write API
• The tabledata.insertAll method
• Batch loading
• The INSERT DML statement

With continuous queries, you can perform time-critical operations such as generating immediate insights, triggering real-time machine learning (ML) predictions, and syncing data to external platforms. This capability effectively transforms BigQuery into an event-driven engine for powering application logic and decision-making.