Systems | Development | Analytics | API | Testing

In this tutorial, learn how to use Gretel to generate synthetic data with BigQuery DataFrames, a Python client for BigQuery.

BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.

Support for pipe syntax in GoogleSQL brings the elegance of piped data flow to SQL queries in BigQuery and Cloud Logging.

BigQuery history-based optimizations learn from similar queries to identify and apply additional improvements to the query execution.

The BigQuery tables for Apache Iceberg storage engine offers autonomous storage optimizations, clustering, and high-throughput streaming ingestion.

Bigtable provides the query latency that real-time systems need when using data from BigQuery via the EXPORT DATA to Bigtable (reverse ETL) function.

Learn about an architecture built on BigQuery Omni that reduces the overall cost of log analytics by 10x as compared to a traditional approach.

BigQuery external datasets for Spanner let you browse, explore and query Spanner tables as if they were native BigQuery tables.

A partnership between Google Cloud and Gretel lets data engineers generate synthetic data within BigQuery.

BigQuery’s pipe syntax introduces an intuitive, top-down syntax for understanding data transformations, and is used in Cloud Logging Log Analytics.