Google BigQuery

Mountain View, CA, USA
2010
  |  By Bernard Chang
Learn about the challenges of natural language to SQL (NL2SQL), and how to implement it on Google Cloud with BigQuery and Gemini.
  |  By Jeff Nelson
Learn how BigQuery and Document AI Layout Parser can help with document preprocessing when building retrieval-augmented generation (RAG) pipelines.
  |  By Jiaxun Wu
In this tutorial, learn how to use Gretel to generate synthetic data with BigQuery DataFrames, a Python client for BigQuery.
  |  By Tim Bezold
BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.
  |  By Candice Chen
Support for pipe syntax in GoogleSQL brings the elegance of piped data flow to SQL queries in BigQuery and Cloud Logging.
  |  By Peter Freiling
BigQuery history-based optimizations learn from similar queries to identify and apply additional improvements to the query execution.
  |  By Christopher Crosbie
Bigtable provides the query latency that real-time systems need when using data from BigQuery via the EXPORT DATA to Bigtable (reverse ETL) function.
  |  By Anoop Johnson
The BigQuery tables for Apache Iceberg storage engine offers autonomous storage optimizations, clustering, and high-throughput streaming ingestion.
  |  By Rodrigo Vale
Learn about an architecture built on BigQuery Omni that reduces the overall cost of log analytics by 10x as compared to a traditional approach.
  |  By Jagan R. Athreya
BigQuery external datasets for Spanner let you browse, explore and query Spanner tables as if they were native BigQuery tables.
  |  By Google BigQuery
Writing complex SQL queries can be challenging, but BigQuery's new pipe syntax offers a more intuitive way to structure your code. Learn how pipe syntax simplifies both exploratory analysis and complex log analytics tasks, helping you gain insights faster. Watch along and discover how to leverage pipe syntax in BigQuery for a more efficient analytics experience. Chapters: Speaker: Jeff Nelson Products Mentioned: Cloud - Data Analytics - BigQuery.
  |  By Google BigQuery
Learn how to transform your raw BigQuery data into captivating visualizations and actionable insights. This video demonstrates how to seamlessly connect and visualize your BigQuery data using Looker’s user-friendly interface and powerful semantic modeling capabilities. Watch along and discover how Looker and BigQuery empower business users with self-service analytics today.
  |  By Google BigQuery
Want to learn more about how you can use Gemini with BigQuery? In this video, Chloe Condon (Developer Relations Engineer for Google Cloud) helps break things down with the help of Gemini. You'll learn how BigQuery can be combined with AI tools to make your developer workflow more efficient.
  |  By Google BigQuery
Integrating Anthropic's Claude models into BigQuery opens new doors for advanced analytics and insights. From security enhancements to content localization, the possibilities are vast. Learn how in this quick tutorial. Speaker: Billy Jacobson Products Mentioned: Cloud - Data Analytics - BigQuery.
  |  By Google BigQuery
Tired of wrestling with SQL? BigQuery lets you ask questions in plain English & get instant answers. Watch how! #shorts #BigQueryTips #DataMadeEasy.
  |  By Google BigQuery
Unlock the power of your audio data! This video demonstrates how BigQuery's integration with Cloud Speech-to-Text lets you transform audio files – think customer calls, interviews, feedback – into structured text transcripts. Analyze this data at scale within your data warehouse, fueling smarter decisions and deeper customer understanding.
  |  By Google BigQuery
Turn your documents into actionable data! This video explores the power of BigQuery's integration with Document AI. Learn how to transform unstructured documents – think invoices, contracts, forms – into neatly structured tables within your data warehouse, unlocking smarter insights using familiar SQL syntax.
  |  By Google BigQuery
Discover the power of semantic search! With BigQuery's vector search capabilities, you can analyze unstructured data like text, images, and videos based on their underlying meaning. Explore how machine learning transforms your data into numerical representations called embeddings, making it possible to find connections that traditional keyword searches often miss.
  |  By Google BigQuery
Experience the magic of Large Language Models (LLMs) like Gemini, applied to your BigQuery data at scale! BigQuery's Vertex AI integration empowers you to use the Gemini models to analyze unstructured, semi-structured, and structured data - using just SQL.
  |  By Google BigQuery
Datastream is Google Cloud's change data capture (CDC) service for streaming ingestion and replication. In this video, we show how to use Datastream to easily set up replication from a MySQL database to BigQuery.

BigQuery is Google's serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator.

Analyze all your data by creating a logical data warehouse over managed, columnar storage, as well as data from object storage and spreadsheets. Build and operationalize machine learning solutions with simple SQL. Easily and securely share insights within your organization and beyond as datasets, queries, spreadsheets, and reports. BigQuery allows organizations to capture and analyze data in real time using its powerful streaming ingestion capability so that your insights are always current, and it’s free for up to 1 TB of data analyzed each month and 10 GB of data stored.