Google BigQuery

Mountain View, CA, USA
2010
  |  By Giulia Carella
Using BigQuery DataFrames with CARTO visualization tools can help Python users access cloud-native analytics and generative AI services.
  |  By Wissem Khilfi
Learn to a knowledge base with an AI chatbot frontend to your BigQuery or unstructured Cloud Storage data using RAG and BigQuery ML.
  |  By Shobhit Singh
Learn how to use BigQuery DataFrames together with LLMs to generate synthetic data right inside of BigQuery.
  |  By Karl Weinmeister
How to use Gemini generative AI models through BigQuery to boost productivity in Google productivity tools like Sheets.
  |  By Nivedita Kumari
The BigQuery ML.GENERATE_TEXT function lets you use LLMs from Vertex AI within your SQL queries to analyze text in a BigQuery table.
  |  By Lanre Ogunmola
BigQuery now integrates with Sensitive Data Protection with native SQL functions that allow interoperable deterministic encryption and decryption.
  |  By Jing Mao
BigQuery numeric search indexes enable optimized lookups on INT64 and TIMESTAMP data types.
  |  By Luis Velasco
A look at how to augment LLM results with retrieval augemented generation (RAG) and vector search, directly from BigQuery.
  |  By Adam Paternostro
With integration to Vertex AI, you can use multimodal LLMs like Gemini Pro 1.0 and Gemini Vision Pro 1.0 in BigQuery to create marketing campaigns.
  |  By Bennett Crumbling
Optable’s end-to-end data clean room platform for the advertising industry integrates with BigQuery to enable audience activation and insights.
  |  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.
  |  By Google BigQuery
Data practitioners spend much of their time on complex, fragmented and sometimes, repetitive tasks. This limits their ability to focus on strategic insights and maximize the value of their data. Gemini in BigQuery shifts this paradigm by providing AI capabilities that help streamline your workflows across the entire data lifecycle.
  |  By Google BigQuery
Reimagine your data analysis experience with the AI-powered BigQuery data canvas. This natural language centric tool simplifies the process of finding, querying, and visualizing your data. Its intuitive features help you discover data assets quickly, generate SQL queries, automatically visualize results, and seamlessly collaborate with others – all within a unified interface.
  |  By Google BigQuery
Data and AI Cloud for Marketing: Your marketing, multiplied by Google Cloud Speakers: Jiby Varghese.
  |  By Google BigQuery
Duet AI can easily create SQL or Python code to do things like customer segmentation in BigQuery Studio.
  |  By Google BigQuery
If you’re working with large amounts of data, and looking for guidance on how to build a data warehouse in Google Cloud using BigQuery- this new Jump Start Solution is for you! In this video, we’ll walk you through the Jump Start Solution that combines BigQuery as your data warehouse and Looker Studio as a dashboard and visualization tool.

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.