We recently launched a bi-monthly BigQuery newsletter to give users a roundup of the latest announcements, innovations, code samples and learning resources. We’re always building something new, often based on your feedback, so how about a single place for you to find out what we’ve built to (maybe) make your life easier?
We’ve heard from many of you that Google Cloud data analytics need to simplify and work better together – that’s why we’ve unified data analytics capabilities into a single platform with BigQuery, along with built-in gen AI-powered assistance to save you time. Our goal is to provide you with learning resources, product updates, and more to help you make the most out of BigQuery. Checkout January 2025’s featured content.
Featured resource
Learn what’s new for the BigQuery unified platform including innovations, governance, streaming, and more in the Q4 Data Analytics Roadmap webinar, now on demand.
Price performance: improve query performance and simplify SQL
BigQuery’s new history-based query optimization learns from previously completed executions to help make queries run faster and/or consume fewer resources. To simplify SQL, a new Pipe syntax is in preview that serves as an extension to standard SQL syntax, which makes SQL more concise and more flexible.
Open source: BigQuery tables for Apache Iceberg
Now in preview, this is a fully managed, Apache Iceberg-compatible storage engine to give you choice and flexibility from BigQuery, with features such as autonomous storage optimizations, clustering, and high-throughput streaming ingestion.
Open source: run Apache Spark in BigQuery without moving data
BigQuery supports Apache Spark alongside SQL within a single unified workspace. Centralize source control and revision history, improve collaboration and maximize productivity with integrated CI/CD and new unified tooling.
Data governance: data and AI governance directly in BigQuery
New governance capabilities powered by Dataplex are directly available in BigQuery. From a unified data and AI catalog to lineage and quality, governance is built in and available where your data resides.
Real time & streaming: new capabilities with SQL and Flink
Run real-time ML inference and reverse extract-transform-load (ETL) with SQL directly in BigQuery with continuous queries. Prefer Flink? BigQuery Engine for Apache Flink is now in preview for Flink stream processing integrated into BigQuery, and works with Google Managed Service for Apache Kafka. Read the blog for all of the latest streaming innovations.
Security & resiliency: BigQuery managed disaster recovery
BigQuery cross-region managed disaster recovery provides managed failover and redundant compute capacity with a service-level agreement (SLA) for business-critical workloads.
AI innovations: connect your data to AI
BigQuery ML provides built-in capabilities to create and run ML models for your BigQuery data. BigQuery vector search is now generally available, setting the stage for a new class of AI-powered analytics. Check out the newest code samples for RAG with BQML Layout Parser and I/O Notebook Converted to Vertex. Accelerate DataFrames Pandas workloads at scale and save compute costs with new partial ordering mode in BigQuery DataFrames.
Productivity with gen AI: New Gemini in BigQuery features
Gemini in BigQuery boosts productivity with a preview of AI-assisted data preparation, providing a natural language interface to build data pipelines in BigQuery Studio. Data insights automates query generation based on the metadata of a table to help you uncover patterns and assess data quality.
Partner updates: New model capabilities with Gretel.ai and Claude
To help with training ML models, we announced support for synthetic data in BigQuery with a new partnership with Gretel.ai. This adds to third-party model capabilities, including the recent partnership with Claude in BigQuery.
Learn from your peers: Latest blogs by BigQuery customers
Box enhanced developer efficiency while tightening governance and security policies across all regions. Shopify used real-time ML to power semantic search to better understand customer intent. Virgin Media O2 saved 30 hours a week of engineering time with secure data sharing.
Featured Learning Path
Cloud Skills Boost: Integrate Generative AI Into Your Data Workflow. Deep dive into 5 different hands-on courses including: Gemini for Data Scientists and Analysts, Using BigQuery ML for Inference, Work with Gemini Models in BigQuery, Boost Productivity with Gemini in BigQuery, and Create ML Models with BigQuery ML. Happy learning!
We want to hear from you! Share your BigQuery tips with the Google Cloud Community, join our Google Cloud Innovators program, or share feedback on BigQuery with our engineering team. Hoping to see something that wasn’t included? Drop a comment below on what other content you want to see from us.