All news with #bigquery tag

Google Adds Official MCP Support Across Key Cloud Services

🔌 Google announced fully-managed, remote support for Anthropic's Model Context Protocol (MCP), enabling agents and standard MCP clients to access a unified, enterprise-ready endpoint for Google and Google Cloud services. The managed MCP servers integrate with services like Google Maps, BigQuery, GCE, and GKE to let agents perform geospatial queries, in-place analytics, and infrastructure operations. Built-in discovery, governance, IAM controls, audit logging, and Google Cloud Model Armor provide security and observability. Developers can expose and govern APIs via Apigee and the Cloud API Registry to create discoverable tools for agentic workflows.

VMO2 and Google Cloud: Data Contracts for Scalable AI

🔒 VMO2, with Google Cloud, implemented data contracts as machine-readable agreements to guarantee dataset quality, schema, semantics, and SLOs for individual assets like BigQuery tables and Cloud Storage buckets. Defined in YAML and managed via GitLab, contracts are validated and operationalized by Dataplex Universal Catalog, which provisions Data Quality Scan jobs and profiling. The platform uses Cloud Composer, Pub/Sub, and BigQuery to orchestrate scans, surface results, and provide dashboards for real-time observability.

Nutanix NC2 Now Generally Available on Google Cloud

🚀 Nutanix Cloud Clusters (NC2) is now generally available on Google Cloud, enabling organizations to run their Nutanix hybrid cloud directly on Google Compute Engine bare metal without refactoring workloads. NC2 supports the Z3 and C4 machine families with high-density NVMe local SSDs, integrates Nutanix Flow virtual networking, and maintains unified management via Prism Central. The solution connects to Google data and AI services like BigQuery and Vertex AI, supports license portability, and will be purchasable through Google Cloud Marketplace.

Back Market Migrates to Google Data Cloud, Cuts Costs

🔁 Back Market migrated its data and core tech stack from AWS-based Snowflake and Databricks to Google Cloud, consolidating all historical and operational data in BigQuery. The team executed a two-week proof of concept and a live double-run migration that kept production on Databricks while writing to cloned BigQuery tables until outputs matched. They replaced AWS DMS with Datastream, implemented hourly batching to control small-file costs, and completed critical switchover in six months. The move halved data processing times, cut CDC costs by 90%, reduced technical debt, and improved observability, governance, and developer productivity.

PubMed Data in BigQuery to Accelerate Medical Research

🔬 Google Cloud has made PubMed content available as a BigQuery public dataset with integrated vector search via Vertex AI, enabling semantic search across more than 35 million biomedical articles. Both BigQuery and Vertex AI Vector Search are FedRAMP High authorized, allowing organizations to run embedding models and VECTOR_SEARCH queries inside BigQuery. Early adopters like The Princess Máxima Center report literature reviews reduced from hours to minutes, and example SQL plus a demo repo are provided to help teams get started.

Automated Metadata Generation in Google Data Cloud

🧭 Google announces generally available automated metadata generation in the Google Data Cloud, using Dataplex Universal Catalog and Gemini to convert profiling and schema context into human-readable table and column descriptions. The capability integrates with BigQuery, stores generated descriptions for search and governance, and is accessible via an API. It aims to reduce "metadata debt," accelerate time-to-insight, and provide reliable grounding for AI agents, while still encouraging human review for key business definitions.

Building Conversational Genomics with Multi-Agent AI

🧬 Combining Google’s ADK, Gemini, and Cloud infrastructure, this work reframes variant interpretation as a conversational workflow that removes repetitive scripting and context switching. A two-phase design performs heavy VEP annotation once, stores versioned ADK artifacts and public BigQuery datasets, and enables sub-5-second interactive queries via a QueryAgent. Validation with an APOB spike-in demonstrated single-variant precision, compatibility across DeepVariant versions, and scalability to ~8.8M variants.

Google: Leader in 2025 Gartner Magic Quadrant for CDBMS

📈 Google announces it was named a Leader in the 2025 Gartner Magic Quadrant for Cloud Database Management Systems for the sixth consecutive year and positioned furthest in vision. The post presents the company's AI-native Data Cloud—a unified stack integrating BigQuery, Spanner, AlloyDB, Looker, and Dataplex—to support agentic AI. Google highlights embedded specialized agents, developer tooling (Data Agents API, ADK, Gemini CLI) and Agent Analytics in BigQuery to accelerate AI-driven applications while asserting cost and governance benefits on a single, open platform.

BigQuery AI: Unified ML, Generative AI, and Agents

🤖 BigQuery AI consolidates BigQuery’s built-in ML, generative AI functions, vector search, and agent tools into a unified platform. It enables users to apply generative models and embeddings directly via SQL, perform semantic vector search, and run end-to-end ML workflows without moving data. Role-specific data agents and assistive features like a data canvas and code completion accelerate work for engineers, data scientists, and business users.

BigQuery Agent Analytics: Stream and Analyze Agent Data

📊 Google introduces BigQuery Agent Analytics, an ADK plugin that streams agent interaction events into BigQuery to capture, analyze, and visualize performance, usage, and cost. The plugin provides a predefined schema and uses the BigQuery Storage Write API for low-latency, high-throughput streaming of requests, responses, and tool calls. Developers can filter and preprocess events (for example, redaction) and build dashboards in Looker Studio or Grafana while leveraging vector search and generative AI functions for deeper analysis.

BigQuery Data Transfer Service Enhancements and Compliance

🔔 The BigQuery Data Transfer Service expands its connector ecosystem with new GA integrations (Oracle, Salesforce, ServiceNow, SFMC, Facebook Ads, and GA4) and preview connectors like Stripe, PayPal, Snowflake, and Hive. Platform improvements include event-driven transfers, incremental ingestion, GAQL-based custom Google Ads reports, and enhanced Oracle scale. Security and compliance gains—EU Data Boundary GA, FedRAMP High, CJIS, access transparency, regional endpoints, and key usage tracking—support regulated workloads. A new consumption-based pricing model applies to third-party connectors once they reach GA.

BigLake Metastore Adds Iceberg REST Catalog Support

🔔 Google Cloud announced general availability of BigLake metastore support for the Iceberg REST Catalog, offering a serverless, standards-based runtime metastore that enables interoperability across Iceberg-compatible engines (Spark, Trino) and BigQuery. The service provides credential vending, integrated governance via Dataplex Universal Catalog for lineage and data quality, and a UX console for creating and managing Iceberg catalogs. By removing the need to run custom metastore deployments, BigLake metastore aims to reduce operational overhead while preserving enterprise scale and security.

TimesFM Integration Brings Forecasting to BigQuery

🕒 Google is integrating the TimesFM time-series foundation model into BigQuery and AlloyDB, enabling zero-shot forecasting on customer data without retraining. AI.FORECAST and AI.EVALUATE are now Generally Available in BigQuery, while AI.DETECT_ANOMALIES is in public preview. TimesFM 2.5 offers improved accuracy and lower latency, supports dynamic context windows up to 15K, and can return historical data with forecasts. AlloyDB preview lets users call TimesFM endpoints hosted on Vertex AI so operational data can be forecasted in-place, preserving data residency and reducing export overhead.

Using BigQuery ML to Solve Lookalike Audiences at Zeotap

🔍 Zeotap and Google Cloud describe a SQL-first approach to building scalable lookalike audiences entirely within BigQuery. They convert low-cardinality categorical features into one-hot and multi-hot vectors, use Jaccard similarity reframed via dot-product and Manhattan norms, and index vectors with BigQuery’s VECTOR_SEARCH. By combining pre-filtering on discriminative features and batching queries, the workflow reduces compute, latency, and cost while avoiding a separate vector database.

Advancing Text-to-SQL: Gemini's BIRD Benchmark Breakthrough

🚀 Google Cloud reports a new state-of-the-art Single Trained Model Track score on the BIRD benchmark, achieving 76.13 with a fine-tuned Gemini 2.5-pro. The team credits rigorous data filtering, multitask supervised fine-tuning, and test-time self-consistency selection for the gains. These improvements bolster NL2SQL features in AlloyDB AI and BigQuery, and enhance developer tooling such as Gemini Code Assist for reliable SQL generation.

BigQuery AI Functions: Reimagining SQL for the AI Era

🤖 BigQuery is introducing managed AI functions in public preview — AI.IF, AI.CLASSIFY, and AI.SCORE — that let analysts apply generative AI directly inside SQL queries. These functions enable semantic filtering and joins, label-based classification of text and images, and natural-language ranking, while BigQuery applies prompt, query-plan, and endpoint optimizations to reduce LLM calls and control cost. They complement existing Gemini inference functions and remove much of the need for complex prompt tuning or separate model selection, making AI-driven analytics more accessible within familiar SQL workflows.

BigQuery adds MATCH_RECOGNIZE for row-sequence SQL

🔍 BigQuery now supports MATCH_RECOGNIZE, a SQL clause for identifying ordered patterns across rows and time-series data. It lets analysts express complex sequence logic—using PARTITION BY, ORDER BY, PATTERN, DEFINE and MEASURES—inside a single query without heavy joins or external processing. The feature targets use cases like funnels, fraud detection, log sequencing, and financial pattern detection, and is immediately available to all BigQuery users.

How BigQuery Brought Vector Search to Analytics at Scale

🔍 In early 2024 Google introduced native vector search in BigQuery, embedding semantic search directly into the data warehouse to remove the need for separate vector databases. Users can create indexes with a simple CREATE VECTOR INDEX statement and run semantic queries via the VECTOR_SEARCH function or through Python integrations like LangChain. BigQuery provides serverless scaling, asynchronous index refreshes, model rebuilds with no downtime, partitioned indexes, and ScaNN-based TreeAH for improved price/performance, while retaining row- and column-level security and a pay-as-you-go pricing model.

Zeotap cuts costs 46% migrating to Bigtable from ScyllaDB

🚀 Zeotap migrated its Customer Data Platform from ScyllaDB to Bigtable to address scaling challenges, operational overhead, and highly spiky workloads. The cloud-native stack—using Dataflow, a home-grown streaming engine, Memorystore as a cache, Bigtable as the hot store, and BigQuery for analytics—delivers predictable low-latency reads and writes at scale. The transition yielded a 46% reduction in TCO and a ~20% drop in operational tasks while enabling sub-second SLAs and faster ML deployment.

Ericsson Secures Data Integrity with Dataplex Governance

🔒 Ericsson has implemented a global data governance framework using Dataplex Universal Catalog on Google Cloud to ensure data integrity, discoverability, and compliance across its Managed Services operation. The program standardized a business glossary, automated quality checks with incident-driven alerts, and visualized column-level lineage to support analytics, AI, and automation at scale. It balances defensive compliance with offensive innovation and embeds stewardship through Ericsson’s Data Operating Model.