All news with #bigquery tag

🔒 Telus Digital has confirmed a cybersecurity incident after threat actors identifying as ShinyHunters claimed to have exfiltrated nearly 1 petabyte of data from the company's BPO systems over several months. The attackers say they used Google Cloud credentials found in the Salesloft/Drift breach to access a large BigQuery instance and then used trufflehog to locate additional secrets and pivot to other systems. Telus says it discovered unauthorized access to a limited number of systems, engaged forensic experts, is investigating what was stolen and which customers were affected, and reports no evidence of customer connectivity or service disruption.

🔒 Cybersecurity researchers disclosed nine cross-tenant vulnerabilities in Google Looker Studio that could have allowed attackers to execute arbitrary SQL and exfiltrate data across Google Cloud projects. Tenable has labeled the set of flaws LeakyLooker; there is no evidence of active exploitation and Google patched the issues after responsible disclosure in June 2025. Affected connectors include BigQuery, Spanner, Google Sheets, PostgreSQL, MySQL and many JDBC-based sources, and several bugs could retain stored credentials or enable one-click data exfiltration via crafted reports.

📡 Google Cloud and DigitalRoute are delivering reusable, cloud-native data pipelines that turn diverse telecom telemetry into AI-ready datasets. Running DigitalRoute’s Usage Engine Private Edition on GKE, the solution normalizes proprietary formats at edge and core, filters noise, and routes data into Spanner for real-time digital twins and BigQuery for large-scale analytics and training with Vertex AI. The result is consistent, contextualized subscriber traces that accelerate production-grade autonomous network use cases.

🚀 The preview of the Spanner columnar engine enables low-latency serving of Apache Iceberg lakehouse data with Spanner’s horizontal scale and strong consistency. It adds a columnar storage layer and vectorized execution to accelerate analytical scans — Google cites up to 200× faster scans — while isolating heavy analytical queries from transactional workloads. The feature supports on-demand columnar conversion, automatic query routing, and reverse ETL integrations with BigQuery, Databricks, Snowflake and Oracle to make curated analytical data available for real-time applications.

🚀 PayPal completed a multi-year, large-scale migration of more than 300 petabytes of analytics data into BigQuery on Google Cloud to create a unified data foundation for generative AI. The initiative consolidated disparate platforms — including Teradata, Hadoop, Redshift, and Snowflake — and reduced vendor complexity. PayPal automated migration tasks, used live dashboards, and integrated FinOps to maintain zero business downtime while enabling faster queries and much fresher data for AI model training.

🔁 BigQuery now provides autonomous embedding generation that automatically maintains an embedding column derived from a source column, eliminating manual embedding pipelines and synchronization burdens. The managed column is defined via familiar SQL (GENERATED ALWAYS AS AI.EMBED) and can be stored asynchronously. It integrates with VECTOR_SEARCH and a new AI.SEARCH function, and supports secure calls to Vertex AI models with connection-based permissions. The feature is available in preview.

💬 This Google Cloud post by David Tamaki Szajngarten demonstrates how to build a context-aware conversational agent for BigQuery using the Conversational Analytics API powered by Gemini. It provides a reference Python SDK flow to register BigQuery tables, create a DataAgent with system instructions and permitted datasources, and deploy stateful or stateless conversations. The article shows a streaming chat loop that returns generated SQL, DataFrame-like results, Vega‑Lite chart specs, and final natural-language answers, and highlights integration with the Agent Development Kit (ADK) and lifecycle controls.

🌐 BigQuery now supports global queries (preview), letting you run a single standard SQL statement across datasets stored in different geographic regions without building ETL pipelines. BigQuery orchestrates distributed execution by running region-specific operations, transferring partial results to a chosen execution location, and combining them to produce final results. The feature is disabled by default and requires administrator opt-in and explicit per-user permissions. It respects VPC Service Controls and gives you control over where queries execute to help meet compliance and data residency requirements.

🔍 Conversational Analytics in BigQuery (preview) brings an AI-powered reasoning agent into BigQuery Studio, enabling users to query, visualize, and forecast directly with natural language. The agent generates and executes SQL grounded in your schema, metadata, and verified queries, and it exposes the SQL and reasoning behind each answer to build trust. Security, governance, and audit logging are enforced by BigQuery’s compliance controls, and the feature also supports unstructured data and API integration for custom agents.

🚀 The BigQuery AI Hackathon concluded on September 22, 2025, with 5,350 entrants and 277 submissions demonstrating practical uses of BigQuery's generative AI, vector search, and multimodal tools. Winners such as OncOmix AI, SpeakAura AI, and TriLink showcased solutions for precision oncology, speech therapy, and automated ticket triage. BigQuery AI also added SQL-first capabilities including AI.IF with Preview sublinear scaling, AI.CLASSIFY, AI.SEARCH, AI.SCORE, and AI.EMBED to simplify embedding, search, classification, and multimodal workflows while improving performance.

🚀 In 2025, Spanner became a first-class database for AI and multi-model workloads, integrating vectors, graphs, and text search to provide richer context for generative applications. Google expanded AI integrations including ML.PREDICT, Vertex RAG Engine support, an Agent Development Kit, and ANN search with ScaNN to speed semantic retrieval. The release also bridged operational and analytical silos with a columnar engine and tighter BigQuery/Iceberg integrations, simplified Cassandra and MySQL migrations, boosted price-performance, and strengthened enterprise safeguards such as drop protection and default backup schedules.

🚀 Fastweb + Vodafone rebuilt its Customer 360 platform on Google Cloud to deliver unified, real-time customer insights using Spanner, BigQuery, and Gemini. The team replaced brittle, high-maintenance integrations with a connected platform that enables low-latency serving, centralized governance, and AI-assisted engineering workflows. Spanner’s native BigQuery integration sped migrations and simplified monitoring, while Apigee exposes consistent data to call centers, digital channels, and partners.

🚀Palo Alto Networks partnered with Google Cloud to replace a brittle single-tenant data pipeline model with a unified, multi-tenant Unified Data Platform powered by Dataflow, Pub/Sub and BigQuery. The migration consolidated more than 30,000 pipelines into a shared, autoscaling platform that processes billions of events daily. The change delivered roughly 30% compute cost savings, faster onboarding, and reduced operational overhead, enabling engineers to refocus on analytics and threat detection.

🚀 BigQuery now supports managed third‑party generative AI inference (Preview) for open models from Hugging Face and Vertex AI Model Garden, enabling SQL-native deployment and inference. With a single CREATE MODEL statement you can provision and configure compute, control lifecycle with endpoint_idle_ttl and ALTER MODEL, and run inference via AI.GENERATE_TEXT or AI.GENERATE_EMBEDDING. BigQuery automates resource cleanup and integrates cost controls to reduce operational overhead.

🔎 Comments to SQL in BigQuery introduces an AI-driven way to write queries by placing natural-language expressions inside SQL comments. The system analyzes surrounding SQL context and translates plain English prompts into executable BigQuery SQL across SELECT, FROM, WHERE, GROUP BY and other clauses. It supports iterative refinement and aims to help both non-SQL users and experienced analysts move faster.

🚀 The preview release of a fully managed, remote MCP server for BigQuery (Jan 2026) lets developers connect LLM-powered agents directly to analytics data via a standard HTTP endpoint without managing infrastructure. The blog demonstrates step‑by‑step integration with the Agent Development Kit (ADK) and the Gemini CLI, including OAuth client creation and Gemini API key setup, and loading a sample cymbal_pets dataset. It highlights compatibility with popular frameworks (ADK, LangGraph, Claude code, Cursor IDE) and reminds readers to follow AI security and production best practices.

📢Google Cloud’s Data Cloud updates through mid‑2025 introduce new self‑service Looker features, expanded Model Context Protocol (MCP) support, and tighter AI-to-data integrations. Highlights include AlloyDB AI time‑series forecasting via AI.FORECAST, GA of Conversational Analytics powered by Gemini, and the MCP Toolbox and ADK to securely connect agents to BigQuery, Spanner, Cloud SQL, and Looker. Dataplex Universal Catalog now previews curated data products for governed, deployable datasets and AI use.

🔹Google's Data Cloud presents an AI-native approach to unify siloed, multimodal enterprise data and enable real-time insights. The platform integrates Gemini with BigQuery to automate pipeline building, vector embedding, and data quality, while Dataplex provides continuous cataloging and contextual metadata for agents. Enhancements to managed streaming, Pub/Sub UDFs, and Dataflow extend real-time processing, governance, and developer productivity.

🔌 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, 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.