All news with #bigquery tag

🔍 BigQuery Graph and Kineviz GraphXR deliver an integrated workflow to extract, model, and visualize knowledge from unstructured enterprise data without separate graph databases or heavy ETL. By performing text extraction, Gemini-powered inference, and graph creation inside BigQuery, organizations preserve provenance and avoid data duplication. GraphXR connects live to the graph for low-code, interactive analysis, evidence-linked tracing, and dashboards that update as graph views evolve.

⚡ This post outlines an event-driven architecture that pairs BigQuery continuous queries with Pub/Sub Single Message Transforms and ADK-powered agents on Vertex AI Agent Engine to detect, route, and resolve anomalies in real time. Continuous queries push precise, filtered events into Pub/Sub where SMTs reshape payloads for agent webhooks. Deployed agents investigate autonomously, escalate complex cases, and log analytics back into BigQuery for observability.

📊 Google is reintroducing Data Studio (formerly Looker Studio) as the central home for Google Data Cloud assets, emphasizing unified access to reports, BigQuery conversational agents, and data apps built in Colab. The redesigned product will sit alongside Looker, targeted to personal, ad-hoc exploration while Looker remains the governed enterprise BI solution. A free edition continues to serve individuals and a Data Studio Pro tier offers AI, enterprise security, and management features; existing assets will be migrated transparently.

🔍 Prism is an open-source evaluation framework that helps teams run repeatable, measurable tests for Conversational Analytics agents in BigQuery and Looker. It enables developers to define test suites, assertions, and latency limits to validate generated SQL, returned data, and conversational behavior. Prism’s Trace View and Comparison Dashboard provide execution transparency and regression tracking so teams can identify failures and iterate with confidence.

🧊 Google announced preview read/write interoperability between BigQuery and Iceberg-compatible engines via the Google-managed Iceberg REST Catalog. The capability lets BigQuery, Trino, Spark, Flink and others create, update, and query a single Iceberg table type while enforcing unified governance and table-level access controls. Customers can offload compaction and garbage collection to BigLake to reduce small-file and metadata bloat and improve query performance.

🏠 Rightmove migrated from siloed on-premises databases to Google Cloud to build a unified analytics and AI platform it calls the data hive. Using BigQuery, Vertex AI, and Looker, the company extracts metadata from listings and images to deliver personalized search, agent-assist messaging, and an Automated Valuation Model. The hub-and-spoke architecture centralizes governance while enabling business units to run tailored forecasting and ML use cases. Around 300 staff now use the platform to convert data into operational and commercial value.

🚀 RVU accelerated its personalization platform by adopting Dataproc and Google Cloud Serverless for Apache Spark, using high-speed Spark processing for feature engineering across its consumer brands. The company reduced feature engineering and model development time from weeks to days, enabling faster experimentation and quicker contractor onboarding. This scalable, managed approach co-locates data in BigQuery, simplifies operations, and improves time-to-market for hyper-personalized campaigns.

🔍 Looker now provides self-service Explores that convert CSV, XLS/XLSX, or Google Sheets into instant, governed Explorations using a drag-and-drop or sheet import workflow. Uploaded files are securely persisted in your BigQuery instance and can be re-uploaded or refreshed for ongoing ad-hoc dashboards. Merge queries enable combining uploaded data with modeled Looker datasets (unlimited within the same BigQuery connection) to enrich official metrics, while conversational analytics supports natural-language querying. Admin controls and monitoring keep ad-hoc work distinct from core, governed models to maintain metric integrity.

🔍 Honeylove consolidated disparate analytics into BigQuery and integrated outputs with Gemini to automate reporting, contribution analysis, and SKU-level forecasting. They use BigQuery ML (ARIMA) for demand planning with forecasts consistently within 5% of manual calculations, and Gemini embeddings plus vector search to semantically analyze customer tickets. These automations have saved the team hundreds of hours annually and about 30 seconds per ticket, accelerating product iteration and operational efficiency.

⚙️ Google and partners are applying generative AI to streamline environmental reporting and infrastructure. Internally, Google used Gemini to auto-validate claims against policies and NotebookLM to convert static reports into an interactive, cited knowledge base. Equinix built a Sustainability Data Lake on BigQuery, ingesting data from 240+ sites to shift from manual spreadsheets to on-demand insights. The approach pairs serverless architecture and carbon-intelligent infrastructure to cut cost, compute waste, and reporting cycle time.

🚀 Manhattan Associates modernized its Manhattan Active SaaS platform by migrating from legacy Oracle and DB2 to Google Cloud databases. Cloud SQL and BigQuery now power core transactions and real-time analytics, enabling over a billion API calls per day with average responses under 150 ms. Containerized microservices on GKE, Pub/Sub streaming, and managed observability deliver automated failover, cross-region recovery, and faster feature delivery. The shift reduced manual scaling and licensing overhead while boosting operational agility and resilience.

🔍 The new Gemini-powered assistant in BigQuery Studio makes the agent context-aware by integrating active query tabs with the chat interface, eliminating copy-paste and context-switching. It generates advanced SQL, including AI operators and federated queries, to support more complex analyses from simple prompts. Built-in job analysis examines job history to diagnose long-running queries, failures, and cost drivers while respecting access permissions.

🔒 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.