All news with #bigquery tag

🔍 Storage Insights datasets now include activity insights that provide near-real-time visibility into object and bucket operations across your Google Cloud Storage estate. These BigQuery-linked views expose object-level writes, updates, deletes and errors, bucket-level aggregates and regional traffic patterns to support cost optimization and faster troubleshooting. The feature is generally available and customizable by org, folder, project, or specific buckets, enabling queries, Looker visualizations, and integration with other Storage Intelligence capabilities.

🔍 Alcidion migrated its Miya Precision platform from Microsoft SQL Server to Google Cloud's AlloyDB for PostgreSQL to improve stability, performance, and operational overhead. The move used Database Migration Service and custom synchronization tools to achieve a rapid cutover, reducing transition time to about 15 minutes. The new architecture enabled dramatic speedups in JSON processing and reduced administrative burden for SREs.

📈 BigQuery Graph enables building a digital twin of interconnected systems like supply chains and restaurant networks by creating a Graph View over existing tables. The approach maps items, recipes, locations and dependencies into nodes and edges, enabling targeted queries for issues such as recalls, weather disruptions, or procurement leaks. It emphasizes keeping relational data for metrics while using graphs for structure, cleaning keys, and capturing edge properties for richer modeling.

🛡️ The Remote Model Context Protocol (MCP) Server for AlloyDB is now generally available, providing a secure HTTP endpoint that lets AI agents access real-time operational data. This fully managed service simplifies production deployments by centralizing discovery, offering fine-grained IAM-based authorization, audit logging, and integration with Model Armor for prompt and response protection. Developers can join AlloyDB operational data with analytics in BigQuery and use built-in AI functions for low-latency agentic experiences.

📊 Connected Sheets removes CSV exports and turns Google Sheets into a live, secure interface to BigQuery, enabling business users to analyze petabytes of governed data without SQL. Admins retain security and governance by provisioning table or view access while preventing data alteration from Sheets. End users gain immediate agility using familiar tools like pivot tables, charts, and formulas to analyze billions of rows and create refreshable reports and hybrid models. Connecting requires a Google Workspace account and a billing-enabled Google Cloud project, with connections established either from Sheets or the BigQuery UI.

🔧 Data Agent Kit is an open-source toolkit from Google Cloud that brings data engineering and data science skills, plugins, and secure connectors directly into your IDE or CLI. It provides prebuilt agentic skills, Model Context Protocol (MCP) integrations to BigQuery, AlloyDB, and Cloud Storage, plus native extensions for VS Code, Gemini CLI, Claude Code, and Codex. By grounding agents in unified enterprise data, it reduces manual ETL and context-window costs and accelerates intent-driven pipelines; the kit is available in preview.

🔒 This article outlines five architectural patterns for exposing enterprise data to autonomous systems on Google Cloud, using BigQuery examples and mocked CRM data as pedagogical blueprints. It contrasts deterministic, developer-authored SQL APIs with agentic approaches that use LLMs, platform-native reasoning like the Conversational Analytics API, and the vendor-neutral Model Context Protocol (MCP). It highlights trade-offs in trust, complexity, cost, latency, and maintenance.

🚀 Google Cloud announced streaming AI enhancements to its Agentic Data Cloud at Next ‘26, unifying Pub/Sub, Dataflow, BigQuery, Bigtable and Managed Service for Kafka to deliver real-time context and low-latency inference. These additions include Pub/Sub AI inference, BigQuery continuous queries for stateful stream processing, Pub/Sub→Bigtable subscriptions, and unified embedding sinks for immediate semantic search and agent memory. The platform also supports MCP and ADK integrations so agents can manage resources and run inside Dataflow pipelines, reducing context lag for use cases like fraud detection and autonomous supply chain actions.

🔍 Google Cloud presents a SIGMOD paper introducing proxy models—cost‑optimized, ultra‑lightweight models that replace most LLM calls in AI-powered SQL functions. They rely on precomputed embeddings (using Gemini) and simple classifiers (currently logistic regression) to deliver orders‑of‑magnitude reductions in latency and token costs. BigQuery and AlloyDB implement this optimization with online training in BigQuery and PREPARE-based offline training in AlloyDB. The technique performs well for many semantic filters but can fail on tasks requiring complex reasoning or extreme selectivity.

🔒 A ShinyHunters campaign has compromised data for over 197,000 Zara customers, according to HaveIBeenPwned. Stolen items include unique email addresses, product SKUs, order IDs and support ticket data after stolen authentication tokens from analytics provider Anodot were used to access BigQuery and Snowflake instances; the group leaked a claimed 140GB trove. Inditex says no names, passwords or payment details were affected and operations remained unaffected. Other reported victims include Vimeo, Rockstar Games and McGraw Hill.

🚀 The BigQuery Studio notebook gallery is now generally available, providing a curated collection of pre-built templates that help teams skip setup and start analysis faster. The gallery supports SQL, Python, and Spark workflows and includes templates for generative AI, ML development, and data pipelines. Templates demonstrate best practices for BigQuery DataFrames, serverless Spark, and multimodal analysis. Users can preview templates in read-only mode and add copies directly into their projects from the BigQuery Studio console.

🔍 BigQuery Graph is now available in preview, offering an integrated, serverless graph analytics capability within BigQuery that scales to billions of nodes and edges. It provides an intuitive graph query experience using a GQL dialect aligned with the ISO GQL standard and full interoperability with SQL, removing the need to copy or move data. The preview includes vector and full-text search, notebook visualizations, and federation with Spanner Graph for combined real-time and historical analysis, aimed at use cases such as fraud detection, drug discovery, and supply-chain analysis.

🔗 Google Cloud introduces a unified graph solution that pairs Spanner Graph for operational (OLTP) workloads with BigQuery Graph for analytical (OLAP) queries, enabling developers and analysts to work against a consistent GQL schema without duplicating data. Both platforms support integrated table-to-graph mapping, mixed GQL/SQL queries, built-in vector and full-text search, and AI integrations to power real-time applications and large-scale historical analysis. The solution also offers cross-system workflows via Data Boost (query Spanner from BigQuery), reverse ETL exports to Spanner, and visualization integrations with partners like Kineviz and Linkurious to accelerate investigations and insights.

🔍 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.