All news with #bigquery tag

🧭 Conversational Analytics in BigQuery is now generally available, enabling business and technical users to query data, run multi-step analyses, and produce visual reports using natural language directly where data resides. Built on Google’s Gemini models and BigQuery’s governed foundation, it offers inspectable answers, context citations, proactive disambiguation, and long-term memory. The feature integrates with Lakehouse sources, supports enterprise security and governance controls, and provides agentic workflows for scheduled monitoring and automated reports.

🔍 Curve partnered with Google Cloud to adopt BigQuery Graph, moving beyond relational joins to perform multi-hop network analysis across billions of connections. By modeling users and shared identifiers as a property graph, they can traverse massive datasets with GQL, combine graph traversals with standard SQL and ML workflows, and avoid costly data migrations. This integration has improved detection, operational efficiency, and enabled plans for real-time signals and visualization.

🧭 This post introduces the preview of BigQuery's new AI.AGG() function, which enables natural-language aggregation over millions of rows of unstructured and multimodal data directly inside SQL. It explains practical uses—analyzing logs, discovering product categories, and summarizing image collections—while showing how AI.AGG() batches inputs, handles NULLs, reports errors, and integrates with other BigQuery AI functions like AI.CLASSIFY(). The write-up outlines best practices for token usage, model endpoint selection, and struct handling to help users deploy AI.AGG() effectively.

📣 Google Cloud now lets you create alerts in Observability Analytics using SQL to query logs and traces. This preview feature runs scheduled SQL queries via BigQuery on telemetry linked datasets and supports row count and boolean conditions. When conditions are met, Cloud Monitoring opens incidents and notifies configured channels. Note that BigQuery execution costs apply under your billing model.

🛠️ Google Cloud announced enhancements to its Observability suite, rebranding Log Analytics as Observability Analytics and bringing trace data and the Observability API to general availability. The update unifies logs and traces, enables SQL queries across telemetry, and allows in-place analysis without duplicating data. Use cases include diagnosing AI agent tool failures and correlating latency with customer impact. Users can link observability buckets to BigQuery and run cross-dataset analytics directly in the Cloud console.

🐍 BigQuery now supports fully managed Python User-Defined Functions (UDFs) in GA, enabling data teams to run custom Python code securely inside BigQuery using SQL or BigQuery DataFrames. The service runs on BigQuery-managed serverless infrastructure that auto-scales and removes the need to manage containers. It provides access to popular Python libraries, vectorized PyArrow processing, configurable container resources, concurrency controls, and streaming logs for observability. Billing is integrated with BigQuery SKUs and supports spend commitments and cost monitoring.

🤖 Google announces expanded Agentic Data Cloud capabilities, introducing new data agents and tools to enable conversational analytics and agent-driven workflows across BigQuery, Lakehouse, AlloyDB, Spanner, and Cloud SQL. The update includes Data Engineering, Data Science, Database Observability, Looker Dashboard, Data Insights, and Deep Research agents, plus developer toolkits like the Data Agent Kit and Managed MCP servers. These features aim to ground agents in real-time enterprise data with unified governance and near-100% accuracy for tasks such as NL-to-SQL conversions and automated pipeline maintenance.

🔍 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.