All news with #bigquery tag

Automating FinOps Governance with Workload Manager

🔧 Workload Manager automates FinOps governance by codifying cost-control policies and enforcing them across Google Cloud environments. It supports both predefined checks (for example, bigquery-missing-labels) and custom rules written in Open Policy Agent (OPA) Rego, allowing organization-, folder-, or project-level scans. Scheduled evaluations can export results to BigQuery, trigger notifications (email, Slack, PagerDuty), and feed Looker Studio dashboards for reporting and trend analysis. New pricing reduces scan costs by up to 95% and includes a small free tier to accelerate adoption.

BigQuery's Data Engineering Agent: Automating Pipelines

🔧 The preview of the Data Engineering Agent in BigQuery introduces a Gemini-powered assistant that automates pipeline development, maintenance, and migrations. The agent converts natural-language requirements into SQL, enforces engineering best practices, and supports custom instructions and UDFs to reflect organizational logic. Integrated with Dataplex, it uses governance metadata to improve table descriptions, data quality assertions, and PII-aware handling, and it also generates documentation and troubleshooting guidance. The feature is available in preview via BigQuery Pipelines and the Dataform UI.

Mercado Libre's Spanner-Based Platform for Scale and AI

🚀 Mercado Libre leverages Spanner as the core of a developer-facing platform, exposing consistent, globally-scalable transactions through its internal gateway, Fury. Fury abstracts distributed database complexity and serves both relational and key-value workloads. Integration with BigQuery via Data Boost and Change Streams enables near-real-time analytics and reverse ETL to operational systems.

Log Analytics Query Builder Makes Log SQL Easier for Teams

🔍 The Log Analytics query builder in Google Cloud Console provides a UI-driven way to build and preview SQL-based log queries without hand-coding. It helps DevOps engineers, SREs, and application developers search across fields, infer JSON schemas, select nested values, and apply aggregations via an intuitive interface. Real-time SQL preview and one-click visualizations let users switch to the editor to fine-tune queries and save dashboards.

Integrating Oracle with Google Cloud for AI Automation

🔁 This Google Cloud post explains how enterprises can integrate Oracle Database with cloud-native analytics and AI by moving transactional data into BigQuery. It recommends ingestion patterns such as low-latency Change Data Capture via Datastream, batch staging to Cloud Storage, and notes ODBC/JDBC for interactive queries but not continuous replication. Once data resides in BigQuery, organizations can leverage Gemini-powered features, BigQuery ML, and AI agents (via the Agent Developer Kit) for natural-language exploration, assisted coding, multimodal analysis, and automated workflows across retail and education use cases.

Agent Factory Recap: AI Agents for Data Engineering

🔍 The episode of The Agent Factory reviewed practical AI agents for data engineering and data science, highlighting demos that combine Gemini, BigQuery, Colab Enterprise, and Spanner-based graph queries. It showcased a BigQuery Data Engineering Agent that generates pipelines, time dimensions, and data-quality assertions from SQL, and a Data Science Agent that runs end-to-end anomaly detection in Colab. The post also covered CodeMender for autonomous code security fixes and a creative Spanner+ADK comic demo illustrating multi-region concepts.

Dataplex Supports Column-Level Lineage for BigQuery

🔍 Dataplex Universal Catalog now captures column-level lineage for BigQuery, extending object-level tracing to granular column transformations at no extra cost. The update provides interactive visual lineage graphs so users can inspect upstream and downstream flows for individual columns, trace origins, and assess downstream impact of modifications. This granularity helps validate authoritative sources for AI/ML features, enforce column-level governance, and improve compliance. It also surfaces freshness and usage metadata to support context-aware agents.

SmarterX Builds Custom LLMs with Google Cloud Tools

🔍 SmarterX uses Google Cloud to build custom LLMs that help retailers, manufacturers, and logistics companies manage regulatory compliance across product lifecycles. Using BigQuery, Cloud Storage, Gemini, and Vertex AI, the company ingests, normalizes, and indexes unstructured regulatory and product data, applies RAG and grounding, and trains customer-specific models. The integrated platform empowers subject matter experts to evaluate, correct, and deploy model updates without heavy engineering overhead.

Design Patterns for Scalable AI Agents on Google Cloud

🤖 This post explains how System Integrator partners can build, scale, and manage enterprise-grade AI agents using Google Cloud technologies like Agent Engine, the Agent Development Kit (ADK), and Gemini Enterprise. It summarizes architecture patterns including runtime, memory, the Model Context Protocol (MCP), and the Agent-to-Agent (A2A) protocol, and contrasts managed Agent Engine with self-hosted options such as Cloud Run or GKE. Customer examples from Deloitte and Quantiphi illustrate supply chain and sales automation benefits. The guidance highlights security, observability, persistent memory, and model tuning for enterprise readiness.

Oklahoma Transforms Data Access, Strengthens Employer Trust

🔍 The Oklahoma Employment Security Commission modernized its 40‑year mainframe data architecture with a cloud-first data platform built on BigQuery and analytics delivered via Looker. Partnering with Google Public Sector and Phase2, OESC reorganized opaque, mainframe-mimicking schemas into a performant, intuitive model and enabled point-in-time snapshots previously impossible. Critical reporting moved from months to hours, stakeholders gained self-service access, and the agency unlocked employer insights that supported tax analysis, improved auditability, and accelerated fraud detection.

BigQuery Studio updated with streamlined console UI

🔧 BigQuery Studio unveils a simplified, organized console interface designed to help data analysts, engineers, and scientists work more efficiently. The update introduces an expanded Explorer view for easier resource discovery, a context-aware Reference panel that surfaces table schemas and lets you insert query snippets, and a decluttered layout including a dedicated Job history tab. These changes reduce context switching and tab proliferation so users can focus on analysis.

Google Cloud Adds AI Annotations and Object Contexts

🧠 Google Cloud is introducing two Cloud Storage features—auto annotate and object contexts—that apply pretrained AI to generate metadata and attach custom key-value tags to stored objects. Auto annotate (experimental) produces image annotations such as object detection, labels, and objectionable-content signals tied to an object's lifecycle. Object contexts (preview) let teams add, manage, and query contextual tags with IAM controls and Storage Insights integration. Together they enable scalable discovery, curation, and governance of previously unanalyzed unstructured “dark data.”

BigQuery Data Clean Room Query Templates — Preview

🔒 BigQuery data clean room query templates are now available in preview, enabling clean room owners to publish fixed, reusable TVF-based queries that accept table or field inputs and return only aggregated rows. Templates reduce data exfiltration risk, simplify onboarding for non-SQL users, and enforce consistent analytical and privacy controls via aggregation thresholds and approval workflows. They support single-direction and multi-party collaboration while keeping query logic hidden from subscribers.

AI Forecasting and Conversational Analytics in BigQuery

🔎 Google added two BigQuery tools—ask_data_insights and BigQuery Forecast—to the MCP Toolbox and the Agent Development Kit (ADK) to enable conversational analytics and time-series predictions for agents. ask_data_insights uses the Conversational Analytics API to interpret plain-English questions, generate and run queries, and return summarised answers with a step‑by‑step log for transparency. BigQuery Forecast leverages BigQuery ML’s TimesFM model via AI.FORECAST so agents can run forecasting jobs directly inside BigQuery without separate ML infrastructure.

Google Cloud Customers: Monthly Innovations Roundup

🚀 This roundup highlights how leading organizations are using Google Cloud to optimize networks, accelerate AI, and scale mission-critical services. From Uber reducing edge latency with Hybrid NEGs to Target rebuilding search with AlloyDB AI hybrid search, customers report measurable gains in performance, cost, and reliability. Healthcare, finance, media, and telecommunications teams also describe operational wins — faster inference, seamless migrations, and stronger real-time experiences.

Gemini CLI Extensions Enable Google Data Cloud Access

🔧 Google released open-source Gemini CLI extensions that integrate Gemini with Google Data Cloud services, enabling terminal-based access to BigQuery, Cloud SQL, and AlloyDB. Developers install the CLI (recommended v0.6.0), add extensions, and configure IAM and environment variables to connect to projects. Extensions support provisioning databases and users, natural-language querying, AI forecasting, and conversational analytics, though some require enabling additional APIs.

Enabling Data Scientists to Become Agentic Architects

🧭 Google outlines an AI-native stack to transform data scientists into agentic architects, unifying development, real-time data access, and production-grade agent deployment. Enhancements to Colab Enterprise notebooks add native SQL cells, editable visualizations, and an interactive Data Science Agent that can orchestrate BigQuery ML, DataFrames, and Spark workflows. The Lightning Engine is now generally available to accelerate Spark, while previews for stateful BigQuery continuous queries and autonomous embedding generation bring real-time streaming and vector search into analytics. A 'Build-Deploy-Connect' toolkit, including the Agent Development Kit, MCP Toolbox, and Gemini CLI extensions, helps move notebook prototypes into secure, scalable agent fleets.

Seattle Children’s Uses AI to Accelerate Pediatric Care

🤖 Seattle Children’s partnered with Google Cloud to build Pathway Assistant, a multimodal AI chatbot that turns thousands of pediatric clinical pathway PDFs into conversational, searchable guidance. Using Vertex AI and Gemini, the assistant extracts JSON metadata, parses diagrams and flowcharts, and returns cited answers in seconds. The tool logs clinician feedback to BigQuery and stores source documents in Cloud Storage, enabling continuous improvement of documentation and metadata.

Google Cloud's Differentiated AI Stack Fuels Startups

🚀 Google Cloud highlights how its differentiated AI tech stack is accelerating startup innovation worldwide, with nine of the top ten AI labs, most AI unicorns, and more than 60% of generative AI startups using its platform. Startups are leveraging Vertex AI, TPUs, multimodal models like Veo 3 and Gemini, plus services such as AI Studio and GKE to build agents, generative media, medical tools, and developer platforms. Programs like the Google for Startups Cloud Program provide credits, mentorship, and engineering support to help founders scale.

BigQuery scalability and reliability upgrades for Gen AI

🚀 Google Cloud announced BigQuery performance and usability enhancements to accelerate generative AI inference. Improvements include >100x throughput for first-party text generation and >30x for embeddings, plus support for Vertex AI Provisioned Throughput and dynamic token batching to pack many rows per request. New reliability features—partial-failure mode, adaptive traffic control, and robust retries—prevent individual row failures from failing whole queries and simplify large-scale LLM workflows.