All news with #bigquery tag

🕒 Google is integrating the TimesFM time-series foundation model into BigQuery and AlloyDB, enabling zero-shot forecasting on customer data without retraining. AI.FORECAST and AI.EVALUATE are now Generally Available in BigQuery, while AI.DETECT_ANOMALIES is in public preview. TimesFM 2.5 offers improved accuracy and lower latency, supports dynamic context windows up to 15K, and can return historical data with forecasts. AlloyDB preview lets users call TimesFM endpoints hosted on Vertex AI so operational data can be forecasted in-place, preserving data residency and reducing export overhead.

🔍 Zeotap and Google Cloud describe a SQL-first approach to building scalable lookalike audiences entirely within BigQuery. They convert low-cardinality categorical features into one-hot and multi-hot vectors, use Jaccard similarity reframed via dot-product and Manhattan norms, and index vectors with BigQuery’s VECTOR_SEARCH. By combining pre-filtering on discriminative features and batching queries, the workflow reduces compute, latency, and cost while avoiding a separate vector database.

🤖 BigQuery is introducing managed AI functions in public preview — AI.IF, AI.CLASSIFY, and AI.SCORE — that let analysts apply generative AI directly inside SQL queries. These functions enable semantic filtering and joins, label-based classification of text and images, and natural-language ranking, while BigQuery applies prompt, query-plan, and endpoint optimizations to reduce LLM calls and control cost. They complement existing Gemini inference functions and remove much of the need for complex prompt tuning or separate model selection, making AI-driven analytics more accessible within familiar SQL workflows.

🔍 BigQuery now supports MATCH_RECOGNIZE, a SQL clause for identifying ordered patterns across rows and time-series data. It lets analysts express complex sequence logic—using PARTITION BY, ORDER BY, PATTERN, DEFINE and MEASURES—inside a single query without heavy joins or external processing. The feature targets use cases like funnels, fraud detection, log sequencing, and financial pattern detection, and is immediately available to all BigQuery users.

🔍 In early 2024 Google introduced native vector search in BigQuery, embedding semantic search directly into the data warehouse to remove the need for separate vector databases. Users can create indexes with a simple CREATE VECTOR INDEX statement and run semantic queries via the VECTOR_SEARCH function or through Python integrations like LangChain. BigQuery provides serverless scaling, asynchronous index refreshes, model rebuilds with no downtime, partitioned indexes, and ScaNN-based TreeAH for improved price/performance, while retaining row- and column-level security and a pay-as-you-go pricing model.

🚀 Zeotap migrated its Customer Data Platform from ScyllaDB to Bigtable to address scaling challenges, operational overhead, and highly spiky workloads. The cloud-native stack—using Dataflow, a home-grown streaming engine, Memorystore as a cache, Bigtable as the hot store, and BigQuery for analytics—delivers predictable low-latency reads and writes at scale. The transition yielded a 46% reduction in TCO and a ~20% drop in operational tasks while enabling sub-second SLAs and faster ML deployment.

🔧 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.

🔧 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 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.

🔁 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.

🔍 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 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 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.

🔍 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 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 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 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.

🧭 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.

🚀 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.

🧭 Google Cloud has expanded the Data Science Agent in Colab Enterprise notebooks to support BigQuery ML, BigQuery DataFrames and Spark, enabling large-scale data transformation, model training, and inference directly on BigQuery or via Serverless for Apache Spark. The agent can now auto-retrieve BigQuery table metadata and lets you add tables via an @ mention from your current project to provide prompt context. To invoke frameworks, include keywords such as BigQuery ML, BigFrames, or PySpark; sample prompts are provided to guide forecasting, supervised learning, and dimensionality reduction workflows. Notable limitations: generated PySpark targets Spark 4.0 and @ mentions only search the current project; BigQuery improvements are available now in BigQuery notebooks and coming soon to Vertex AI.