All news with #vector database tag

BigQuery Agent Analytics: Stream and Analyze Agent Data

📊 Google introduces BigQuery Agent Analytics, an ADK plugin that streams agent interaction events into BigQuery to capture, analyze, and visualize performance, usage, and cost. The plugin provides a predefined schema and uses the BigQuery Storage Write API for low-latency, high-throughput streaming of requests, responses, and tool calls. Developers can filter and preprocess events (for example, redaction) and build dashboards in Looker Studio or Grafana while leveraging vector search and generative AI functions for deeper analysis.

Microsoft Databases and Fabric: Unified AI Data Estate

🧠 Microsoft details a broad expansion of its database portfolio and deeper integration with Microsoft Fabric to simplify data architectures and accelerate AI. Key launches include general availability of SQL Server 2025, GA of Azure DocumentDB (MongoDB-compatible), the preview of Azure HorizonDB, and Fabric-hosted SaaS databases for SQL and Cosmos DB. OneLake mirroring, Fabric IQ semantic modeling, expanded agent capabilities, and partner integrations (SAP, Salesforce, Databricks, Snowflake, dbt) are positioned to deliver zero-ETL analytics and operational AI at scale.

Using BigQuery ML to Solve Lookalike Audiences at Zeotap

🔍 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.

Amazon DocumentDB 8.0 Adds MongoDB 8.0 Compatibility

⚡ Amazon DocumentDB (with MongoDB compatibility) version 8.0 adds support for MongoDB API drivers 6.0, 7.0, and 8.0 while delivering up to 7x improved query latency and up to 5x better compression. The release introduces Planner Version3, new aggregation stages and operators, dictionary-based Zstandard compression, text index v2, and parallel vector index builds. Upgrades from 5.0 instance-based clusters are supported via AWS Database Migration Service, and DocumentDB 8.0 is available in all Regions where the service is offered.

AlloyDB AI: Auto Vector Embeddings and Indexing Capabilities

🔍 AlloyDB AI launches two preview features—Auto Vector Embeddings and Auto Vector Index—that let teams convert operational databases into AI-native stores using simple SQL. Auto Vector Embeddings generates and incrementally refreshes vectors in-database, batching calls to Vertex AI and running as a background process. The Auto Vector Index (ScaNN) self-configures, self-tunes, and maintains vector indexes to accelerate filtered semantic search and reduce ETL and tuning overhead for production workloads.

Agent Factory Recap: Securing AI Agents in Production

🛡️ This recap of the Agent Factory episode explains practical strategies for securing production AI agents, demonstrating attacks like prompt injection, invisible Unicode exploits, and vector DB context poisoning. It highlights Model Armor for pre- and post-inference filtering, sandboxed execution, network isolation, observability, and tool safeguards via the Agent Development Kit (ADK). The team demonstrates a secured DevOps assistant that blocks data-exfiltration attempts while preserving intended functionality and provides operational guidance on multi-agent authentication, least-privilege IAM, and compliance-ready logging.

Moloco and Google Cloud Power AI Vector Search in Retail

🔎 Moloco’s AI-native retail media platform, integrated with Vertex AI Vector Search on Google Cloud, delivers semantic, real-time ad retrieval and personalized recommendations. The joint architecture uses TPUs and GPUs for model training and scoring while vector search runs efficiently on CPUs, enabling outcomes-based bidding at scale. Internal benchmarks report ~10x capacity, up to ~25% lower p95 latency, and a ~4% revenue uplift. The managed service reduces operational overhead and accelerates time-to-value for retailers.

Scaling Customer Experience with AI on Google Cloud

🤖 LiveX AI outlines a Google Cloud blueprint to scale conversational customer experiences across chat, voice, and avatar interfaces. The post details how Cloud Run hosts elastic front-end microservices while GKE provides GPU-backed AI inference, and how AgentFlow orchestrates conversational state, knowledge retrieval, and human escalation. Reported customer outcomes include a >90% self-service rate for Wyze and a 3× conversion uplift for Pictory. The design emphasizes cost efficiency, sub-second latency, multilingual support, and secure integrations with platforms such as Stripe, Zendesk, and Salesforce.

Amazon ElastiCache Adds Vector Search with Valkey 8.2

🚀 Amazon ElastiCache now offers vector search generally available with Valkey 8.2, enabling indexing, searching, and updating billions of high-dimensional embeddings from providers such as Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI with microsecond latency and up to 99% recall. Key use cases include semantic caching for LLMs, multi-turn conversational agents, and RAG-enabled agentic systems to reduce latency and cost. Vector search runs on node-based clusters in all AWS Regions at no additional cost, and existing Valkey or Redis OSS clusters can be upgraded to Valkey 8.2 with no downtime.

Amazon CloudWatch Adds Generative AI Observability

🔍 Amazon CloudWatch is generally available with Generative AI Observability, providing end-to-end telemetry for AI applications and AgentCore-managed agents. It expands monitoring beyond model runtime to include Built-in Tools, Gateways, Memory, and Identity, surfacing latency, token usage, errors, and performance across components. The capability integrates with orchestration frameworks like LangChain, LangGraph, and Strands Agents, and works with existing CloudWatch features and pricing for underlying telemetry.

Amazon Neptune Analytics Launched in Two New Regions

🚀 Amazon has made Neptune Analytics available in the AWS Canada (Central) and Australia (Sydney) Regions, enabling local creation and management of analytics graphs. Neptune Analytics is a memory‑optimized graph engine that supports fast, in‑memory processing, a library of optimized analytic algorithms, low‑latency graph queries, and vector similarity search within traversals. You can ingest data from an Amazon Neptune Database, snapshots, or Amazon S3, and start via the AWS Console or CLI; consult the Neptune pricing page and AWS Region Table for costs and availability.

AI in Today's Cybersecurity: Detection, Hunting, Response

🤖 Artificial intelligence is reshaping how organizations detect, investigate, and respond to cyber threats. The article explains how AI reduces alert noise, prioritizes vulnerabilities, and supports behavioral analysis, UEBA, and NLP-driven phishing detection. It highlights Wazuh's integrations with models such as Claude 3.5, Llama 3, and ChatGPT to provide conversational insights, automated hunting, and contextual remediation guidance.

Amazon OpenSearch Service Adds Batch AI Inference Support

🧠 You can now run asynchronous batch AI inference inside Amazon OpenSearch Ingestion pipelines to enrich and ingest very large datasets for Amazon OpenSearch Service domains. The same AI connectors previously used for real-time calls to Amazon Bedrock, Amazon SageMaker, and third parties now support high-throughput, offline jobs. Batch inference is intended for offline enrichment scenarios—generating up to billions of vector embeddings—with improved performance and cost efficiency versus streaming inference. The feature is available in regions that support OpenSearch Ingestion on domains running 2.17+.

Amazon Neptune Analytics Launches in Mumbai Region

📍 Amazon Neptune Analytics is now available in the Asia Pacific (Mumbai) Region, enabling customers to create and manage analytics graphs locally. Neptune Analytics is a memory-optimized graph engine designed for fast, in-memory processing of large graph datasets, supporting optimized analytic algorithms, low-latency graph queries, and vector search within traversals. It complements Amazon Neptune Database, and you can load data from a Neptune Database, snapshots, or Amazon S3. To get started, create a new Neptune Analytics graph via the AWS Management Console or AWS CLI; see the Neptune pricing page for region and cost details.

Source-of-Truth Authorization for RAG Knowledge Bases

🔒 This post presents an architecture to enforce strong, source-of-truth authorization for Retrieval-Augmented Generation (RAG) knowledge bases using Amazon S3 Access Grants with Amazon Bedrock. It explains why vector DB metadata filtering is insufficient—permission changes can be delayed and complex identity memberships are hard to represent—and recommends validating permissions at the data source before returning chunks to an LLM. The blog includes a practical Python walkthrough for exchanging identity tokens, retrieving caller grant scopes, filtering returned chunks, and logging withheld items to reduce the risk of sensitive data leaking into LLM prompts.

Amazon OpenSearch Serverless Adds Disk-Optimized Vectors

🔍 Amazon has added disk-optimized vector storage to OpenSearch Serverless, offering a lower-cost alternative to memory-optimized vectors while maintaining equivalent accuracy and recall. The disk-optimized option may introduce slightly higher latency, so it is best suited for semantic search, recommendation systems, and other AI search scenarios that do not require sub-millisecond responses. As a fully managed service, OpenSearch Serverless continues to automatically scale compute capacity (measured in OCUs) to match workload demands.

Amazon OpenSearch Service adds OpenSearch 3.1 for vectors

🚀 Amazon OpenSearch Service now supports OpenSearch 3.1, bringing targeted improvements for vector-driven and traditional search workloads. The release bundles Lucene 10 for optimized vector field indexing, faster indexing times, reduced index sizes, sparse indexing, and vector quantization to lower memory usage. It also improves range query and high-cardinality aggregation latency and introduces a new Search Relevance Workbench for iterative quality testing. Additional vector search enhancements include Z-score normalization for more reliable hybrid search and memory-optimized Faiss support; OpenSearch 3.1 is available in all AWS Regions.

Target modernizes search with hybrid AlloyDB AI platform

🔍 Target rebuilt its on-site search to combine lexical keyword matching with semantic vector retrieval, using AlloyDB AI to power filtered vector queries at scale. The engineering team implemented a multi-index architecture and a multi-channel relevance framework so hybrid queries can apply native SQL filters alongside vector similarity. The overhaul produced measurable gains — ~20% improvement in product discovery relevance, halved "no results" occurrences, and large latency reductions — while consolidating the stack and accelerating development.

Amazon Neptune Integrates with Zep for Long-Term Memory

🧠 Amazon Web Services announced integration of Amazon Neptune with Zep, an open-source memory server for LLM applications, enabling persistent long-term memory and contextual history. Developers can use Neptune Database or Neptune Analytics as the graph store and Amazon OpenSearch as the text-search layer within Zep’s memory system. The integration enables graph-powered retrieval, multi-hop reasoning, and hybrid search across graph, vector, and keyword modalities, simplifying the creation of personalized, context-aware LLM agents.

What's New in Google Data Cloud: August Product Roundup

🔔 This Google Cloud roundup summarizes recent product milestones, GA launches, previews, and integrations across the data analytics, BI, and database portfolio. It highlights updates to BigQuery, Firestore, Cloud SQL, AlloyDB, and adjacent services aimed at easing ingestion, migration, and AI-driven operations. Notable items include MongoDB-compatible Firestore GA, PSC networking improvements for Database Migration Service, and a redesigned BigQuery data ingestion experience. The post also emphasizes resilience and DR enhancements such as immutable backups and Near Zero Downtime maintenance.