Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week. And if you prefer it in your inbox, sign up here!
Oracle announced new agentic AI innovations for Oracle AI Database that will help users rapidly build, deploy, and scale secure agentic AI applications that are suitable for full-scale production workloads. Oracle AI Database architects agentic AI and data together across operational databases and analytic lakehouses. It enables AI agents to securely access real-time enterprise data wherever it resides and easily use business data with LLMs trained on public data to provide business insights. Users can choose AI models, agentic frameworks, open data formats, and deployment platforms. In addition, customers running on Oracle Exadata further benefit from Exadata Powered AI Search, which enables agentic AI at the highest scale with accelerated AI queries for high-volume, multi-step agentic workloads.
New capabilities that help eliminate the need to build and maintain data-movement pipelines include:
- Oracle Autonomous AI Vector Database, whichprovides the simplicity of a vector database with the full power of Oracle AI Database.
- Oracle AI Database Private Agent Factory, which enables business analysts and domain experts to rapidly build and safely deploy data-driven agents and workflows.
- Oracle Unified Memory Core, whichlets users store context for AI agents in a single system, enabling low-latency reasoning across vector, JSON, graph, relational, text, spatial, and columnar data in one converged engine, with consistent transactions and security.
New capabilities that help users safeguard data from external attacks, insider misuse, accidental disclosure, and unintended exposure to LLMs across multicloud, hybrid, and on-premises environments include:
- Oracle Deep Data Security, which implements powerful end-user-specific data access rules in the database.
- Oracle Private AI Services Container, which enables customers with stringent security requirements to run private instances of AI models while avoiding the sharing of data with third-party AI providers or sending data outside of their firewall.
- Oracle Trusted Answer Search, which provides enterprises with an accurate, testable, and deterministic way to use AI to provide answers to end-users instead of directly using an LLM to answer an end-user question.
Additionally, Oracle AI Database gives users the flexibility to choose the AI model and application-tier agentic framework that best fits their needs. They can build, deploy, and run agentic AI applications using open standards and data formats. New capabilities include:
- Oracle Vectors on Ice, which provides customers with native support for vector data that is stored in Apache Iceberg tables. AI Vector Search can read vector data directly from Iceberg tables, create vector indexes to accelerate vector search, and automatically update these indexes as the underlying vector data changes.
- Oracle Autonomous AI Database MCP Server, which enables external AI agents and MCP clients to securely access Autonomous AI Database and its capabilities without custom integration code or manual security administration.
In other company news, Oracle announced Fusion Agentic Applications, a new class of enterprise applications powered by coordinated teams of specialized AI agents that are outcome-driven, proactive, and reasoning-based, and engineered for enterprise execution. Built into Oracle Fusion Cloud Applications, Fusion Agentic Applications can make and execute decisions within business processes by securely accessing unified enterprise data, workflows, policies, approval hierarchies, permissions, and transactional context. Unlike copilots, AI assistants, or other AI add-ons, being native to the transactional system enables Fusion Agentic Applications to execute in real time, at enterprise scale, with full governance.
Real-time analytics news in brief
Aerospike unveiled LangGraph integration for its NoSQL Database 8, delivering persistent memory to stateless AI agentic workflows struggling to move from prototype to production scale. The integration of Aerospike Database with LangGraph lets developers build faster, more effective, and highly stable agentic AI agents and applications. Additionally, developers can persist both short-term execution context and longer-term agent memory without altering how graphs are defined or executed.
Akamai announced new AI-powered capabilities for Akamai Guardicore Segmentation designed to transform how organizations design and enforce security policies. The solution leverages AI to identify, analyze, and interpret application behavior, then automatically generates accurate, enforcement-ready policies. Organizations can now speed up segmentation efforts, implement stronger controls with confidence, and remain resilient against rapidly evolving, AI-powered threats.
AlphaSense announced new custom AI agent capabilities, automatically scheduled to deliver insights, that get users from question to answer more efficiently. AlphaSense delivers personalized outcomes rooted in premium and proprietary business content. AlphaSense’s latest capabilities with purpose-built, industry-specific agents address the growing need among enterprises for domain-specific AI that is both trustworthy and scalable.
Arm announced the next evolution of the Arm compute platform, extending into production silicon products for the first time in the company’s history. The first step in this change is the launch of the Arm AGI CPU, an Arm-designed CPU for AI data centers, built to address a rising class of agentic AI workloads. The company claims the solution will support greater workload density, improved accelerator utilization, and more usable compute within existing power envelopes, something that is critical as AI infrastructure scales.
BigID announced the expansion of its Data Access Governance (DAG) capabilities to cover AI agents. With this move, BigID extends the same data-centric governance model it applies to humans directly to agents. To that end, the solution supports agent identity discovery and mapping, access right-sizing for non-human identities, and real-time agent activity monitoring.
CData Software announced major enhancements to CData Sync designed to meet the data pipeline demands of modern enterprises. The updates deliver coordinated pipeline orchestration, expanded change data capture (CDC) for mission-critical systems, and native support for open table formats, empowering data teams to operate continuously across legacy and modern architectures.
Codenotary announced the availability of AgentX, an autonomous platform to manage, secure, and protect large-scale Linux infrastructure in the cloud or on-premises through coordinated networks of AI agents. AgentX introduces a new approach to infrastructure operations by allowing distributed AI agents to collaborate, automating security enforcement, operational tasks, and lifecycle management while maintaining full permissions control and governance for administrators.
Databricks announced Lakewatch, a new open, agentic SIEM (Security Information and Event Management) designed to help organizations defend against increasingly sophisticated agent attackers. Lakewatch enables organizations to unify all their data in open formats so they can analyze years of data cost-effectively without moving or duplicating it. This includes multi-modal data like video and audio to identify social engineering, insider threats, and anomaly detection.
Datadog announced that Bits AI Security Analyst is available to customers everywhere as part of Datadog’s Cloud SIEM. The solution pairs the expertise of a senior SOC analyst with machine scale and speed, enabling investigation analysis across a breadth and volume of data sources, while still delivering high-accuracy verdicts backed by real-world context. This allows analysts to scale their investigation expertise so they can focus more time on high-impact defense priorities.
DataBahn.ai announced Autonomous In-Stream Data Intelligence (AIDI), a new operating model for security data pipelines, where data is continuously interpreted, validated, and acted on in real time as it flows. Such capabilities allow in-stream decision-making, enabling organizations to detect issues earlier, adapt dynamically, and ensure data is trusted before it reaches downstream systems.
In other company news, DataBahn introduced the DataBahn Agent Farm, a coordinated system of specialized AI agents that operationalize AIDI across the data lifecycle, continuously building, validating, optimizing, and protecting data in-stream.
Domo made a number of agentic AI announcements at its annual Domopalooza conference. To start, the company announced a new AI orchestration framework that helps businesses operationalize artificial intelligence. The new capabilities include AI Agent Builder, AI Toolkits, a centralized AI Library, and the Domo MCP Server, which connects enterprise data directly to external AI platforms. Together, these tools help organizations build and deploy custom AI agents deeply integrated with enterprise data and workflows.
EnterpriseDB (EDB) touted the community release of CloudNativePG 1.29, the latest milestone for the leading open-source Postgres operator for Kubernetes. With version 1.29, CNPG introduces modular extensions and built-in supply chain security, decoupling PostgreSQL extensions from the core database to eliminate monolithic, custom-built images and significantly reduce operational complexity. Users can now dynamically install only the extensions they need, such as AI-driven vector search, on demand, enabling a more flexible, lightweight, and secure Postgres deployment model.
ExtraHop announced a comprehensive approach to de-risking AI innovation by providing enterprises with the definitive visibility and oversight required to manage their AI and agentic infrastructure. To that end, ExtraHop ensures operational integrity for the agentic enterprise by delivering deep insights that transform the network into a source of truth for AI observability, threat investigation and response, and governance.
Fivetran announced it is donating SQLMesh, its open source data transformation framework, to the Linux Foundation. Initial members Benzinga, CloudKitchens, Harness, Infinite Lambda, Jump AI, and Minerva will join the project to support a community-governed approach to developing and maintaining SQLMesh as part of the modern data stack.
Immuta introduced a data provisioning platform for managing agentic data access. The launch includes new Agentic Data Access capabilities that enable enterprises to provision and govern AI agent access to enterprise data in real time. With Agentic Data Access, Immuta treats AI agents as first-class identities, with their own attributes, intent, temporary access, and audit trail, so they can act on behalf of users without authenticating as them, standing privileges, or ticket-driven delays.
Operant AI announced the launch of Agent ScopeGuard, a new capability for Operant’s Agent Protector that detects and blocks AI agents from acting outside their intended operational scope in real-time, at GPU-accelerated speed, before damage is done. The solution defines, monitors, and enforces the operational boundary of every agent at runtime, ensuring agents operate within their authorized scope and are stopped when they don’t.
Relyance AI announced the commercial availability of Lyo, an autonomous data defense engineer designed to monitor and secure how AI agents interact with enterprise data. The solution is powered by Relyance’s AI Data Journeys and continuously monitors and attaches business and behavioral context to data activity across code, cloud infrastructure, MCP servers, SaaS applications, identities, third parties, and AI agents.
Reveal announced the ability for companies to deliver conversational AI analytics directly inside their products with enterprise-level controls. To that point, Reveal embeds conversational analytics and AI-driven insights directly inside enterprise products, rather than adding AI as a separate assistant or external tool. That enables users to easily explore data, understand what is happening in their business, and decide what to do next.
Revenium announced the launch of AI Outcomes, a new solution linking every AI agent execution to its business outcome and calculating ROI at the workflow level. Where previously released Revenium tools answered the question of what AI spent across machine and human workflows, AI Outcomes answers whether that spend produced results. Teams now have the instrument they need to break through the AI ROI wall.
Spur Intelligence announced new enhancements to its IP intelligence platform, designed to give security and fraud teams deeper visibility into anonymized infrastructure and the ability to make informed decisions on risky user sessions in real time. New capabilities include AI service identification, a new policy API for real-time user session decisions, and enhanced geographic infrastructure insights.
Vectra AI announced a major advancement to the Vectra AI Platform, delivering exposure management built for AI enterprises. The new capabilities enable organizations to proactively identify, prioritize, and reduce exploitable security gaps, maintain compliance, and prevent future attacks across the modern AI-augmented enterprise. The expanded capabilities unify asset visibility, behavior-driven risk prioritization, and AI usage insights into a single platform.
Partnerships, collaborations, and more
Denodo announced that it is joining the Open Semantic Interchange (OSI), an open source initiative that creates a universal specification for all companies to standardize their fragmented data definitions with an open, vendor-neutral semantic model specification. Through OSI, Denodo will enable interoperability between its semantic layer, which delivers trusted business context and live access to operational data across hybrid, multi-cloud, and sovereign environments, and other semantic technologies, helping customers drive measurable business outcomes.
MariaDB plc announced the completion of its acquisition of GridGain Systems, the force behind Apache Ignite. By integrating GridGain’s in-memory technology, MariaDB now provides a single, high-velocity, persistent grounding layer that supports the entire AI lifecycle, from real-time data ingestion to complex reasoning.
Nasuni announced an official partnership and integration between the Nasuni File Data Platform and Oracle Cloud Infrastructure (OCI). With this integration, customers can run Nasuni on OCI to unify the storage, protection, and management of unstructured data across hybrid and distributed environments. Built on Nasuni’s patented UniFS global file system, the platform replaces data silos with a single namespace that supports unlimited scale, high-performance global collaboration, and built-in cyber resilience.
Percona announced a new partnership with Chainguard to deliver supremely secure open-source software with enterprise-grade support. With the partnership, Percona now supports Chainguard builds across its database software portfolio, giving organizations a path to secure, production-ready container images of open-source databases with expert support behind them.
Red Hat announced that it is contributing llm-d, the Kubernetes-native high-performance distributed LLM inference framework, to the Cloud Native Computing Foundation (CNCF) as a Sandbox project. The company noted that this isn’t just a hand-off of code. It’s a commitment to making high-performance AI serving as a core, portable capability of the cloud-native stack. By moving llm-d into the CNCF, we’re expanding the target of a multi-vendor coalition, including CoreWeave, IBM, Google, and NVIDIA, to build the open standard for distributed inference.
Stelia AI announced a collaboration with Nokia to advance the deployment of high-performance, high-trust AI at enterprise scale. By integrating Nokia’s leading open-standards-based networking technology into its AI ecosystem, Stelia is enabling reliable performance across distributed systems built for the most demanding business use cases.
If your company has real-time analytics news, send your announcements to ssalamone@rtinsights.com.
In case you missed it, here are our most recent weekly real-time analytics news roundups:
Best Practices for Deploying and Scaling Industrial AIArtificial Intelligence (AI) is transforming industrial operations, helping organizations optimize workflows, reduce downtime, and enhance productivity. Different industry verticals leverage AI in unique ways.Link to The Center for Adaptive Edge Intelligence
The Center for Adaptive Edge IntelligenceAdaptive edge intelligence brings real-time decision-making to the point of data creation, whether from sensors, machines, or cameras.Link to The Value of Vehicle Electrification
The Value of Vehicle ElectrificationElectric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges.Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT
Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoTDigital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.Link to Smart Manufacturing for Automotive
Smart Manufacturing for AutomotiveLeading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.Link to Center for Data Pipeline Automation
Center for Data Pipeline AutomationAs businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential.Link to Center for Automated Integration
Center for Automated IntegrationThe goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more.Link to Continuous Intelligence: Insights
Continuous Intelligence: InsightsDigital transformation requires continuous intelligence (CI). Today’s digital businesses are leveraging this new category of software which includes real-time analytics and insights from a single, cloud-native platform across multiple use cases to speed decision-making, and drive world-class customer experiences.
