FICO has introduced a digital hub designed to connect organizations with data and analytics providers. This innovative new Marketplace offers easy access to data, artificial intelligence (AI) models, optimization tools, decision rulesets, and machine learning models, which deliver enterprise business outcomes from AI. With FICO Marketplace, FICO® Platform users can fast-track their journey to becoming an intelligent enterprise, because they will be able to: Unlock Value from Data Faster: by experimenting with new data sources and decision assets to determine predictive power and business value. Users can expect to cut the time required to access, validate and integrate new data sources by half. Leverage Decision Agents Across Multiple Use Cases, Improving Collaboration: with its open API architecture, it allows for any decision asset, data service, analytics model, software agent or third-party solution to address a wide range of use cases including customer management, fraud, originations, and marketing. The reusability of decision agents across multiple departments breaks down silos and improves collaboration. Drive Better Customer Experiences: by enabling a holistic view of each individual customer, as well as building innovative new intelligent solutions and analytic capabilities that come from industry collaboration. “FICO Marketplace will facilitate the type of collaboration across the industry that drives the next generation of intelligent solutions,” said Nikhil Behl, president, Software, FICO.
TensorStax’s data engineering AI agents can design and deploy data pipelines through structured and predictable orchestration using a deterministic control layer that sits between the LLM and the data stack
Startup TensorStax is building AI agents that can perform tasks on behalf of users with minimal intervention to the challenge of data engineering. The startup gets around this by creating a purpose-built abstraction layer to ensure its AI agents can design, build and deploy data pipelines with a high degree of reliability. Its proprietary LLM Compiler acts as a deterministic control layer that sits between the LLM and the data stack to facilitate structured and predictable orchestration across complex data systems. Among other things, it does the job of validating syntax, normalizing tool interfaces and resolving dependencies ahead of time. This helps to boost the success rates of its AI agents from 40% to 50% to as high as 90% in a variety of data engineering tasks, citing internal testing. The result is far fewer broken data pipelines, giving teams the confidence to offload various complicated engineering tasks to AI agents. TensorStax says its AI agents can help to mitigate the operational complexities involved in data engineering, freeing up engineers to focus on more complex and creative tasks, such as modeling business logic, designing scalable architectures and enhancing data quality. By integrating directly within each customer’s existing data stack, TensorStax makes it possible to introduce AI agent data engineers into the mix without disrupting workflows or rebuilding their data infrastructure. These agents are designed to work with dozens of common data engineering tools. The best thing is that TensorStax AI agents respond to simple commands. Constellation Research Inc. analyst Michael Ni said TensorStax appears to be architecturally different to others, with its LLM compiler, its integration with existing tools and its no-customer-data-touch approach.
Kong’s platform enables enterprises to securely manage both their APIs and Apache Kafka-powered real-time data streams by regulating the workloads interaction though encrypting the records and subjecting applications to authentication
Kong introduced Kong Event Gateway, a new tool for managing real-time data streams powered by Apache Kafka. According to the company, customers can now use Konnect to manage both their APIs and Kafka-powered data streams. That removes the need to use two separate sets of management tools, which can ease day-to-day maintenance tasks. Kafka makes it possible to create data streams called topics that connect to an application, detect when the application generates a new record and collect the record. Other workloads can subscribe to a topic to receive the records it collects. Kong Event Gateway acts as an intermediary between an application and the Kafka data streams to which it subscribes. Before data reaches the application, it goes through the Kong Event Gateway. The fact that information is routed through the tool allows it to regulate how workloads access the information. Using Kong Event Gateway, a company can require that applications perform authentication before accessing a Kafka data stream. The tool encrypts the records that are sent over the data stream to prevent unauthorized access. According to Kong, it doubles as an observability tool that enables administrators to monitor how workloads interact with the information transmitted by Kafka. Kafka transmits data using a custom network protocol. According to Kong, Kong Event Gateway allows applications to access data via standard HTTPS APIs instead of the custom protocol. That eases development by sparing the need for software teams to familiarize themselves with Kafka’s information streaming mechanism. Kong Event Gateway allows multiple workloads to share the same data stream without the need for copies. Administrators can create separate data access permissions for each workload. Another feature, Virtual Clusters, allows multiple software teams to share the same Kafka cluster without gaining access to one another’s data.
Alation helps data teams turn messy, raw data into trusted, reusable data products for AI
Alation has launched its Data Products Builder Agent, an AI-powered tool that helps data teams turn messy, raw data into trusted, reusable data products. It removes the busywork of data teams, enabling them to deliver the data products that business users and AI need. The Data Products Builder Agent transforms raw data into productized, AI-ready assets that are easy to find and use in the Alation Data Products Marketplace. By automating the data product lifecycle, the Data Products Agent streamlines curation, packaging, and publishing processes. Based on user prompting, the agent identifies the right data to answer the user’s business question. It then auto-generates and documents the data product design specification and ensures data products meet marketplace and governance standards, all while keeping a human in the loop. This enables data teams to focus on strategic work while empowering the business with trusted, ready-to-use data products. The Alation Data Product definitions build on the Open Data Products Specification (ODPS), a YAML-based standard that enables open, portable, and extensible metadata for data products. Key capabilities of the Alation Data Products Builder Agent include: Effortless data product creation; Built-in trust; and Business-aligned relevance.
Archive360’s cloud-native archiving platform provides governed data for AI and analytics by simplifying the process of connecting to and ingesting data from any enterprise application and offering full access controls
Archive360 has released the first modern archive platform that provides governed data for AI and analytics. The Archive360 Platform enables enterprises and government agencies to unlock the full potential of their archival assets with extensive data governance, security and compliance capabilities, and primed for intelligent insights. The Archive360 Modern Archiving Platform enables organizations to control how AI and analytics consume information from the archive, and to simplify the process of connecting to and ingesting data from any application, so organizations can start realizing value faster. The capability reduces the risk AI can pose to organizations by inadvertently exposing regulated data, company trade secrets, or simply ingesting faulty and irrelevant data. The Archive360 AI & Data Governance Platform is deployed as a cloud-native, class-based architecture. It provides each customer with a dedicated SaaS environment to enable them to completely segregate data and retain administrative access, entitlements, and the ability to integrate into their security protocols. It allows organizations to: Shift from application-centric to data-centric archiving; Protect, classify and retire enterprise data; and AI Activation.
Qlik launches Open Lakehouse offering 2.5x–5x faster query performance and up to 50% lower infrastructure costs, while maintaining full compatibility with the most widely used analytics and machine learning engines
Qlik announced the launch of Qlik Open Lakehouse, a fully managed Apache Iceberg solution built into Qlik Talend Cloud. Designed for enterprises under pressure to scale faster and spend less, Qlik Open Lakehouse delivers real-time ingestion, automated optimization, and multi-engine interoperability — without vendor lock-in or operational overhead. Qlik Open Lakehouse offers a new path: a fully managed lakehouse architecture powered by Apache Iceberg that delivers 2.5x–5x faster query performance and up to 50% lower infrastructure costs, while maintaining full compatibility with the most widely used analytics and machine learning engines. Qlik Open Lakehouse combines real-time ingestion, intelligent optimization, and true ecosystem interoperability in a single, fully managed platform: Real-time ingestion at enterprise scale; Intelligent Iceberg optimization, fully automated; Open by design, interoperable by default; Your compute, your cloud, your rules; One platform, end to end. As AI workloads demand faster access to broader, fresher datasets, open formats like Apache Iceberg are becoming the new foundation. Qlik Open Lakehouse responds to this shift by making it effortless to build and manage Iceberg-based architectures — without the need for custom code or pipeline babysitting. It also runs within the customer’s own AWS environment, ensuring data privacy, cost control, and full operational visibility.
Elastic’s new plugin to accelerate open-source vector search index build times and queries on Nvidia GPUs; integrates with Nvidia validated designs to enable on-premises AI agents
Elastic announced that Elasticsearch integrates with the new NVIDIA Enterprise AI Factory validated design to provide a recommended vector database for enterprises to build and deploy their own on-premises AI factories. Elastic will use NVIDIA cuVS to create a new Elasticsearch plugin that will accelerate vector search index build times and queries. NVIDIA Enterprise AI Factory validated designs enable Elastic customers to unlock faster, more relevant insights from their data. Elasticsearch is used throughout the industry for vector search and AI applications, with a thriving open source community. Elastic’s investment to accelerate vector search on GPUs builds upon previous longstanding efforts to optimize its vector database performance through hardware-accelerated CPU SIMD instructions, new vector data compression innovations like Better Binary Quantization and making Filtered HNSW faster. With Elasticsearch and the NVIDIA Enterprise AI Factory reference design, enterprises can unlock deeper insights and deliver more relevant, real-time information to AI agents and generative AI applications.
Starburst Data’s lakehouse model supports AI models by using data where it already lives without needing to copy it into a centralized repository and without requiring external data pipelines
Starburst Data is unveiling a suite of enhancements intended to make it easier for enterprises to develop and apply artificial intelligence models. Starburst’s updates are focused on enabling what it calls an AI “lakeside,” in which companies can use data where it already lives without needing to copy it into a centralized repository. Starburst defines a lakeside as a staging ground for AI, or an area adjacent to the data lakehouse where data is the most complete, cost-efficient and governed. The company’s new Lakeside AI architecture combines AI-ready tools with an open data lakehouse model. It allows companies to experiment with, train and deploy AI systems while keeping sensitive or regulated data in place. Starburst AI Workflows accelerates AI application development by making it easier to transform unstructured data into vector embeddings, a machine learning technique that turns data into numerical representations that capture the meaning and relationships between different data points without requiring explicit keywords. Workflows manage prompts and models with SQL and enforce governance policies. Starburst said these capabilities are fully contained within its platform and require no external data pipelines. Data is stored on Apache Iceberg tables with connectors available for a variety of third-party vector databases. Basically, this means users can build AI features that rely on unstructured or semi-structured sources like emails, documents and logs without having to move data or stitch together multiple tools. The Starburst AI Agent is a built-in natural language interface that allows users to talk to their data using natural language. It automatically scans for sensitive data such as names, email addresses and other personally identifiable information at the column level and tags it so access policies can be applied. That reduces the need for manual checks and helps organizations enforce privacy rules more consistently. A new Starburst data catalog replaces the aging Hive metastore and provides better support for the Iceberg data format that is rapidly becoming the standard for cloud data lakes. The new catalog supports both legacy Hive data and Iceberg tables. To improve performance across large-scale deployments, Starburst is also introducing a native ODBC Driver that improves connection speed and reliability with business intelligence tools such as Salesforce Inc.’s Tableau and Microsoft Corp.’s Power BI.
Ataccama enhances data lineage with audit-ready snapshots, historical tracking, and cloud-native processing to strengthen data trust
Ataccama has released Ataccama ONE data trust platform v16.1. This new version introduces powerful data lineage and connectivity capabilities, including enhanced diagram export for audit and compliance use cases and improved lineage visualization tools. It also expands pushdown processing for cloud platforms, such as Azure Synapse and Google BigQuery. With these updates, Ataccama helps organizations more easily operationalize automated lineage, govern data across complex environments, and deliver trusted insights at scale. The Ataccama ONE data trust platform closes the data trust gap by giving organizations a comprehensive and portable view of how data moves, transforms, and impacts downstream systems. New capabilities make it easier to manage lineage across environments, including exporting diagrams for audits, preserving historical lineage states, and migrating metadata to support governance workflows and system changes. Teams can go beyond static data views to track sensitive information, audit its handling, and build confidence with point-in-time documentation. Expanded pushdown processing allows organizations to analyze data directly within cloud platforms like Azure Synapse and BigQuery, reducing movement, improving performance, and maintaining governance at scale. These updates enable teams to act faster, meet regulatory requirements, and confidently deliver trusted insights. New capabilities in v16.1: Automated lineage and audit snapshots; Enhanced visibility and collaboration; Cloud-native data processing; Support for big data workloads; Enhanced connectivity and flexibility.
Salesforce’s acquisition of Informatica to elevate its data management capabilities including catalog, integration, governance, quality, privacy and Master Data Management (MDM) for deploying agentic AI
Salesforce is making a big bid to become a much larger player in the enterprise space, announcing an $8B acquisition of Informatica. The move will bring together two large, established enterprise software providers with decades of real-world experience. By acquiring Informatica, Salesforce aims to enhance its trusted data foundation for deploying agentic AI. The combination will create a unified architecture enabling AI agents to operate safely, responsibly and at scale across enterprises by integrating: Informatica’s rich data catalog, integration, governance, quality, privacy and Master Data Management (MDM) capabilities; Salesforce’s platform includes Data Cloud, Agentforce, Tableau, MuleSoft and Customer 360. According to Forrester Analyst Noel Yuhanna, Salesforce’s acquisition of Informatica fills a gap in its data management capabilities. “The acquisition markedly elevates Salesforce’s position across all critical dimensions of modern data management, including data integration, ingestion, pipelines, master data management (MDM), metadata management, transformation, preparation, quality and governance in the cloud,” Yuhanna told VentureBeat. “These capabilities are no longer optional—they are foundational for building an AI-ready enterprise, especially as the industry accelerates toward agentic AI.” To fully realize AI’s promise, Yuhanna said that vendor solutions must tightly integrate data and AI as two sides of the same coin. In his view, this acquisition strengthens Salesforce’s ability to do just that, laying the groundwork for next-generation data that can power intelligent, autonomous and personalized experiences at scale to support AI use cases. Yuhanna sees the acquisition as a major advancement for Salesforce customers. He noted that Salesforce customers will be able to seamlessly access and leverage all types of customer data, whether housed within Salesforce or external systems, all in real time. It represents a unified customer data fabric that can deliver actionable insights across every channel and touchpoint. “Critically, it accelerates Salesforce’s ability to deploy agentic AI, enabling low-code, low-maintenance AI solutions that reduce complexity and dramatically shorten time to value,” Yuhanna said. “With a fully integrated data management foundation, Salesforce customers can expect faster, more innovative, and more personalized customer experiences at scale.” The opportunity is equally appealing for Informatica customers. In Yuhanna’s view, this acquisition unlocks a faster path to agentic AI workloads, backed by the reach and power of the Salesforce ecosystem. As data management evolves, intelligent agents will automate core functions, turning traditionally time-consuming processes like data ingestion, integration, and pipeline orchestration into self-operating data workflows. Tasks that once took days or weeks will be executed with zero to little human intervention. “With a unified data, AI, and analytics platform, Informatica customers will benefit from accelerated innovation, greater operational agility, and significantly enhanced returns on their data investments,” he said.