Today, we’re thrilled to introduce VMware Tanzu Data Intelligence, a unified data platform designed to help enterprises seamlessly access, govern, and activate all their data, regardless of where that data lives. Tanzu Data Intelligence can provide this foundation, integrating data lake and warehousing engines, streaming pipelines, high-concurrency query engines, and intelligent caching into one cohesive system, fully managed and optimized for modern workloads.
Unlocking the value of “big data” was a hot topic a decade ago, especially as IoT data collection reached mainstream adoption. But IT leaders at that time faced a complicated decision tree: What do you do with all that data (beyond analytical dashboards and corporate slide decks)? How do you accomplish that goal? And are the benefits ultimately worth the cost of a data architecture overhaul?
Today, things look different. The recent explosion of generative and agentic AI creates countless possibilities for enterprises to extract significant value from their data. However, one thing hasn’t changed. At the root of a successful AI strategy must be a thoughtful data strategy.
Realizing a return on investment in AI will depend in part on how easily and cost efficiently organizations can access and activate their data. This brings fresh urgency to revisiting data strategy, including both technology stacks and operational models as data teams, AI teams, and application development teams must collaborate in new ways.
As enterprises invest heavily in new digital initiatives, the ability to harness their proprietary data is fast-becoming the key differentiator in realizing business value from AI and next-generation applications. IT leaders and architects are re-examining how and where data is being moved, processed, stored, and queried. To deliver on the promise of AI with safety, speed, and efficiency, organizations need a data platform that spans structured and unstructured data, supports both historical and real-time data processing, can run on premises to meet cost and sovereignty requirements, and can scale across AI, analytics, and operational use cases. Delivering on this promise is the intention behind Tanzu Data Intelligence. With this new offering, we’re helping security-minded enterprises take advantage of this paradigm shift to crack open new possibilities with their data.
Top five industry shifts that make a data intelligence a must-have
We’ve seen five industry shifts for which traditional data platforms are no longer sufficient.
1. Cloud native thinking has shifted to AI-native thinking.
The conversation has moved from where systems run to what they enable. Enterprise leaders are shifting focus on how to use AI to improve decisions, automate operations, and deliver new customer experiences. In becoming AI native, the focus shifts to sustainable outcomes: getting faster insights, building intelligent applications, and winning competitive advantages driven by AI with maximum cost-efficiencies.
2. Data explosion is accelerating in variety, velocity, and volume.
Data is arriving faster, in more formats, and from more sources than ever before, including biometric devices, smartphones, sensors, and machine-generated logs. This growth isn’t just in volume, but in the diversity and immediacy of the data. Businesses must be able to ingest, process, and act on this data as it arrives—whether it’s a real-time stream or a batch from a distributed system. And it must be carried out in the most cost-efficient way possible to be financially sustainable, unhampered by additional egress fees, for example. Modern data platforms must meet these challenges because AI and smart applications depend on fast, diverse, and high-quality data to deliver the business outcomes they’re designed to achieve.
3. Modern applications require real-time, high-concurrency data access.
Smart applications and agentic systems that act autonomously or in coordination with users depend on immediate access to fresh data. These workloads often involve thousands or even millions of simultaneous queries, updates, and decisions happening in real time. Supporting this level of concurrency requires infrastructure that’s purpose-built for low latency, parallel execution, and high throughput. Real-time performance is now a fundamental requirement for use cases like recommendation systems, fraud detection, and digital assistants, rather than a specialized feature.
4. Natural language interfaces are reinforcing the value of SQL and democratizing data.
The rise of natural language interfaces doesn’t replace SQL but rather elevates it. As large language models (LLMs) interpret user intent and generate queries, SQL remains the ideal language for precise, declarative data access. SQL databases, widely adopted in enterprises for managing most structured data, offer a declarative, standardized architecture that simplifies locating, filtering, and combining data. The structure, maturity, and widespread adoption of SQL make it perfectly suited for translating natural language into actionable logic across diverse data systems. This shift makes data more accessible to non-technical users while reinforcing SQL’s role as the backbone of enterprise analytics. Rather than becoming obsolete, SQL is becoming more powerful, serving as the critical bridge between human intention and machine execution in AI-driven systems.
5. Postgres is becoming ubiquitous as an interface to data
Postgres is the common denominator for general purpose data workloads as well as innovation in the data ecosystem. The open license nature encourages innovation, both across the OSS community and within the enterprise–and this in turn reduces risk and promotes trust. From everyday workloads to scale out architectures, Postgres is the interface being used to access data.
Introducing Tanzu Data Intelligence capabilities
The five industry shifts outlined above make it clear that traditional data architectures are no longer sufficient. Organizations now need a unified platform that can handle the scale, speed, and complexity of modern data and AI workloads. Tanzu Data Intelligence was created specifically to address these new requirements. It is built to bring together large-scale analytics, real-time data processing, and AI-ready services into a single platform. Designed for operational efficiency and broad usability, it helps teams build applications that act on data in real time, support intelligent decision making, deliver the cost efficiencies and sovereignty of running data on premises, and scale with evolving business needs.
Tanzu Data Intelligence is an AI-ready data platform providing end-to-end data preparation and orchestration for data-hungry AI applications. Designed and optimized for private clouds, Tanzu Data Intelligence can provide the data layers and capabilities necessary for intelligent or agentic applications to access the right data, no matter where it lives. AI agents, applications, and AI models can seamlessly leverage data across various systems with federated query capabilities in Tanzu Data Intelligence, driving automation, advanced analytics, and intelligent decision making.
VMware Tanzu Data Intelligence solution diagram
A unified, enterprise-grade, AI-ready data platform
At the core of the Tanzu Data Intelligence platform is a massively parallel, enterprise-grade data lakehouse architecture, purpose-built to handle diverse, large-scale workloads with performance, flexibility, and governance. The data lakehouse can provide scalable storage and computation for unstructured and semi-structured data, making it useful for AI and ML use cases. It also supports raw data staging, model training, and large-scale processing of logs, documents, and other file-based sources, forming the foundation for downstream analytics and AI workflows that can empower agentic applications.
The data lakehouse architecture is the evolution of a mature and battle-tested data warehousing solution, now integrated with a data lake for scalable storage of raw data staging, large-scale file processing, and model training.
The data lakehouse can benefit organizations focused on AI-native transformation by improving and unifying access to diverse data across environments, whether structured, unstructured, native, or federated, scaling effortlessly in volume across data, users, and APIs. In addition, the data lakehouse tracks full data lineage to support sovereignty and governance, making it easier to identify the data that informed the AI’s decision, which in turn improves observability and explainability. Tanzu Data Intelligence helps enable enterprise-grade governance, encryption, RBAC, and policy enforcement to help organizations keep sensitive data protected and meet industry regulations like HIPAA, PCI, and GDPR. As one platform for multiple use cases—such as decision support, model training and tuning, and data science—it also offers native vector search at scale, enabling SQL queries and semantic similarity search across vectorized data in a single, powerful environment.
Surrounding this core are specialized components that support the full lifecycle of modern data and AI applications.
Ingestion and workflow orchestration
Seamlessly move and transform data with stream and batch pipelines, supporting real-time data flows and event-driven architectures with Tanzu Data Intelligence. A messaging queue enables reliable, high-throughput streaming data ingestion for microservices, change data capture, and real-time AI event triggering. A visual, low-code data flow tool enables drag-and-drop creation of event-driven and continuous data pipelines for processing, connecting systems and orchestrating live data flows without custom development. On-demand batch processing tools run as containerized jobs—provisioned, scaled, and torn down automatically for efficient resource use in scheduled ETL, model training, and compute-heavy workloads.
Federated query services
Intelligent query layers in Tanzu Data Intelligence can provide instant, unified access across varied data sources and eliminate the need for moving data to perform queries, reducing cost and complexity. This abstraction can also be utilized to enable high concurrency, workload optimization, and AI-readiness without centralizing all data. The data lakehouse includes a Platform Extension Framework (PXF), which enables federated queries across object stores, data lakes, cloud filesystems, and external databases. Users can query distributed data using standard SQL without data movement, allowing for faster insights and reduced pipeline complexity.
Container compute services
When Tanzu Data Intelligence is combined with VMware Tanzu Platform, data science and AI teams can gain access to container and compute services. Tanzu Platform is an easy-to-use, AI-ready, pre-engineered application platform as a service (PaaS) that can be used by data and AI teams to move and process data, such as ETL processing. With Tanzu Platform, container and compute resources are elastically scaled up or down to meet the variable needs of dynamic AI, analytics, and app workloads. The platform offers self-service access to compliant environments with no need for internal tickets, so data architects and data scientists can test their innovations on enterprise-grade, performant production environments.
Real-time data services
To support agentic applications that demand immediate data access and processing, the Tanzu Data Intelligence platform can provide robust real-time data services. This includes an in-memory data grid to enable real-time responsiveness and high concurrency for agentic applications and AI-powered services. These features make it possible for enterprises to deliver real-time data experiences, supporting applications that require instantaneous insights and responses, such as fraud detection, personalized customer interactions, and operational monitoring.
Advanced analytics and AI/ML
Embedded support for predictive models, vector processing, and agentic workflows can empower developers and data scientists to build AI-powered applications faster. This opens up new and innovative use cases, such as unifying traditional SQL queries with semantic search, allowing users to perform similarity searches on vectorized data directly within the platform. By enabling efficient comparisons of high-dimensional data, this capability can be particularly beneficial for applications requiring contextual understanding, such as recommendation engines, natural language processing, and image recognition, all at enterprise scale.
Together, these layers make Tanzu Data Intelligence a powerful platform for modern enterprises without needing to rearchitect the current data estate—from business intelligence to agentic AI, and from governed self-service to scalable, app-integrated data experiences.
Safer data access for AI with Tanzu Data Intelligence
Tanzu Data Intelligence is a comprehensive data intelligence platform for security-conscious enterprises operating on premises. The platform is built for data leaders, platform teams, AI/ML practitioners, and application developers who need fast, secure, and governed access to enterprise data across environments. With Tanzu Data Intelligence, enterprises are equipped to meet the growing demands of AI-native applications. Whether you’re modernizing analytics, enabling AI use cases, or simplifying data operations, Tanzu Data Intelligence can provide the foundation to unlock the full potential of your data across any environment to deliver meaningful business outcomes.
Ready to leapfrog to a modern, AI-ready data architecture? Contact the Tanzu team. And if you’re attending Explore in Las Vegas 2025, come to our breakout sessions and meet-the-expert roundtable session highlighting Tanzu Data Intelligence:
- How a Modern Data Strategy Enables Success on Your AI Journey (MODB1724LV)
- Powering Private AI with a Secure and Scalable Data Platform (MODB1045LV)
- Build Ultra-Fast, Resilient AI Applications with VMware Tanzu Data Intelligence [VBT2825LV]
- Dig Deeper into VMware Tanzu Data Intelligence [MODM2003LV]
