Say "Yes" to every data request

Build your data fabric with Stardog’s Enterprise Knowledge Graph platform. Create a flexible, reusable data layer for answering complex queries across data silos.

Digital transformation demands rapid insight from increasingly hybrid, varied, and changing data. Traditional data integration platforms can’t keep up with this growing complexity, not to mention frequently changing business requirements and greater demand for curated datasets.

Data fabrics offer a more flexible solution, supporting dynamic delivery of semantically enriched data. An Enterprise Knowledge Graph is the key ingredient to transforming existing data infrastructure into a data fabric. Stardog’s platform accomplishes this through a unique combination of graph, virtualization, and inference.

Modernize without disrupting legacy systems

You don’t need to copy your data into Stardog to use it. Modernize existing investments in data integration platforms, data lakes, and data catalogs. Access these systems remotely using virtualization, whether data lives on-prem or in the cloud, or choose to materialize as needed. Stardog’s third-generation virtualization engine supports high-performance, cost-efficient data fabric deployments. It also carries the benefit of avoiding complex ETL jobs and saving money on cloud hosting costs of duplicated data.

Learn more:

7 questions to decide if you should virtualize or materialize your data

Maintain multiple data definitions

Stardog’s Enterprise Knowledge Graph platform enables collaboration with stakeholders across the business and supports their unique needs. In contrast to the fixed structure of data integration platforms, Stardog allows different use cases, apps, or lines of business to share and reuse connected data without stepping on each other’s toes or, just as crucially, without requiring a single schema to rule all the others.

Learn more:

Schema multi-tenancy and virtual transparency support advanced data fabrics

Support countless use cases

From satisfying reporting requirements to supporting application development teams, Stardog allows for different interpretations of the same data. Our Enterprise Knowledge Graph platform centralizes and leverages data modeling, meaning each additional use case deploys faster. This low-code centralized data model dramatically cuts down project-specific data preparation and stitch code.

Learn more:

IDB improves findability of research across the bank with 5 connected use cases

  • Quote

    In a data fabric approach, one of the most important components is the development of a dynamic, composable and highly emergent knowledge graph that reflects everything that happens to your data. This core concept in the data fabric enables the other capabilities that allow for dynamic integration and data use case orchestration.

    Gartner “How to Activate Metadata to Enable a Composable Data Fabric,” Mark Beyer, Ehtisham Zaidi, 16 July 2020
  • Quote

    The advances in technology and the utilization of knowledge graphs as core components of data integration tools now provide significant inputs to the design of what can become the data fabric.

    Gartner “How to Activate Metadata to Enable a Composable Data Fabric,” Mark Beyer, Ehtisham Zaidi, 16 July 2020
  • Quote

    The layered analyses and resulting graph outputs become the basis of understanding how different datasets throughout the enterprise relate to each other. The resulting knowledge graph becomes the interface for understanding the data fabric and makes it searchable.

    Gartner “How to Activate Metadata to Enable a Composable Data Fabric,” Mark Beyer, Ehtisham Zaidi, 16 July 2020


Performant and scalable

Scale to 50 billion data points on a single node; we’re also Kubernetes compatible and ACID-compliant

Data quality management

Use Data Quality Constraints to find inconsistencies across your data silos and flag conflicts to data producers


Stardog is built on open W3C standards designed to facilitate interoperability and exchange of data

Robust security

Assign permissions by user profile, restrict access to datasets with Named Graphs, and authenticate users for secure access