Semantic Layer for Databricks

The Ultimate Semantic Layer for your Lakehouse

Innovative companies like Databricks and their lakehouse approach help organizations simplify their data architecture by unifying their data, analytics, and AI workloads on a common platform. Datalakes are technical solutions, very effective at consolidating data naturally amassed in silos. However, to deliver ROI for an organization, datalakes need to demonstrate business value. That means being able to answer complex questions by connecting relevant data elements and providing true context and greater meaning to data by modeling relationships.

Stardog’s Enterprise Knowledge Graph platform powers a “missing middle” semantic data layer to accelerate returns from your datalake investment. By connecting enterprise data and overlaying business semantics, knowledge graphs facilitate more agile data operations, reduce the cost of data integration, and help generate powerful insights into complex business challenges.

Harmonize data with business meaning

Data consumers can easily define relevant business concepts and relationships as a semantic data model meaningful to their use-case with our no-code, low-code visual modeling tool. Data can be shared and reused through a common, standards-based vocabulary. The semantic model can be enhanced with data quality constraints applicable to the business context.

Connect to all relevant data

Data wrangling continues to hinder feature engineering and data science productivity with data both inside and outside the lakehouse. Link to relevant data outside the lakehouse with one of our 150+ connectors, including unstructured text. Stardog’s platform is underpinned by a highly efficient, robust, and ACID-compliant graph database. Ensure near-real-time responses on up to 100 billion data points per installation.

Enable data exploration and discovery

Ask and answer questions across a diverse set of connected data domains to fuel business insights without specialized skills. Inference creates new relationships by interpreting your source data against your data model. By expressing all the implied relationships and connections between your data sources, you create a richer, more accurate view of your data. This includes the ability to represent multiple definitions for the same data, empowering collaboration between stakeholders and expressing situational or theoretical truths.

Learn more:

Global Biotech Pioneer Harnesses Stardog + Databricks to Power Insights and Drive Innovation

  • Quote

    Growing levels of data volume and distribution are making it hard for organizations to exploit their data assets efficiently and effectively. Data and analytics leaders need to adopt a semantic approach to their enterprise data; otherwise, they will face an endless battle with data silos.

    Gartner Report, “Leverage Semantics to Drive Business Value From Data”, Guido De Simoni, Robert Thanaraj November 23, 2021
  • Quote

    Ad-hoc report requests went from taking 5+ days to taking 1 day.

    Managing Director, Enterprise Infrastructure, Top 10 US Bank

Features

Enterprise scalability

Scale up to 1 trillion triples; we’re also Kubernetes compatible and ACID-compliant.

Standards-based

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

Built-in ML

Use built in predictive analytics and similarity search to develop models to improve recommendations in search results.

Explainable

Where some AI tools cannot provide explanations for results, Stardog offers proofs for all query results for easy interpretation.

Semantic Layer for Databricks gif

Turn your columns into concepts

Stardog makes creating a knowledge graph easy directly from Databricks. Watch how to connect data from your lakehouse into a semantic layer which feeds your existing BI tools.

Watch the demo