Merkle Data provides a centralized knowledge‑graph platform that aggregates metadata from databases, data lakes, ETL pipelines, and SaaS applications into a unified graph model. The system offers automated lineage tracking, a semantic layer for consistent querying, and built‑in governance controls accessible via an interactive UI and GraphQL/REST APIs, enabling enterprise data teams to perform impact analysis, root‑cause diagnostics, and compliance monitoring.
Funding
$10M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises often operate with siloed data stores and fragmented metadata, making it difficult to trace data lineage, enforce governance policies, and derive cross‑system insights. The lack of a unified view hampers rapid data discovery and increases the risk of compliance breaches.
Solution
Merkle Data delivers a centralized knowledge‑graph platform that ingests metadata from heterogeneous sources—databases, data lakes, ETL pipelines, and SaaS applications—and materializes the relationships as a graph model. Automated lineage extraction continuously maps upstream and downstream dependencies, while a semantic layer normalizes entities for consistent querying. Users can explore the graph via an interactive UI or programmatically through a GraphQL/REST API, enabling real‑time impact analysis and root‑cause diagnostics. Built‑in governance controls enforce access policies and data‑quality rules directly on the graph, ensuring compliance without manual overhead. The platform’s analytics engine applies graph‑based algorithms to surface hidden patterns, recommend data enrichment, and support predictive operational intelligence.
Target Audience
Primary customers are data‑engineering, data‑governance, and analytics teams within large enterprises—particularly in regulated sectors such as finance, healthcare, and telecommunications—that need a holistic view of data relationships to accelerate discovery and ensure compliance.
Features
- Scalable graph database engine optimized for high‑cardinality relationship queries across petabyte‑scale metadata
- Automated metadata ingestion connectors for relational, NoSQL, cloud storage, and SaaS platforms with incremental change detection
- Continuous lineage tracking that captures schema evolution, data transformations, and job scheduling dependencies
- Semantic modeling toolkit that lets data stewards define canonical entities, attributes, and taxonomies across domains
- GraphQL and RESTful APIs with fine‑grained RBAC for programmatic access and integration into BI or data‑catalog tools
- Interactive visualization console featuring drill‑down path analysis, impact heatmaps, and customizable dashboards
- Policy engine that enforces data‑governance rules (e.g., GDPR, CCPA) directly on graph edges and nodes
- Built‑in anomaly detection using graph‑based machine‑learning models to flag unexpected lineage changes or data‑quality issues