FIDDS provides a cloud‑native, Kubernetes‑orchestrated data platform that unifies structured, semi‑structured, and streaming sources into a centralized data lake while preserving architectural context. The platform includes automated metadata cataloging, lineage visualization, role‑based access controls, and built‑in MLOps pipelines for model training, versioning, and deployment, enabling enterprise data and analytics teams to deliver governed, real‑time insights.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often operate with fragmented data silos across business, IT, and sourcing domains, making it difficult to establish a reliable single source of truth. This fragmentation leads to dashboards that lack actionable insight and hampers data‑driven decision making. Additionally, integrating AI/ML workflows into existing infrastructure is complex without a unified data platform.
Solution
FIDDS delivers a composable, cloud‑native data engine that consolidates disparate data sources into a centralized data lake while preserving the underlying architectural context. The platform provides automated metadata harvesting, data lineage tracking, and a governance layer that enforces role‑based access and compliance policies. Built on Kubernetes, it supports DevOps‑style CI/CD pipelines for data ingestion, transformation, and model deployment, enabling rapid iteration and reproducible workflows. Integrated MLOps capabilities allow teams to train, validate, and serve AI models directly on the curated data set. Through open APIs and a self‑service catalog, business users can discover and query data without relying on IT bottlenecks, turning raw information into actionable insights.
Target Audience
The primary customers are enterprise‑level data and analytics leaders—CIOs, CDOs, and heads of data engineering—who need to unify data across multiple domains and accelerate AI‑enabled initiatives. The solution also serves regulated industries such as finance, healthcare, and utilities that require robust governance and compliance.
Features
- Kubernetes‑orchestrated, cloud‑agnostic data engine with modular plug‑in architecture for adopt, extend, or build use cases
- Schema‑on‑read ingestion framework that ingests structured, semi‑structured, and streaming data into a unified data lake
- Automated metadata catalog and data lineage visualizer for traceability and impact analysis
- Fine‑grained, role‑based access control and end‑to‑end encryption complying with GDPR and industry standards
- API‑first integration layer (REST & GraphQL) enabling seamless connectivity to BI tools, ERP systems, and custom applications
- Built‑in MLOps pipeline supporting model training, versioning, monitoring, and deployment at scale
- Real‑time analytics engine with support for SQL, Spark, and Flink workloads for both batch and streaming use cases
- Governance dashboard that enforces data quality rules, monitors usage metrics, and provides audit trails