Enterprise Data Hubcentralized management
Enterprise data management
enterprise data management
Client
DataHub
Industry
SaaS
Headquarters
Spain
Services
Branding
About project
DataHub is an enterprise data management platform that centralizes data cataloging, lineage tracking, and governance across distributed data infrastructure.
Human-Readable Translation
Send 100 USDC to secure Vault
Auto-Recovery Security Shield Enabled
Web3 Security, Redefined.
We engineered a hardware-compatible interface that translates confusing crypto actions into clean, readable experiences.
Enterprise teams suffered from undiscoverable data assets, broken lineage tracking, and governance gaps that increased compliance risk and slowed analytics delivery.
Zylo architected a metadata-driven data catalog with automated lineage capture, role-based governance controls, and search-first data discovery workflows.
Process
Discovery
Data infrastructure mapping
Governance gap analysis
User workflow research
Lineage source identification
Compliance requirement review
Data Architecture
Metadata model design
Lineage capture system
Governance policy engine
Search index structure
Access control framework
Implementation
Connector development
Performance testing
Governance validation
Enterprise rollout
System Architecture
A metadata ingestion layer connects to databases, pipelines, and BI tools — continuously indexing assets, capturing lineage events, and surfacing governance alerts.
Interface Engineering
The catalog interface organizes data assets by domain, owner, and quality score — with lineage graphs, tag systems, and policy indicators all accessible without deep technical navigation.
Every data asset — discoverable, governed, and trusted.
MOBILE-FIRST
EXECUTION
Mobile access provides lightweight data asset search, governance alert notifications, and approval workflows for data access requests.
UI Kit
Component library covers asset cards, lineage graph nodes, quality score badges, governance status chips, and metadata edit forms.
AUTOMATION
FRAMEWORK
Metadata ingestion, lineage capture, quality scoring, and policy enforcement are fully automated through connector-based data platform integrations.
Design system
Results
Measurable outcomes from real-world AI and engineering deployments.
9×
Faster data asset discovery
+81%
Increase in documented data lineage coverage
-63%
Reduction in data governance incidents
94%
Data quality improvement across catalogued assets
Verified across 500+ enterprise deployments
Let's work together
3-day AI Engineering Collaboration
Sprint
AI-driven collaboration sprint with senior engineers to design, build, and refine real-world software solutions. Focused on execution, technical depth, AI capability, and product thinking—not just ideas, but working systems.