Why this matters: Your enterprise data lives in dozens of systems — cloud warehouses, on-premises databases, SaaS applications, data lakes. Traditional integration approaches require months of ETL development, costly data movement, and constant maintenance. Data fabric architecture offers a fundamentally different path: unified access without relocation, intelligent automation without complexity, and governance without bottlenecks.
For a comprehensive overview of data fabric architecture and use cases, download our eBook “Demystifying Data Fabric”
What Is Data Fabric Architecture?
Data fabric is an architectural pattern that creates a virtualized layer connecting disparate data sources while integrating metadata management and automated governance to provide consistent, secure, and real-time access across hybrid environments.
The fundamental shift: Instead of physically moving data into centralized repositories, data fabric creates a logical access layer that brings the query to the data — not the data to the query.
Core architectural principles:
Data virtualization foundation — Creates unified views without physical data movement or duplication. Your data stays where it lives; the fabric provides the connective tissue.
Metadata-driven intelligence — Leverages active metadata for automated decision-making, quality monitoring, and intelligent routing. The fabric learns from usage patterns and continuously optimizes.
Federated governance — Enables local data management with global policy enforcement. Business units maintain autonomy while enterprise standards remain consistent.
Real-time integration — Supports both streaming and batch data processing. Get sub-second responses for operational queries alongside complex analytical workloads.
AI-powered automation — Uses machine learning for intelligent data orchestration, quality validation, and performance optimization. The system gets smarter with every query.
Core Architectural Components
Based on Gartner’s reference architecture, data fabric consists of three foundational layers supporting data consumers through orchestration capabilities.
Data Fabric Architecture Diagram
Layer 1: Data Sources
Foundation layer — All enterprise data assets regardless of location, format, or platform.
Source types:
Cloud data warehouses — Snowflake, Databricks, BigQuery, Redshift providing structured analytics storage
On-premises databases — Oracle, SQL Server, PostgreSQL, MySQL, DB2 hosting transactional and operational data
SaaS applications — Salesforce, Workday, ServiceNow, HubSpot containing business-critical information
Data lakes — S3, Azure Data Lake, Google Cloud Storage holding raw and semi-structured data
File systems — Network shares, HDFS, object storage with documents and unstructured content
Streaming platforms — Kafka, Kinesis, Event Hubs delivering real-time data flows
APIs and web services — REST, GraphQL, SOAP endpoints exposing external data sources
The fabric connects to all these sources without requiring data movement — query where data lives.
Layer 2: Data Integration
Connectivity and access layer — Establishes unified access to distributed data sources through standardized interfaces.
Core capabilities:
Universal connectivity — Pre-built connectors supporting 200+ data sources with automated discovery and cataloging. When teams add new systems, the fabric identifies available data automatically.
Data virtualization — Creates logical views without physical replication. Query data across multiple sources as if it were a single database — the fabric handles distributed query execution complexity.
Federated query processing — Distributed SQL processing using engines like Trino or Presto that push computation to data sources for optimal performance.
Real-time and batch integration — Supports both streaming data flows and batch processing. Change Data Capture (CDC) ensures synchronized updates without full reloads.
Protocol translation — Handles different data formats (JSON, XML, Parquet, CSV), schemas, and communication protocols. Applications use standard SQL or REST APIs regardless of underlying complexity.
API gateways — RESTful and GraphQL interfaces for programmatic access from custom applications and analytical tools.
Layer 3: Augmented Data Catalog
Discovery and metadata management — Provides centralized inventory of data assets with comprehensive metadata and business context.
Catalog capabilities:
Automated asset discovery — AI-powered identification and registration of data sources, tables, columns, files, and APIs. Continuous scanning keeps the catalog current as systems evolve.
Business glossary integration — Standardized definitions and context for data elements and business terms. “Customer” means the same thing across departments and systems.
Collaborative enrichment — User-generated tags, comments, ratings, and annotations. Crowdsource data quality feedback and business context from actual users.
Search and discovery:
Semantic search — Natural language queries for finding relevant data assets. Search for “customer lifetime value” and find tables regardless of technical naming.
Faceted navigation — Multi-dimensional filtering by source type, domain, quality metrics, sensitivity level, and business area.
Recommendation engine — AI-powered suggestions for related or complementary datasets based on usage patterns and relationships.
Usage analytics — Insights into most-accessed datasets and popular query patterns. Understand which data assets provide the most business value.
Layer 4: Knowledge Graph — Enriched with Semantics
Relationship and context layer — Maps connections between data assets, business concepts, and organizational knowledge.
Knowledge graph capabilities:
Entity resolution — Identifies when the same real-world entity (customer, product, location) appears across multiple systems with different identifiers or formats.
Relationship mapping — Discovers and documents connections between data assets. Automatically identifies foreign key relationships, hierarchies, and dependencies.
Semantic enrichment — Layers business meaning onto technical structures. Links database columns to business concepts, processes, and outcomes.
Ontology management — Maintains enterprise vocabulary and classification schemes. Ensures consistent understanding of data across teams and systems.
Context propagation — Applies metadata and business rules consistently across related data assets. Changes to customer definition cascade to all relevant tables and fields.
Graph-powered insights:
Impact analysis — Understand downstream effects before making changes. What breaks if we modify this table schema? Which reports depend on this data source?
Lineage visualization — Interactive maps showing data flow from source to consumption. Click any field to see where it comes from and where it’s used.
Data discovery — Find data assets by exploring relationships rather than just searching names. “Show me all data related to customer churn prediction.”
Layer 5: Active Metadata
Intelligence and automation layer — Transforms passive metadata into dynamic insights that drive automated decision-making.
Active metadata capabilities:
Real-time metadata collection — Continuously captures metadata from data catalogs, processing engines, BI tools, and user interactions. Metadata stays current with actual system state.
Usage pattern analysis — Tracks access patterns, query performance, and user behavior. Identifies frequently used data, performance bottlenecks, and optimization opportunities.
Data quality monitoring — Automated validation, anomaly detection, and quality scoring. Catch issues before they impact decisions with continuous assessment.
Performance intelligence — Analyzes query patterns and resource consumption to optimize caching, indexing, and query routing strategies.
Predictive recommendations — Suggests data sources for analysis, optimization opportunities, and potential quality issues based on historical patterns and machine learning.
Metadata-driven automation:
Schema evolution handling — Detects source system changes and automatically updates mappings, transformations, and downstream dependencies.
Policy enforcement — Uses metadata about data sensitivity and user roles to apply security and governance policies dynamically at query time.
Smart caching — Determines which data should be cached based on access frequency, query patterns, and freshness requirements.
Layer 6: Data Preparation and Data Delivery
Transformation and provisioning layer — Prepares and delivers data in formats and structures optimized for consumption.
Preparation capabilities:
Automated data profiling — Analyzes data distributions, quality metrics, and patterns. Identifies anomalies, missing values, and potential issues without manual inspection.
Intelligent transformations — AI-powered suggestions for data cleaning, normalization, and enrichment. “This field looks like phone numbers with inconsistent formatting — should we standardize them?”
Self-service data preparation — Business users perform common transformations (filtering, aggregation, joining) without writing code. Technical precision without technical barriers.
Quality assurance — Automated validation rules and quality checks applied during preparation. Ensure data meets standards before delivery to consumers.
Delivery mechanisms:
Format optimization — Converts data to formats optimized for specific use cases: Parquet for analytics, JSON for APIs, CSV for spreadsheets.
Incremental updates — Delivers only changed data rather than full refreshes. Reduces network load and processing time for large datasets.
Scheduled provisioning — Automated delivery pipelines that refresh data on defined schedules or triggered by specific events.
API-based access — RESTful and GraphQL endpoints for real-time programmatic data access from applications and services.
Layer 7: Recommendation Engine
Intelligent guidance layer — Provides context-aware suggestions to accelerate data discovery and analysis.
Recommendation types:
Data asset suggestions — “Users analyzing customer churn also used these product usage and support ticket datasets.”
Query optimization hints — “This query would run 10x faster if you added these filters or used this pre-aggregated view.”
Quality improvement recommendations — “This field has 15% null values — consider these alternative data sources or imputation strategies.”
Related analysis patterns — “Other analysts exploring this topic created these visualizations and metrics.”
Recommendation mechanisms:
Collaborative filtering — Analyzes what similar users accessed and found valuable. Leverage collective intelligence of the organization.
Content-based recommendations — Suggests assets based on metadata similarity, relationships in the knowledge graph, and semantic connections.
Context-aware suggestions — Considers user role, current task, and analysis goals when making recommendations. Marketing analysts see different suggestions than finance teams.
Learning feedback loops — Improves recommendations based on user actions. When suggestions are followed and lead to successful analysis, the system learns and refines future recommendations.
Layer 8: Data & AI Orchestration
Coordination and workflow layer — Manages end-to-end data workflows, AI model execution, and resource optimization.
Orchestration capabilities:
Workflow automation — Intelligent scheduling and dependency management. Complex data pipelines execute automatically with proper sequencing and error handling.
Resource optimization — Dynamic allocation based on workload requirements. Scale compute resources up during peak hours, down during quiet periods.
AI/ML pipeline management — Coordinates model training, evaluation, deployment, and monitoring. Data scientists focus on model development while the fabric handles infrastructure.
Real-time and batch coordination — Manages both streaming data flows and batch processing jobs. Ensures data freshness meets consumption requirements.
Performance monitoring — Real-time tracking of processing speed, resource consumption, and bottlenecks. Identifies optimization opportunities before they impact users.
AI-driven optimization:
Self-tuning systems — Automatic adjustment of processing parameters for optimal performance. The fabric learns which query patterns benefit from caching, which sources need connection pooling, and how to route requests efficiently.
Self-healing capabilities — Proactive identification and resolution of system issues. When a data source becomes temporarily unavailable, the fabric routes around it and retries when connectivity returns.
Predictive scaling — Anticipatory resource allocation based on usage patterns. If Monday mornings always spike with reporting queries, the system prepares additional capacity automatically.
Intelligent query routing — Directs queries to optimal execution engines based on query type, data location, and current system load.
Layer 9: Data Consumers
Access and interaction layer — Enables diverse user personas and applications to consume data through preferred interfaces.
Consumer types:
Business analysts — Access through BI tools, dashboards, and self-service analytics platforms or text-to-SQL tools. Get answers without technical expertise.
Data scientists — Work through notebooks, ML platforms, and statistical tools. Access raw and prepared data for model development.
Data engineers — Integrate through APIs, SDKs, and ETL tools. Build automated pipelines and custom applications.
AI agents — Query through natural language interfaces and programmatic APIs. Autonomous access for decision-making and automation.
Business applications — Consume through REST APIs, GraphQL, and embedded analytics. Integrate insights directly into operational workflows.
Access patterns:
Natural language queries — Ask questions in plain English through conversational interfaces. “What were our top-selling products in Q4?”
SQL and code-based access — Technical users write queries, scripts, and applications using standard protocols and languages.
Pre-built dashboards — Curated views of key metrics and KPIs for monitoring business performance.
Self-service exploration — Interactive tools for ad-hoc analysis without predefined reports or IT involvement.
Embedded analytics — Data insights integrated directly into CRM, ERP, and other business applications.
Cross-Cutting Capabilities: Governance and Security
While not a separate layer in the Gartner model, governance and security are foundational capabilities that span all architectural components.
Unified governance:
Centralized policy management — Define data rules, quality standards, and access policies once. The fabric enforces them consistently across all connected sources.
Role-based access control (RBAC) — Assign permissions based on job functions and responsibilities. Marketing analysts see customer data; finance teams access revenue information; neither sees what they shouldn’t.
Attribute-based access control (ABAC) — Dynamic permissions based on user attributes, data sensitivity, and context. A regional manager sees only their region’s data automatically.
Data lineage — Complete tracking of data flow from source systems through transformations to final consumption. Answer “where did this number come from?” with full transparency.
Compliance automation — Built-in support for GDPR, CCPA, HIPAA, SOX, and industry-specific regulations. Prove compliance with automated audit trails and policy enforcement.
Enterprise security:
End-to-end encryption — Data protection at rest and in transit using industry-standard algorithms with comprehensive key management.
Dynamic data masking — Context-aware obfuscation for sensitive information. Analysts see real patterns without exposing actual customer details.
Comprehensive audit trails — Log every data access, modification, and policy change. Meet regulatory requirements with complete visibility.
Zero trust architecture — Continuous verification with minimal privilege access. Users and applications prove identity and authorization for every request.
Threat detection — AI-powered monitoring identifies unusual access patterns, potential breaches, and policy violations in real time.
Five-Phase Implementation Framework
Phase 1: Integrate — Data Source Connection (4-8 weeks)
Objectives: Establish foundational connectivity to critical data sources and validate integration patterns.
Key activities:
Complete data source inventory — Audit existing data systems, formats, access patterns, and ownership. Document everything from cloud warehouses to departmental spreadsheets.
Validate connectivity — Test connection protocols, authentication methods, and performance characteristics. Ensure stable, secure access before proceeding.
Configure security — Implement authentication, authorization, and encryption protocols. Apply zero trust principles from day one.
Pilot integration — Connect 3-5 high-value data sources for proof-of-concept validation. Choose sources that demonstrate immediate business value and technical feasibility.
Establish performance baselines — Measure response times, throughput, and reliability. Set benchmarks for future optimization.
Success criteria:
- Secure connections to pilot data sources with proper authentication
- Sub-second query response times for simple aggregations
- Successful role-based access control enforcement
- Documented integration patterns for future scaling
Phase 2: Model — Metadata and Schema Design (6-10 weeks)
Objectives: Create comprehensive metadata models and establish semantic consistency across integrated sources.
Key activities:
Schema harmonization — Standardize data types, naming conventions, and business definitions. Resolve conflicts where “customer” means different things in different systems.
Build metadata catalog — Create comprehensive catalog with business context and technical specifications. Make data discoverable and understandable.
Map data lineage — Document data flow and transformation processes. Establish end-to-end visibility from source to consumption.
Establish quality framework — Define data quality rules, metrics, and monitoring procedures. Set standards for completeness, accuracy, consistency, and timeliness.
Create business glossary — Develop standardized vocabulary for enterprise data assets. Ensure everyone speaks the same language about data.
Deliverables:
- Unified semantic model covering pilot data sources
- Automated metadata collection and enrichment processes
- Data quality dashboard with key metrics and alerts
- Business-friendly data catalog with search and discovery
Phase 3: Connect — User Access and Integration (4-6 weeks)
Objectives: Enable business users and analytical tools to access data through the fabric layer.
Implementation steps:
Develop APIs — Create RESTful and GraphQL interfaces for programmatic access. Enable custom applications and integrations.
Integrate BI tools — Connect existing business intelligence platforms to the fabric. Preserve current dashboards and reports while extending their data reach.
Deploy self-service portal — Launch user-friendly interface for data discovery and access. Empower analysts to find and query data independently.
Optimize performance — Implement caching, indexing, and query optimization. Ensure sub-second responses for interactive queries.
Provide user training — Comprehensive training on new access patterns, self-service capabilities, and best practices.
Integration patterns:
- SQL gateway — Standard database connectivity for existing tools
- REST APIs — Modern application integration for custom development
- Streaming interfaces — Real-time data feeds for operational applications
- Embedded analytics — Integration within business applications and dashboards
Phase 4: Secure — Governance and Compliance (8-12 weeks)
Objectives: Implement comprehensive governance framework ensuring security, privacy, and regulatory compliance.
Security implementation:
Access control matrix — Role-based permissions aligned with business domains and responsibilities. Document who can access what and why.
End-to-end encryption — Data protection with comprehensive key management and rotation. Never expose sensitive data unencrypted.
Audit framework — Comprehensive logging and monitoring of all data access and modifications. Prove compliance with complete audit trails.
Privacy controls — Dynamic masking and anonymization for sensitive data elements. Comply with GDPR, CCPA, and other privacy regulations.
Compliance automation — Built-in controls for industry-specific regulations. Reduce manual compliance work with automated policy enforcement.
Governance processes:
Data stewardship — Assign domain experts as data stewards with clear responsibilities. Distribute ownership while maintaining consistency.
Policy enforcement — Automated implementation of business rules and data quality standards. No manual checks, no gaps in coverage.
Change management — Controlled processes for schema evolution and system modifications. Prevent breaking changes with impact analysis.
Incident response — Procedures for handling data breaches, quality issues, and system failures. Respond quickly with predefined playbooks.
Phase 5: Build — Advanced Analytics and Optimization (6-12 weeks)
Objectives: Deploy advanced analytical capabilities and optimize fabric performance for enterprise scale.
Advanced capabilities:
Machine learning integration — Deploy automated ML pipelines and model management. Enable data scientists to work at speed without infrastructure complexity.
Real-time analytics — Implement streaming analytics for operational intelligence. Make decisions based on current data, not yesterday’s batch job.
AI-powered automation — Enable intelligent query optimization and resource management. Let the system handle complexity while teams focus on insights.
Advanced visualization — Deploy modern analytics tools with self-service capabilities. Empower business users with interactive exploration.
Performance tuning — Optimize for enterprise-scale concurrent usage. Handle hundreds of simultaneous users without degradation.
Scaling considerations:
Load balancing — Distribute query processing across multiple nodes. Prevent bottlenecks with intelligent request routing.
Intelligent caching — Cache frequently accessed data for instant responses. Balance freshness requirements with performance needs.
Dynamic resource allocation — Scale compute resources based on workload. Maintain performance during peak usage without overprovisioning.
Comprehensive monitoring — Track system health, query performance, and resource utilization. Identify and resolve issues before they impact users.
Enterprise Benefits and Value Realization
Immediate operational gains
75% reduction in data preparation time — Automated integration and standardization eliminate manual data wrangling. Analysts spend time analyzing, not preparing.
60% faster time-to-insight — Business users get answers in minutes instead of waiting weeks for IT to build pipelines. Self-service access accelerates decision-making.
50% reduction in IT overhead — Self-service capabilities reduce dependency on centralized data teams. Engineers focus on strategic initiatives instead of routine data requests.
90% decrease in data movement — Zero-copy federation eliminates costly and time-consuming ETL processes. Maintain a single source of truth without duplication.
Strategic advantages
Unified data access — Single interface for all enterprise data regardless of source location or format. Break down silos without migration projects.
Real-time decision making — Sub-second query responses enable operational intelligence. React to changing conditions immediately, not days later.
Regulatory compliance — Automated governance ensures adherence to data protection regulations. Reduce compliance risk with consistent policy enforcement.
Innovation acceleration — Faster deployment of AI/ML initiatives through streamlined data access. Data scientists spend time building models, not hunting for data.
Long-term strategic value
Cloud migration support — Hybrid architecture enables gradual cloud adoption without disruption. Connect cloud and on-premises systems seamlessly during transition.
AI readiness — Foundation for enterprise AI initiatives with clean, accessible, governed data. Give AI agents the same unified access your teams enjoy.
Sustainable scalability — Architecture that grows with business needs without requiring complete redesign. Add sources, users, and use cases incrementally.
Vendor independence — Open architecture prevents lock-in to specific technology stacks. Preserve flexibility for future changes.
Reference Architectures
Traditional enterprise data fabric pattern
Hub-and-spoke model — Centralized governance with distributed data processing. Policy enforcement happens at the hub; data stays at the spokes.
Key components:
- Central metadata repository and governance layer
- Distributed data sources across cloud, SaaS, and on-premises
- Federated query engine for cross-source analytics
- API gateway for standardized access
- Security and compliance enforcement at query time
Implementation considerations:
- Requires significant infrastructure investment
- 6-12 month implementation timeline
- Complex integration with existing systems
- High operational overhead for maintenance
Microsoft Fabric approach
Unified platform model — OneLake foundation with integrated compute and analytics services.
Architecture characteristics:
- Centralized data lake (OneLake) as single storage layer
- Shared compute capacity across workloads
- Common metadata and lineage system
- Integrated BI, ML, and data engineering tools
Trade-offs:
- Requires data movement into OneLake
- Tied to Microsoft ecosystem
- Strong integration but less flexibility
- Best for organizations heavily invested in Microsoft stack
Promethium’s Open Data Fabric approach
Zero-copy federation model — Query data where it lives without movement or infrastructure changes.
Unique architectural characteristics:
- Universal connectivity without data movement: Connectors enabling direct access to all your data sources. No OneLake, no central repository, no data duplication.
- AI-native context engine: Automatic assembly of business and technical metadata from existing catalogs, tribal knowledge, and usage patterns. Complete context without manual tagging.
- Conversational interface: Natural language queries through Mantra™ Data Answer Agent. Business users ask questions in plain English, get SQL-backed answers with full explainability.
- Agentic architecture: Purpose-built for human-AI collaboration. AI agents get the same unified, governed access to data as human users.
- Open architecture: Multi-cloud support with no vendor lock-in. Works with your existing data stack — Snowflake, Databricks, cloud warehouses, SaaS platforms.
Architectural deployment advantages:
- Weeks to production: Not months of complex integration. Deploy without infrastructure changes or data migration.
- Zero disruption: Preserve existing workflows, tools, and team expertise. The fabric extends what you have, doesn’t replace it.
- Immediate ROI: Demonstrate value with pilot programs before full-scale deployment. Connect a few sources, solve real business problems, expand from there.
Positioning against alternatives:
- vs. traditional data fabric: Faster implementation with lower infrastructure requirements and zero data movement
- vs. Microsoft Fabric: Open architecture without OneLake requirement, preserves existing investments
- vs. ETL/data warehouse: Real-time access without batch processing delays or data staleness
- vs. data virtualization alone: Modern AI-powered interface with business user accessibility, not just technical capability
Complementary Architecture Patterns
Data fabric + data warehouse integration
Pattern: Maintain existing data warehouse investments for historical analytics while using data fabric to access real-time operational data from external sources.
Why it works: Preserves BI investment and existing reports while enabling real-time insights. Fabric queries combine warehouse data with live operational systems for complete answers.
Implementation: Connect fabric to warehouse as another data source. Business users query through fabric interface, which routes requests to warehouse or other sources as appropriate.
Data fabric + data lake synergy
Pattern: Use data lake for cost-effective raw data storage with fabric layer enabling diverse processing engines on lake data.
Why it works: Storage optimization with processing flexibility. Support both schema-on-read exploration and schema-on-write analytics from the same underlying data.
Implementation: Fabric connects to data lake (S3, ADLS, GCS) as distributed storage layer. Multiple processing engines (Spark, Presto, custom analytics) access through fabric’s unified interface.
Data fabric + data mesh
Pattern: Combine fabric (universal access) with mesh (domain ownership) for governed self-service at scale
Why it works: Addresses both technical challenges (data access) and organizational challenges (ownership and quality)
Implementation: Deploy fabric as the universal access layer connecting all data sources. Organize around data mesh domains (customer, product, finance) where domain teams own their data and publish curated “data products” through the fabric. The fabric’s metadata layer catalogs these products, enforces access policies, tracks lineage, and enables cross-domain federation. Domain teams maintain autonomy; the enterprise maintains consistency. Business users discover and combine data products across domains without knowing which team owns them or where the underlying data lives — the fabric handles the complexity.
>> Download the complimentary Gartner step-by-step guide
Multi-cloud data fabric
Pattern: Distributed architecture supporting data sources across multiple cloud providers without vendor lock-in.
Why it works: Leverage best-of-breed cloud services while maintaining unified access. Meet geographic data residency requirements while providing global accessibility.
Implementation: Deploy fabric control plane in primary cloud with connectors spanning AWS, Azure, GCP, and on-premises sources. Users see unified view regardless of underlying infrastructure complexity.
Technology Selection Considerations
Evaluation criteria
Connectivity breadth — Does the platform support your current and future data sources? Look for 200+ pre-built connectors plus ability to add custom sources.
Performance characteristics — What query response times can you expect? Real-time use cases need sub-second responses; batch analytics can tolerate minutes.
Security and governance — Does the platform meet your compliance requirements? Look for SOC 2, GDPR, HIPAA certifications plus fine-grained access controls.
Integration capabilities — How well does it work with your existing tools? Native integration with BI platforms, notebooks, and custom applications reduces friction.
Deployment flexibility — Can you deploy where your data lives? Look for hybrid cloud support, not just SaaS-only or on-premises-only options.
Implementation timeline — How fast can you show value? Weeks-to-production solutions enable quick wins; months-long implementations increase risk.
Total cost of ownership — What are the complete costs? Consider licensing, infrastructure, operational overhead, and opportunity cost of delayed value.
Common pitfalls to avoid
Technology-first decisions — Choosing architecture before understanding business requirements. Start with problems you’re trying to solve, then select solutions.
Underestimating complexity — Assuming data fabric eliminates all integration challenges. Technical complexity decreases but governance and organizational challenges remain.
Perfectionism paralysis — Delaying implementation while seeking ideal solutions. Start with high-value pilot, demonstrate ROI, expand incrementally.
Vendor lock-in — Making irreversible commitments to proprietary platforms. Choose open architectures that preserve flexibility for future changes.
Your Next Steps
If you’re exploring data fabric architecture:
- Document current pain points — What’s blocking your teams from getting data? Where do requests queue up? What questions take weeks to answer?
- Identify quick wins — Find 2-3 high-value use cases that demonstrate immediate ROI. Successful pilots build momentum for broader adoption.
- Assess organizational readiness — Do you have executive sponsorship? Technical skills? Change management support? Success requires more than just technology.
- Evaluate vendor approaches — Compare traditional data fabric (months to deploy, infrastructure changes required) vs. instant data fabric (weeks to production, zero disruption).
- Plan for hybrid reality — Most organizations will use multiple approaches. Data fabric doesn’t replace everything — it extends and connects what you have.
Ready to see unified data access in action? Promethium’s Open Data Fabric deploys in weeks — no data movement, no infrastructure overhaul, no disruption to current workflows. Connect your existing systems and get 10x faster answers while preserving everything your teams have built. Talk to our team today to learn more.
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