Enterprise-grade data infrastructure that powers everything. One source of truth. Real-time. Infinite scale.
The Single Data Backbone (SDB) is Weaver's core data infrastructure. Think of it as a peer of the lakehouse architecture[103] popularized by Databricks and Snowflake — but designed from the ground up to power business applications, not just analytics.
Unlike traditional data platforms that require you to build everything on top, SDB comes with native business apps (ERP, CRM, Projects, Expenses) already built. And unlike traditional business apps that each have their own database — the pattern that Inmon[101] first warned about in 1992 and that Halevy and colleagues[104] later showed is the root cause of runaway integration cost — all Weaver apps read and write to the same SDB layer.
This means: no sync issues, no integration hell, no data silos. Everything is real-time. Everything is connected.
See the difference between fragmented tools and unified platform
Separate Databases
Each tool has its own data store
Integration Required
Build connectors, pay for middleware
Sync Delays
Data updates lag between systems
Data Silos
Can't get complete business view
Single Data Backbone
One unified data layer
Native Apps
Built on same platform, zero integration
Real-Time Everything
Changes reflect instantly across all apps
Complete Visibility
Unified analytics across entire business
All apps read and write to the same data layer. No middleware, no sync delays, no conflicts.
Query across all your business data instantly. No ETL, no data warehouses, no waiting.
Build custom apps or integrate with existing tools through a single, unified API.
SDB is built with the same architectural principles[103] as Databricks and Snowflake:
But unlike pure data platforms, SDB comes with native business applications already built on top. You get the infrastructure and the apps, all unified.
Book a demo to see how the Single Data Backbone can transform your business operations.
The Single Data Backbone is grounded in the data-architecture and AI-governance literature below. Full bibliography on the /research hub.
Inmon, W. H. (1992). Building the Data Warehouse. New York: John Wiley & Sons.
Origin of the corporate data-warehouse concept and the case for a single subject-oriented integrated data store.
Read sourceKimball, R. & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley.
Canonical reference on dimensional modeling and the case against silos of analytical data.
Read sourceArmbrust, M., Ghodsi, A., Xin, R., & Zaharia, M. (2021). Lakehouse: A new generation of open platforms that unify data warehousing and advanced analytics. In Proceedings of the 11th Conference on Innovative Data Systems Research (CIDR '21).
The Databricks "lakehouse" paper — the architectural argument for unifying warehouse and lake into one platform that supports both analytics and applications.
Read sourceHalevy, A., Rajaraman, A., & Ordille, J. (2006). Data integration: The teenage years. In Proceedings of the 32nd VLDB Conference, 9–16.
Survey of why enterprise data integration is structurally hard and why "every new application means another integration project."
Read sourceStonebraker, M. (2010). SQL databases v. NoSQL databases. Communications of the ACM, 53(4), 10–11.
On ACID, consistency, and the cost of giving them up — supports the SDB ACID claim.
Read sourceNational Institute of Standards and Technology (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1.
US voluntary standard for managing AI risk — anchors the "AI with human control" claim with a recognised governance framework.
Read sourceSix native business apps split between Strategy (Metric Tree, Business Intelligence, Growth Engine) and Operations (Project Management, Financial Ops, Sales Operations) — all on the Single Data Backbone.
Complete financial operations: GL, AP/AR, financial reporting, multi-entity, multi-currency.
Customer relationships, sales pipeline, and revenue recognition on the Single Data Backbone.
Project management with Shape Up methodology, resource tracking, and project financials wired directly to ERP.
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