Invisible Infrastructure. Visible results.

SchemaVortex turns the data locked in your ERP, CRM and line-of-business systems into a governed lakehouse with history, running entirely inside your own Azure subscription on open Parquet and plain SQL. No pipelines to develop, no data-engineering team to hire.

Many organisations struggle with fragmented data, manual reporting and limited visibility across business systems. Building a reliable data lakehouse internally can take years, requiring specialist data engineering skills, custom pipelines, documentation, testing and ongoing maintenance. SchemaVortex provides a faster path: a ready-made lakehouse platform built on Microsoft Azure technologies, designed to help teams focus on insight instead of infrastructure.

SchemaVortex runs in the customer’s own Azure subscription and is built on Azure Synapse Serverless, Azure Data Lake Storage and open Parquet files. Data remains in the customer’s environment while being made available through standard SQL and familiar analytics tools such as Power BI, Excel and other SQL clients.

Who is it for?

SchemaVortex is designed for organisations that need reliable cross-system reporting without building a custom data platform from scratch. It is especially relevant for companies with small IT teams, BI teams with strong business knowledge but no engineering background, and data or analytics leaders who need a governed platform that can be operated after expert configuration and handover.

The solution is a strong fit for manufacturing, distribution, retail and service organisations where data is spread across ERP, CRM and operational systems. It also supports organisations looking to modernise outdated data warehouse environments, consolidate scattered data sources or strengthen data governance and auditability.

From source systems to business-ready data

SchemaVortex processes data through a four-zone pipeline:

• Intake: brings in pushed data, uploaded Excel and CSV files, and custom sources through the Producer SDK.
• Extraction: collects raw data from supported source systems and prepares it for the lakehouse process.
• Vault: stores historised and deduplicated data with change history, automatic change detection and schema evolution.
• Mart: creates governed, report-ready views and datasets for analytics and business reporting.

This structure helps organisations move from disconnected systems to consistent reporting. BI teams can work with trusted data, analysts can build on a stable foundation, and decision-makers can access insight from across the business.

Governance built in from day one

SchemaVortex is built around the principle that governance should be part of the lakehouse from the beginning, not added afterwards. Every table and column passes through approval, classification and masking controls before it becomes available for analysis.

The governance model uses five approval and masking gates: Vault Approval, Production Data Mask, Compliance Mask, Sensitivity Mask and Delivery Mask. These controls help manage which data enters the Vault, which sensitive values remain protected, and what level of access is required before data can be queried.

Masking is applied when queries run, so there is no separate sanitised copy of production data to build or maintain. Users receive the data view appropriate to their access level, while approvals, classifications and mask changes are recorded in the audit trail.

Why choose Schemavortex?

SchemaVortex helps teams understand where data comes from, how it is used and what may be affected when source systems change. View- and column-level lineage allows users to trace data back to its origin and assess downstream impact.

The platform also supports automatic schema discovery, schema evolution and full change history. When a source system changes, the Vault can preserve versioned structures to help protect existing reports. Historical data can be retained and reviewed, supporting stronger operational resilience and more transparent reporting.

SchemaVortex is managed through a single web interface covering configuration, monitoring, audit, governance and user management. After setup and knowledge transfer, day-to-day operation can be handled by the customer’s BI team or supported by an external specialist.

The platform also includes AI Assistance for query building, schema analysis and data classification. This capability runs in the customer’s Azure environment and follows the same governance model as the rest of the platform.

Contact us to discuss how SchemaVortex can support your data goals.