Enterprise data integration for AI, analytics and modernisation.

IOblend is enterprise data integration and DataOps software that acts as an independent production data layer. It connects heterogeneous systems, processes batch, streaming and Change Data Capture data, applies transformation, quality, lineage and state management, and delivers trusted data into the analytics, AI, application or operational platform the business already uses.

IOblend is designed for problems that sit between systems: fragmented data, legacy-to-cloud migration, real-time integration, system synchronisation, Customer 360, operational data pipelines, AI-ready enterprise context and production data quality. The Solutions Hub maps these needs to dedicated solution patterns.

No. IOblend is designed to complement those platforms. Microsoft Fabric, Databricks and Snowflake can remain analytics, lakehouse, warehouse or AI environments while IOblend handles production integration, transformation, governance and synchronisation across the wider enterprise estate.

No. Systems such as SAP, Salesforce, Oracle and other operational platforms remain systems of record. IOblend connects and synchronises data between them, applies business rules in flight and delivers consistent data to downstream consumers.

Yes. IOblend supports batch processing, real-time streaming and Change Data Capture within the same production pipeline model. This lets teams combine historical data, live events and changing operational records without maintaining separate integration architectures for each mode.

IOblend prepares and maintains the fresh, governed enterprise context AI systems need. Structured and unstructured data can be transformed, validated and enriched before being delivered to AI applications or agents, with lineage, data quality and exception handling around the production flow. See the Agentic AI solution →

Production controls are applied inside the pipeline rather than as a separate after-the-fact process. IOblend supports schema validation, data contracts, quality rules, exception isolation, record-level lineage, metadata, reconciliation and audit patterns while valid data continues through the flow.

Yes. IOblend is designed for customer-controlled cloud, on-premises, hybrid and edge environments. The execution model is based on Apache Spark, while IOblend Designer and Engine provide the pipeline development and production operating model around it.

A warehouse or lakehouse primarily stores and analyses data. IOblend focuses on moving, transforming, validating, synchronising and governing data between systems. It can deliver into a warehouse or lakehouse without requiring that storage platform to become the integration layer for the entire enterprise.

IOblend is a strong fit when an organisation has multiple operational systems, hybrid or multi-platform infrastructure, demanding migration or synchronisation requirements, real-time data needs, or AI initiatives that depend on current, governed business context. It is especially relevant when replacing the entire existing stack would create more cost and risk than value.

Yes. IOblend can build governed feature pipelines from batch, streaming and CDC data, maintain state and point-in-time history, and materialise feature data into the warehouse, lake or serving target already used by the ML estate. See the MLOps solution →

IOblend separates reusable pipeline logic from the underlying infrastructure. Portable playbooks, SQL and Python logic, built-in validation, lineage, state and testing allow teams to reuse patterns rather than rebuilding bespoke integration logic for every pair of systems.