One application. Production data pipelines by design.
Build, test, deploy and govern enterprise data flows without building another integration stack. IOblend combines visual development, portable metadata, Apache Spark execution and production DataOps in one operating model.
The production data layer between your systems and everything that needs trusted data.
IOblend is designed for organisations that already have databases, ERP, CRM, cloud platforms, lakehouses, analytics tools and AI services, but still need a reliable way to move, transform, validate, synchronise and govern data across the estate.
IOblend is enterprise data integration and DataOps software built on Apache Spark. Teams use the IOblend Designer to build and validate pipelines, store the pipeline logic as portable metadata, and execute it through the IOblend Engine on customer-controlled infrastructure. One operating model can cover batch, streaming, CDC, transformation, data quality, lineage, state, synchronisation and AI-assisted processing.
An independent production integration layer.
IOblend sits between enterprise systems and the platforms, applications, analytics or AI workloads that need current, validated and governed data.
Not another warehouse, lakehouse or mandatory SaaS platform.
IOblend is not intended to replace Fabric, Databricks, Snowflake, your databases or your operational applications. It is designed to make those technologies work together more reliably.
Separate the pipeline logic from the infrastructure underneath it.
IOblend uses a metadata-driven architecture to separate pipeline design from runtime execution. Data teams build and validate flows in the IOblend Designer, store the configuration and business logic as portable JSON playbooks, then execute the same definition through the IOblend Engine on Apache Spark infrastructure controlled by the customer.
IOblend Designer
Build visual DAGs, connect enterprise sources and sinks, add SQL or Python transformations, define quality checks and test components while the pipeline is being created.
Portable JSON playbooks
Pipeline configuration, run parameters, mappings and business logic are stored as reusable metadata that can be versioned, compared, shared and moved through normal development workflows.
IOblend Engine
The Engine interprets the playbook, creates the required distributed Spark processing and applies IOblend production controls for quality, lineage, state, exceptions and runtime management.
Your infrastructure
Run locally, on-premises, in cloud, at the edge or across hybrid environments. IOblend does not require enterprise data to pass through an IOblend-hosted SaaS data plane.
Designer → metadata → Engine → Spark → your systems.
This separation is what allows a pipeline definition to stay explicit and reusable while execution can scale on distributed infrastructure appropriate to the workload.
What usually becomes a stack of tools becomes part of the pipeline.
IOblend is designed so production concerns are not bolted on after development. They travel with the pipeline from the start.
Visual pipeline development
Connect sources, transforms and sinks in a visual DAG while retaining the freedom to express business logic in SQL or Python.
See how development works →CDC and synchronisation
Keep operational systems, applications, warehouses and lakehouses current without creating a separate architecture for every update pattern.
Explore synchronisation →Data quality in flight
Apply rules, validation, error handling and exception paths before poor data reaches analytics, applications or AI.
Explore production features →Record-level lineage + metadata
Track what happened to data through the flow and maintain the metadata required for auditability, discovery and operations.
See governance features →Schema + state management
Handle schema changes, stateful processing, windowing, late data and other operational realities of continuously changing data.
View technical highlights →Integrated validation + CI/CD
Test components during construction, prevent invalid pipeline logic from executing, retain revisions and move definitions through normal code repositories.
Go to documentation →SCD, deduplication + MDM
Maintain history, merge changing entities, resolve duplicates and keep trusted representations current inside the dataflow.
See the full feature set →Managed Spark execution
Use the distributed processing power of Spark without asking every data developer to engineer and maintain Spark jobs from scratch.
Read the Spark overview →Embedded AI agents
Call AI logic inside the pipeline, combine unstructured and structured data, validate results and route exceptions in the same production flow.
Explore Agentic AI ETL →Batch, streaming, CDC and AI do not need separate islands.
The architecture is deliberately broad enough to handle historical loads, continuously changing operational data and AI-assisted enrichment without forcing teams to reassemble the stack for every new use case.
One pipeline can move at more than one speed.
IOblend's Kappa-style approach allows teams to process streaming and batch data through the same product, apply transformations in flight and maintain state, history and quality around the flow.
Put intelligence where enterprise context already moves.
Use Python-based AI agents to inspect unstructured documents, extract or enrich information, validate outputs, quarantine exceptions and combine the result with normal ETL processing.
IOblend can also participate in AI workflows built with LangChain.
Build the logic visually. Keep the underlying architecture production ready.
The Designer gives data teams a visual development environment while the resulting pipeline remains explicit, reusable and operationally governed.
From first component to production without changing mental models.
The same pipeline definition moves through development, validation, version control, deployment and operations.
Design
Assemble sources, transforms, business rules, quality checks and sinks in the Designer.
Validate
Execute and inspect components during construction; stop invalid logic before it becomes a deployment problem.
Version
Store JSON playbooks in your normal code repository and retain pipeline revisions for comparison or rollback.
Deploy
Move the Engine to customer-controlled Spark infrastructure (e.g containers, VMs) and schedule execution using the enterprise tooling you prefer.
Operate
Run with lineage, metadata, quality, error management, state and other DataOps capabilities already attached.
Your data does not need to revolve around IOblend.
The Engine is designed to live inside the customer's environment. Storage and compute can be separated, and the processing layer can sit wherever it makes architectural, security and economic sense.
Use the Developer Edition with a local Spark environment for pipeline construction and testing.
Run processing close to existing systems, regulated data or infrastructure that is not moving to public cloud.
Deploy into cloud Spark environments and connect modern platforms to the operational estate around them.
Move and process data across mixed environments without forcing every workload into one platform boundary.
One pattern can replace a family of bespoke pipelines.
A more useful way to judge IOblend is to look at how much engineering disappears from the second, third and twentieth pipeline once the production pattern has been established.
Oracle CDC → Microsoft Fabric, end to end.
Define change capture, SQL/Python transformation, quality gates, quarantine, lineage and the target write in one metadata-driven pattern. Reuse it across a business domain instead of rebuilding the surrounding orchestration for every table.
Move what changed, not the whole source.
CDC and state handling allow the pipeline to process inserts and updates incrementally rather than repeatedly extracting and transforming unchanged data.
Validate the logic before distributed execution.
Build and validate the pipeline in the Designer, catch invalid logic or SQL/Python syntax early, then run the same pipeline definition on the production Spark runtime.
Illustrative architecture / delivery pattern. Exact implementation effort, throughput and latency depend on source systems, data volumes, infrastructure and business rules.
One execution layer. Different enterprise problems.
IOblend is most valuable where data has to keep moving reliably across a mixed technology estate.
Cloud + platform modernisation
Connect Fabric, Databricks, Snowflake and cloud infrastructure to the legacy and operational systems that still run the business.
Explore migration →System synchronisation
Keep CRM, ERP, applications and downstream systems aligned in real time while maintaining transformation and quality rules.
Explore synchronisation →Production AI data
Feed models and agents with current, validated business data and integrate AI output back into governed operational workflows.
Explore Agentic AI →MLOps + feature engineering
Create, refresh and govern model features in the pipeline while using the lake or warehouse you already own.
Feature Store without the Store →Real-time operations + IoT
Combine event streams with reference or historical data for live operational analytics and decision workflows.
Explore solutions →Trusted analytics data
Move analytics-ready data with quality, lineage, history and schema management already embedded in the production flow.
Explore DataOps features →What is IOblend?
IOblend is enterprise data integration and DataOps software. Teams build and validate pipelines in the IOblend Designer, store the pipeline definition as portable metadata, and execute it through the IOblend Engine on Apache Spark infrastructure.
It is designed for production data movement and transformation across databases, applications, ERP, CRM, event streams, files, lakehouses, analytics platforms and AI workflows.
Does IOblend support ETL, ELT, batch, streaming and Change Data Capture?
Yes. IOblend supports historical batch processing, real-time streaming, CDC, synchronisation and transformation within the same production data architecture.
Teams can combine continuously changing operational data with historical or reference data without creating a separate toolchain for every processing pattern.
Do we need to replace Microsoft Fabric, Databricks, Snowflake or our existing data platforms?
No. IOblend is intended to complement an existing data estate rather than replace every platform in it. It can operate as the production integration layer between legacy systems, operational applications, databases, cloud platforms, lakehouses, analytics tools and AI services.
See the IOblend Solutions hub for common integration and modernisation patterns.
Do data engineers need to write Apache Spark code?
No Spark coding is required for normal IOblend pipeline development. The Engine creates the required distributed Spark processing from the pipeline playbook.
Engineers can still use SQL and Python where business-specific transformation, enrichment or custom processing is required.
Where does IOblend run and where does the data go?
Development can run locally, while production execution can run on customer-controlled cloud, on-premises, edge or hybrid Spark infrastructure.
IOblend is designed so enterprise data does not have to pass through an IOblend-hosted SaaS data plane. The appropriate deployment architecture still depends on the customer's infrastructure, security and governance requirements.
How does IOblend handle data quality, lineage and exceptions?
Production controls are part of the pipeline rather than separate after-the-fact tools. Pipelines can apply validation and business rules, isolate exceptions, maintain metadata and preserve record-level lineage context around what happened to the data.
This helps teams investigate failures and data-quality issues without treating every bad record as a complete pipeline outage.
How are pipeline definitions versioned and moved through development?
IOblend stores pipeline definitions as portable JSON playbooks. These can be retained in normal code repositories, compared across revisions and promoted through development workflows.
The Designer also validates pipeline components during construction and prevents invalid logic or SQL/Python syntax from being executed.
Can IOblend process unstructured data and use AI inside an ETL pipeline?
Yes. Python-based AI logic can be used inside the pipeline to inspect documents or other unstructured content, extract or enrich information, validate results and route exceptions alongside conventional data processing.
This allows structured enterprise data, unstructured input and model-generated output to pass through the same production controls for quality, lineage and exception handling.
Is IOblend suitable for system synchronisation as well as analytics pipelines?
Yes. IOblend can be used to keep ERP, CRM, operational applications, databases, lakehouses and downstream systems aligned using CDC, transformations, state management and data-quality rules.
This is useful when integration has to support operational processes as well as analytics or reporting.
Can we try IOblend before an enterprise deployment?
Yes. IOblend provides a free Developer Edition for development and evaluation use. The current download page includes packages and getting-started guidance.
The fastest way to evaluate fit is to use a real source-to-target problem. Bring the systems, processing pattern, data-quality requirements and deployment constraints, and the IOblend team can show how the architecture maps to the use case.
Bring us the pipeline you do not want to build the old way.
A migration that is dragging on. A Fabric programme blocked by legacy integration. Six systems that need to stay in sync. Streaming data nobody fully trusts. An AI use case waiting for production-grade context. Give us the problem and we will show you where IOblend changes the architecture.