Update Frequency Matched to the Need
We select scheduled, event-driven, or streaming patterns according to business requirements and system capabilities. Latency targets and operating costs are agreed for each flow.
Analytics
Data Integration Consulting
Connect Systems and Reduce Manual Data Transfers
We connect applications, databases, and SaaS platforms through monitored data flows. Each integration is designed around the information consumers need, how quickly they need it, and the source systems' capabilities, with agreed validation and recovery procedures.
What We Deliver
Where We Make the Difference
Reliable integration starts with understanding both ends of a data flow. We agree on source behavior, destination requirements, update frequency, and ownership, then build and test the connections needed for your priority use cases.
Connections Built Around Source Constraints
We assess available APIs, connectors, schemas, and extraction methods for your ERP, CRM, and other business systems. Validation and reconciliation checks help identify missing, duplicated, or inconsistent data.
Recovery Planned From the Start
We design error handling, alerts, and recovery procedures for expected failure modes. Retries and replay are used where appropriate, with controls to avoid duplicate processing and clear escalation paths.
Validation With Defined Exception Handling
We apply agreed checks and decide which failures should stop a load, quarantine records, or raise an alert. Exceptions are recorded and routed to an owner rather than silently discarded.
Our Approach
How We Deliver Data Integration Consulting
Step 1: Map the Connections in Scope
We inventory the agreed source systems and consumers, along with their existing integrations, formats, volumes, and update needs, producing an integration map that shows gaps, duplicates, and fragile point-to-point links.
Step 2: Choose the Pattern per Flow
We choose hub-and-spoke, event-driven, or API-led patterns based on each scenario's requirements and select the tools to match, rather than forcing one approach onto every problem.
Step 3: Build and Test End to End
We build pipelines in increments and test them with representative volumes against agreed data-quality thresholds. Where applicable, testing covers source-to-destination reconciliation, schema changes, duplicate delivery, and recovery.
Step 4: Deploy and Establish Ownership
We deploy with monitoring, recovery runbooks, and agreed operating targets. Ownership, escalation, stabilization support, and any ongoing maintenance are defined before handover.
Should we use real-time or batch integration?
We assess how quickly each consumer needs updates, what the source can provide, and the cost of operating the flow. Some use cases need streaming or event-driven updates; others are better served by scheduled batches.
Can you replace our point-to-point integrations?
We assess which connections would benefit from redesign and migrate them in agreed phases. Cutover, reconciliation, rollback, and any parallel operation are planned to avoid conflicting updates or duplicate processing.
What happens when a source system changes its schema?
We define how supported schema changes are detected and handled. Depending on the change, a pipeline may continue, quarantine affected records, or stop and alert an owner. Dependency documentation and validation help assess downstream impact.
Do you work with our existing integration tools?
Usually, yes. If you have invested in Informatica, Azure Data Factory, or another platform, we build on it. When a replacement is worth considering, we weigh cost, supportability, and your team's operating skills alongside technical suitability.
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