Data Integration
Connect systems with REST APIs, GraphQL, Kafka streams, and ETL/ELT pipelines.
Diagnosis
What we fix first
Your systems don't talk to each other. Data is siloed, APIs are brittle, and manual syncs are error-prone.
Platform blueprint
From messy inputs to trusted decisions
Connect
Integrate systems via REST, GraphQL, webhooks, and message queues.
Orchestrate
Coordinate ETL/ELT flows with retries, backpressure, and scheduling.
Validate
Enforce schemas and contracts with reconciliation and alerting.
Sync
Keep systems consistent with idempotent, fully observable pipelines.
Delivery plan
How the work moves
Mapping: Document all systems, data flows, and integration points.
Design: Choose the right patterns (REST, GraphQL, Kafka, webhooks) based on latency and volume.
Implementation: Build robust APIs and event-driven pipelines with retries, idempotency, and monitoring.
Testing: End-to-end tests, load testing, and failure scenarios.
Results
Outcomes your team can measure
Reliable API integrations with error handling
Event streaming with Kafka or RabbitMQ
Data validation and schema enforcement
Monitoring and alerting for failures
Deliverables
What you receive
- REST/GraphQL APIs with documentation
- Kafka producers and consumers
- ETL/ELT pipeline code
- Monitoring dashboards (metrics, logs, traces)
- Integration tests and runbooks
FAQs
Practical questions
Can you integrate with legacy systems?
Yes. We've worked with SOAP, XML, and proprietary protocols.
How do you handle failures?
Retries, dead-letter queues, and circuit breakers. We design for resilience.
What about real-time integrations?
We use Kafka, webhooks, and WebSockets for low-latency data flows.