Source adapters
Implement API, file or database connectors with pagination, rate limits and incremental extraction suited to each source.
Data Integration
Data Integration
Connect applications, databases, APIs, files, and event sources through reliable pipelines and integration patterns with observable data movement.
Inside the delivery
Trace each record from source to destination
Agree record ownership, keys, direction, schema and freshness for each connected system. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Source contracts
Agree record ownership, keys, direction, schema and freshness for each connected system.
Output
Source-to-target mapping and contracts
- 02
Extract and transform
Build connectors and transformations that respect source limits and preserve required meaning.
Output
Connector code and transformation rules
- 03
Recoverable delivery
Handle checkpoints, duplicate records, schema changes and rejected data without silent loss.
Output
Replay and exception-handling procedures
- 04
Reconcile and monitor
Compare counts and control totals, monitor freshness and assign failures to named owners.
Output
Reconciliation checks and integration runbook
Controls across the workflow
- Record ownership
- Schema validation
- Replay controls
- Freshness alerts
Decisions that shape the scope
- Do we need an integration platform?
- Not always. Existing connectors or a small scheduled integration may fit. Choose a platform when the number of flows, change frequency and operating needs justify its cost and administration.
- How do you handle a source system changing without warning?
- By validating at the boundary. Schema expectations are declared, and an unexpected change fails the load loudly rather than propagating a null or a mistyped column into everything downstream.
Before you commit
Is this the right engagement?
- What we need from you
- Source and target schemas, credentials, rate limits, record identifiers, update frequency and reconciliation rules.
- How you accept the work
- Compare counts and control totals, monitor freshness and assign failures to named owners. Acceptance records the tested scope, unresolved issues and the owner's decision.
- Scope & alternatives
- Not always. Existing connectors or a small scheduled integration may fit. Choose a platform when the number of flows, change frequency and operating needs justify its cost and administration.
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Mapping and delivery semantics
Define keys, update direction and transformation rules. Treat deletes, late changes and duplicate delivery as explicit integration cases.
Exception recovery
Separate rejected records from successful loads and provide controlled replay with enough context to investigate failures.
Completeness and freshness
Add source-to-target reconciliation and freshness checks, with alert routing to the team responsible for each flow.
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Microsoft Azure
AWS
Databricks
Snowflake
dbt
Power BI and Tableau
Frequently asked questions
Questions to resolve before starting
01Do we need an integration platform?
Do we need an integration platform?
Not always. A handful of well-built, monitored pipelines is often better than a platform nobody has time to configure properly. Tooling starts to earn its cost once you have enough flows that consistency and central monitoring matter more than per-flow control.
02How do you handle a source system changing without warning?
How do you handle a source system changing without warning?
By validating at the boundary. Schema expectations are declared, and an unexpected change fails the load loudly rather than propagating a null or a mistyped column into everything downstream.
03Should integration be real-time?
Should integration be real-time?
Usually not. Streaming adds real operational complexity and is worth it only where a decision genuinely changes with fresher data. Most integration requirements described as real-time are satisfied by a more frequent, reliable batch.
04What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Source and target schemas, credentials, rate limits, record identifiers, update frequency and reconciliation rules.
05What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes reconciliation checks and integration runbook. Compare counts and control totals, monitor freshness and assign failures to named owners.
06How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Source and target schemas, credentials, rate limits, record identifiers, update frequency and reconciliation rules. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Service
Data Engineering
Service
Data Warehouse
Use case
Automated Reporting
Resource
Modern Data Platform Architecture
Engineering team
Hire Data Engineers
Solution
Invoice Processing Automation
Service
API Development & Integration
Service
Real-Time Data & Streaming
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for Data Integration.