Data integration: connect every system, reliably
Veratas builds the integration layer that moves enterprise data reliably between ERP, CRM, SaaS apps, files, and databases. Using Azure Data Factory, Microsoft Fabric Data Pipelines, change data capture, and API integration, we deliver pipelines that are monitored, recoverable, and built to survive real-world data.
Why most integration breaks in production
A pipeline that works in a demo and a pipeline that survives a year of production are different things.
Enterprise data lives in a dozen systems that were never designed to talk to each other: an ERP for finance and operations, a CRM for sales, SaaS tools for marketing, support, and HR, plus files and databases that accumulated over years. Integration is the layer that moves data between them. The hard part is rarely the happy path. Any tool can copy a table once. The difficulty is what happens when a source API times out, a file arrives malformed, a schema changes without warning, or a load runs twice.
Fragile integration fails quietly. A pipeline silently stops, nobody notices for three days, and a report goes to the board with stale numbers. Or a load half-completes, leaving duplicate rows, and someone spends a weekend untangling which records are real. These are not exotic failures. They are the normal weather of production data, and integration that ignores them is integration that will eventually embarrass someone.
We build for the second case from the start. That means idempotent loads, so a re-run after a failure is always safe and never doubles the data. It means retry logic with sensible backoff for transient errors, and clear alerting with enough context to diagnose a real failure fast. It means full run history, so you can always see what moved, when, and whether it succeeded. Reliability is designed in, not bolted on after the first incident.
Connect two systems, from $5,000
A fixed-scope integration between two systems, mapped, scheduled, and monitored. You see exactly what the price covers, and a fair quote for anything more.
Fixed fee from $5,000 | Live in 3 to 4 weeks | Price shown up front
Two systems, talking to each other
A reliable, monitored pipeline between two of your systems.
- A pipeline connecting two systems
- Field mapping and transformation rules
- Scheduled or triggered runs
- Error handling and basic monitoring
- Logging and alerting
- Documentation and handover
Any platform or connector licences are billed separately. The $5,000 covers the implementation services.
What is in the $5,000, and what we quote separately
Anything beyond the standard package is optional, and always quoted before you commit.
| In your $5,000 | Beyond the package, quoted at a fair rate |
|---|---|
| Two systems, one pipeline | More systems and pipelines |
| Field mapping and transformations | Complex transformation logic |
| Scheduled or triggered runs | Real-time and event-driven integration |
| Error handling and monitoring | Custom connectors and APIs |
| Documentation | Master data management |
You pay $5,000 for the standard integration. Everything else is optional, scoped and quoted transparently at a reasonable rate, and always shown before you decide.
Reliable integration across the estate
We connect systems with pipelines that are observable, recoverable, and built to handle the data you actually have.
Pipeline development
Ingestion pipelines on Azure Data Factory and Microsoft Fabric Data Pipelines, batch and incremental, with retry logic, alerting, and full run history. Each pipeline is built so a failed run can be diagnosed and re-run safely.
Change data capture
CDC-based incremental loads that move only the rows that changed since the last run. This keeps synchronisation from large ERP and transactional databases fast, low in cost, and low in latency, instead of reloading whole tables.
API and SaaS integration
Integration with SaaS platforms and line-of-business systems via REST APIs, native connectors, and Azure Logic Apps. We handle authentication, token refresh, pagination, and rate limits, and build pipelines to tolerate API changes.
Real-time and streaming
Event and streaming integration via Fabric Real-Time Intelligence and Azure services for use cases that genuinely need near-real-time data movement, such as operational dashboards, rather than forcing everything through a batch schedule.
Change data capture and metadata-driven pipelines
Two design choices do most of the work in keeping integration efficient and cheap to extend.
Full reloads do not scale. Copying an entire transactional table every night may work while it holds fifty thousand rows, but at fifty million it becomes slow, expensive, and a heavy load on the source system at exactly the wrong time. Change data capture solves this by identifying only the rows inserted, updated, or deleted since the last run. The pipeline moves a small delta instead of the whole table, which cuts run time and cost and lowers latency, so downstream reports are fresher. For large ERP sources, CDC is usually the difference between integration that keeps up and integration that falls behind.
The second choice is to build pipelines as metadata-driven, parameterised patterns rather than hand-coding each source. Instead of one bespoke pipeline per table, we build a small set of reusable patterns whose behaviour, source, target, keys, and load type, is driven by a control table. Adding the next source then becomes a configuration entry, not a development project. This keeps the estate consistent, makes onboarding new data fast, and means a fix to a pattern improves every pipeline that uses it.
On top of both, observability is treated as a feature, not an afterthought. Every run writes to a log: rows read, rows written, duration, outcome. Failures raise alerts that point to the specific pipeline and error rather than a vague notification. Data lineage is captured so you can trace a figure in a report back through each hop to its source. When something does go wrong, the answer is visible in minutes, not reconstructed from guesswork.
Integration delivered with discipline
Integration work runs through five phases so pipelines end up reliable, not merely functional in a demo.
Map
Source and target inventory, data-flow mapping, and volume and latency requirements for each feed. The output is an integration architecture that shows what connects to what and how often.
Design
Pipeline patterns, error-handling and retry strategy, idempotency approach, secure credential management, and a monitoring and alerting design. Decisions are made here so the build is consistent.
Build
Pipeline development using parameterised, metadata-driven patterns, delivered and demonstrated in agile sprints. Reusable patterns mean later sources are configured rather than rebuilt.
Validate
End-to-end testing, deliberate failure-mode testing, reconciliation against sources, and performance validation at production data volumes, so behaviour under stress is known before go-live.
Operate
Monitoring, alerting, and pipeline maintenance under managed services. As schemas and APIs change over time, pipelines are adjusted before they break a report.
Why clients choose Veratas for data integration
The goal is integration that quietly works for years, not integration that needs a specialist on call every week.
Built to recover
Pipelines are designed with retry logic, alerting, and idempotent loads, so a re-run is always safe. When a source fails, recovery is automatic or obvious, not a two in the morning mystery for whoever is on call.
Observable
Full run history, data lineage, and contextual alerting mean you always know what moved, when, and whether it succeeded. Diagnosing an issue is a matter of reading the logs, not reconstructing events.
Reusable patterns
Parameterised, metadata-driven pipelines mean adding the next source is a configuration change, not a rebuild. The estate stays consistent and onboarding new data is fast and low risk.
Microsoft data stack
Integration on Azure Data Factory and Fabric Data Pipelines, native to the platform your warehouse and Power BI reporting already run on, so there is no separate tool to govern, license, or learn.
FAQ
Frequently asked questions
Quick answers to questions you may have. Can't find what you're looking for? Check out our full documentation.
Connect your systems with integration that lasts
Reliable, observable pipelines are the difference between a data estate that works and one that constantly breaks. Start with a conversation about your source systems.