Data integration services for reliable automation and reporting.
Automation amplifies whatever the data already is. Map the entities, define ownership, and build ingestion and reconciliation that can be observed and replayed.
- Shared data contract
- One
- For every data field
- Owner
- Before bad data spreads
- Checks
- Safe retries
- Replay
Overview
What Data Integration has to solve.
A report or automation nobody trusts because the underlying data is unreliable. The useful questions are about ownership, failure paths, and what the team can operate after launch.
What good looks like
Every field has an owner Bad data is caught at the edge Replays are safe Lineage is visible Reconciliation appears in its own report
Ownership before pipelines
Data integration starts with a question of ownership: who owns this field? If the answer is everyone, it is nobody. The pipeline will copy the disagreement into every downstream system.
We map entities and owners before building. We aim for one owner per field, an agreed definition, and a documented exception path. Once that is settled, the pipeline work is straightforward.
- One owner per entity and field
- Agreed definitions for shared metrics
- Documented exceptions and overrides
- A source of truth per system
Quality checks are part of the pipeline
A pipeline can run successfully and still produce bad data. Freshness, nulls, duplicates, and referential integrity need explicit checks with visible failures.
We treat data quality as a release gate and an operating metric. When a check fails, the team knows which downstream report or automation is affected and what to do next.
- Freshness and volume checks
- Null and referential integrity rules
- Failure alerts tied to consumers
- Safe replay and backfill
What a useful data integration brief should settle
A data integration brief should name the source systems, the entities they own, and the consumers that depend on the result. It should also define which system is authoritative when values disagree.
Quality checks, retries, replay, and reconciliation belong in the design. A pipeline that moves data without explaining failures creates a new operational problem.
- Sources, entities, and system-of-record ownership
- Contracts, transformations, and quality checks
- Retry, replay, and reconciliation behaviour
- Monitoring, access control, and support ownership
Why teams call us
What usually goes wrong.
These are the recurring problems behind a data integration brief. Naming them early changes the architecture, the estimate, and the launch plan.
The same customer exists five times
Each system has its own identifier and its own truth. Reporting struggles to agree on a basic count.
Nobody owns the field
A critical attribute is updated by three teams and trusted by none. The definition lives in someone's head.
The pipeline fails silently
A job stops running and the dashboard looks fine because it is showing yesterday's data.
AI is built on dirty data
An automation or model is expected to work on records that are duplicated, stale, or missing.
Scope
What the project includes.
Every data integration project is scoped around a working system and the decisions that keep it healthy.
Entity and ownership mapping
A shared model for customers, orders, products, and the fields that matter, with an owner for each.
Ingestion and transformation
Batch or event ingestion, schema handling, and transformation logic that is documented and testable.
Reconciliation and quality checks
Row counts, freshness, null checks, referential integrity, and the business rules that catch bad data early.
Lineage and monitoring
Where each field came from, how it changed, and when the pipeline last succeeded.
Replay and backfill
A safe way to reprocess history without duplicating or corrupting downstream data.
Warehouse and reporting models
A curated layer for analytics and automation, with definitions the business can agree on.
Use cases
Where this service helps most.
The same service looks different in each company. These are the situations where it matters most.
A dashboard disagrees with finance
The numbers are close but not the same. The team spends the meeting arguing about the definition when they should be making the decision.
A shared entity model and reconciled reporting layer
A CRM sync is unreliable
Leads, accounts, and activities sync inconsistently. Sales sees stale data and stops trusting the system.
Observable sync with retries and quality checks
An AI workflow needs clean inputs
The automation works in a demo but fails on real records because the source data is incomplete or duplicated.
A quality-gated pipeline before the automation
Process
Four stages. Working software at every review.
Discovery leads to an architecture decision, a build with visible increments, and an operating model that stays after we leave.
What stays constant
One accountable lead, a working demo at each review, and documentation from the first release. Scope and risk set the plan, and we will say when a smaller project is the better answer.
- 01
Map
Inventory the sources, entities, definitions, owners, and the reports or automations that depend on them.
Output: Source map, entity ownership, quality baseline
- 02
Design
Define the target model, ingestion pattern, transformations, checks, and replay strategy.
Output: Data architecture, quality rules, migration plan
- 03
Build
Implement ingestion, transformation, reconciliation, lineage, and the curated reporting layer.
Output: Pipelines, tests, dashboards
- 04
Operate
Monitor freshness and quality, review exceptions, and expand the model as the business changes.
Output: SLOs, quality review, roadmap
Stack & standards
Chosen for durability.
Fewer moving parts, clear ownership, and a handover your engineers can defend. The stack follows the constraint.
dbt
Tested, documented transformations
Snowflake
Curated analytical data
Fivetran
Managed source ingestion
PostgreSQL
Operational data and reconciliation
Great Expectations
Data quality checks
OpenLineage
Lineage and pipeline visibility
How we choose
ETL versus ELT
ELT is usually simpler when the warehouse can handle the transformation. ETL remains useful when the data must be transformed before it lands or when the volume is constrained.
Batch versus streaming
Batch is simpler and often sufficient. Streaming makes sense when the workflow needs a real-time reaction and the operational cost is justified.
One warehouse versus a mesh
A central warehouse is easier for most teams. A data mesh can work for large organisations with strong domain ownership, but it still needs governance.
Measured outcomes
“They rebuilt the core without downtime. The close now runs from one reconciled ledger.”
FAQ
Questions we hear often.
The questions buyers ask before a data integration project. If yours is not here, ask it directly.
Ask an engineer01 Do we need a data warehouse?
If reporting and automation depend on multiple operational systems, a curated warehouse usually pays for itself. A smaller team may start with a simpler model and add the warehouse when the reconciliation cost is visible.
02 Can you integrate a legacy system with no API?
Often. We use database bridges, file exchanges, message queues, or an anti-corruption layer. The constraint is isolated and documented so it does not spread.
03 How do you handle data quality?
We define checks for freshness, nulls, duplicates, referential integrity, and business rules. Failures are visible, owned, and tied to the downstream consumer.
04 How do you keep source systems and reports reconciled?
We define the system of record per entity, run quality checks at the edge, and retain enough lineage to replay or reconcile a batch. Discrepancies surface as exceptions with an owner, so drift does not stay silent.
05 Can you make the pipeline replayable?
Yes. We design idempotent loads, versioned transformations, and a backfill process that does not duplicate downstream data.
Next step
What report or automation is being blamed for the data?
Tell us about the sources, the owners, and the reconciliation that keeps failing. A senior engineer will reply with a data plan.
Scope data integration