Data people trust: collected from every system, checked, documented and delivered to the reports and tools that need it.
What we build
- Pipelines from every source
- Warehouses and lakehouses
- Event streaming
- Quality checks and lineage
Works with
Apache Airflow
Apache Spark
Snowflake
BigQuery
Amazon Redshift
Databricks
PostgreSQL
Power BI
Looker
Pipelines / 02
Every source,
one truth.
- 01
Ingestion
APIs, databases, files and streams brought in on a schedule or as they change.
- 02
Orchestration
Each job run with retries, alerts and tests, in Airflow or Prefect.
- 03
Warehouses and lakehouses
Snowflake, BigQuery, Redshift or Databricks, modelled for the questions people ask.
- 04
Quality and lineage
Checks at every stage, and a record of where each number came from.
- 05
Reporting
Dashboards in the BI tool you already use.
- 06
Governance
Access controls and the documentation reviewers expect.
Event Streaming / 03
Data that moves
as it happens.
- 01
Kafka and messaging
Kafka, RabbitMQ or your cloud's queues, run with schema rules and dead-letter handling.
- 02
Real-time analytics
Streams processed for live dashboards and alerts.
- 03
Event-driven systems
Services that react to events, with ordering and replay handled.
- 04
Across regions
Messages replicated between regions and providers.
Process / 04
The question first,
then the data.
- STEP-1
Assess
Sources, quality and the decisions the data must support.
- STEP-2
Design
Pipelines, models and the platform to run them.
- STEP-3
Build and test
Delivered in increments, with lineage checked.
- STEP-4
Hand over or run
Your team runs it, or we do.
Which number do you not trust?
Start there. We will trace it to its source and propose the fix.
All solutions