Move every data point from source to destination reliably, at scale.
Faster data delivery vs manual export processes
Pipeline uptime SLA on managed deployments
Source systems connected on average per engagement
Connect to any source, APIs, databases, files, SaaS platforms, IoT feeds, with native connectors, managed connectors, or fully custom-built extractors.
Apply business logic, data cleaning, joins, aggregations, type casting, and enrichment in a managed, versioned transformation layer with full testing.
Deliver clean, structured data to your warehouse, data lake, CRM, BI tool, or any downstream system, in the format and frequency they require.
Schedule, sequence, and monitor all pipeline runs with automatic retry logic, alerting on failure, dependency management, and full run logging.
Replaced 180 SSIS packages and fragile SQL scripts at a manufacturing company with a modern Airflow + dbt stack, reducing pipeline failures from 15/month to zero and cutting maintenance time by 80%.
Connected 22 marketing platforms, paid, organic, CRM, and attribution tools, into a single unified marketing data warehouse, enabling cross-channel ROI analysis for the first time.
Built a real-time ELT pipeline synchronising inventory data across 8 warehouse management systems and an eCommerce platform, eliminating oversell events that previously cost £200K/year.
We bring deep domain knowledge across these sectors
Document all source systems, data volumes, update frequencies, and data quality issues.
Design the pipeline architecture, transformation logic, and load strategy for each destination.
Build and test each pipeline component with sample and full-volume data validation.
Deploy to production with full monitoring, alerting, and runbook documentation.
ETL transforms data before loading it to the destination. ELT loads raw data first and transforms it inside the warehouse. We recommend ELT for most modern cloud warehouse projects as it is faster, more flexible, and easier to maintain.
Yes. We regularly modernise legacy ETL built in SQL scripts, SSIS, Informatica, or custom code, replacing fragile, undocumented processes with robust, monitored, maintainable pipelines.
We design pipelines with rate limit awareness built in, request throttling, retry logic, and backoff strategies so pipelines run reliably regardless of API constraints.
We build pipelines with change detection and schema drift alerting. When a source API changes, we are notified immediately and can update the pipeline before it affects downstream data.
Every pipeline includes row count validation, null checks, data freshness monitoring, and reconciliation checks against source system totals. Issues are caught and alerted before data reaches your analysts.
Yes. We build both batch and streaming pipelines depending on your latency requirements. For use cases requiring sub-minute data freshness we use Kafka, Kinesis, or cloud streaming services.
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