Data,
moving across systems.
Batch and streaming ingestion, CDC, event streams and reverse-ETL, engineered as a platform, with contracts, quality and reconciliation from day one.
Data integration is the plumbing every analytics and operational decision depends on. When it's engineered, decisions get made faster. When it isn't, every dashboard becomes a debate.
Ingest honestly
Batch, CDC and streaming ingestion, chosen against decision latency, not architectural taste.
Fivetran and Airbyte where they earn their keep. Debezium and Kafka where CDC and streaming matter. Custom services where volume, latency or logic demand it.
Freshness is designed against the decisions it informs, not against a spec sheet.
Data contracts are the highest-leverage engineering investment in most data platforms.
Contract & model
Contracts between producers and consumers. Modelled data with tests, freshness SLAs and lineage.
Data contracts formalise schemas, semantics, SLAs and ownership. Producers know who consumes; consumers know what they can trust.
Modelling is the layer where trust is built or lost. We invest accordingly.
Activate
Reverse-ETL and event streams push governed data back to the systems that act on it, not just to dashboards.
CRM, ad platforms, ops tooling and product surfaces receive governed data via reverse-ETL or events. Insight leaves the warehouse and lands where the work happens.
Activation is the difference between data as reporting and data as operating leverage.
Operate
Data quality, freshness, lineage and cost monitored as ongoing engineering. Incidents triaged like software.
Monte Carlo, Great Expectations and OpenLineage as substrate. Incidents get owners, runbooks and post-mortems.
Trust in the data compounds instead of erodes.
Toolkit
Framework, platform and vendor selection is driven by the shape of the work and the team who'll own it, not by whichever ecosystem is loudest this year. The list below is representative, not prescriptive.
- Ingest
- Fivetran · Airbyte · Debezium · Custom
- Streaming
- Kafka · Flink · Kinesis
- Warehouse
- Snowflake · Databricks · BigQuery
- Modelling
- dbt · SQLMesh
- Quality
- Monte Carlo · Great Expectations · OpenLineage
Selected work
What clients ask us first
01Batch or streaming?
By decision latency. Micro-batch on the warehouse serves most workloads. Streaming pays off when the business cost of stale data is real, fraud, personalisation, operational alerting.
02Fivetran, Airbyte or custom?
Fivetran for reliability with breadth of connectors and budget. Airbyte for open-source flexibility. Custom where volume, latency, complex transformations or licence economics demand it.
03How do data contracts work in practice?
As versioned artefacts owned jointly by producer and consumer teams. Schema, semantics, SLAs and ownership. Enforced in CI. Changed via deprecation windows.
04How do you handle reverse-ETL?
As a first-class pattern, Hightouch, Census, or custom services. Governed data pushed back to the systems that act on it. Insight leaves the warehouse.
Let's discuss your data-integration platform.
Share a brief overview of the sources and consumers, we'll arrange an introductory conversation with the relevant practice leadership.