Data pipeline development
We build data pipelines that move data from your operational systems into warehouses, reports and AI features, on schedule and with quality checks along the way. Your teams stop reconciling conflicting numbers and start trusting the dashboards they use to decide.
What it is and when it fits.
Data pipeline development covers extracting data from source systems, transforming it into consistent models and loading it where it is needed, whether that is Snowflake, BigQuery, Postgres or a vector index for AI search. We build batch pipelines with Airflow for scheduled workloads and event streams with Kafka where data has to arrive within seconds. Tests, freshness checks and lineage are part of every pipeline, so problems surface before they reach a report.
Pipelines are worth building when numbers are assembled by hand in spreadsheets, when departments disagree on basic figures, or when you want AI features that need clean, current company data. Managed connector services handle standard SaaS sources well, and we use them where they fit. Custom pipelines matter for internal systems, complex transformations and cases where data must stay within your own environment.
What we build.
ETL and ELT pipelines
Scheduled loads from ERP, CRM, finance and product databases into your warehouse, with retries and alerts when a source fails.
Data warehouse modelling
Clear, documented data models for revenue, customers, operations and finance that every report draws from.
Event streaming
Kafka streams that carry orders, status changes and sensor data between systems in near real time.
Data quality checks
Automated tests for completeness, duplicates, freshness and value ranges, with alerts routed to the right owner.
Data for AI features
Pipelines that clean, chunk and embed documents and records into vector search, kept in sync as the source changes.
How it works.
- 01
Audit sources and needs
We list the questions the business needs answered, the systems holding the data and the quality issues already known.
- 02
Design the data model
We agree definitions for key metrics and entities, and choose batch, streaming or a mix for each source.
- 03
Build and validate
Pipelines are built with tests and compared against existing reports until the numbers reconcile.
- 04
Operate and extend
Monitoring, alerting and runbooks go live with the pipelines, and new sources are added as the business asks for them.
Built with.
All technologiesFurther reading.
Common questions.
ELT, loading raw data first and transforming it inside the warehouse, suits most modern cloud warehouses and keeps history available. ETL still makes sense when data must be cleaned or anonymised before it leaves the source environment. Many setups use both.
Snowflake and BigQuery suit larger analytical workloads, while Postgres is often enough for mid-sized companies and keeps costs predictable. We base the choice on data volumes, existing cloud contracts and the skills of your team.
Every pipeline has automated tests, freshness checks, retries and alerts, and runs from version-controlled code with CI. When something fails, the right person hears about it before a stakeholder opens a stale dashboard.
AI features are only as good as the data behind them. Pipelines keep documents and records clean, deduplicated and current in the stores that retrieval and agents read from, with permissions carried along from the source.