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Data Pipeline Development
Ingestion, transformation, and loading pipelines built with Airflow, dbt, and the orchestration tools appropriate to your stack.
- Batch & streaming
- Idempotent execution
- Full observability
OSYSTIC
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When data infrastructure is unreliable or undocumented, every analytics and ML project becomes slower. We build pipelines that are tested, monitored, and structured for the teams that maintain them.
We scope around the system you actually need to operate, maintain, and operate — not a fixed vendor product or a one-size-fits-all implementation.
Ingestion, transformation, and loading pipelines built with Airflow, dbt, and the orchestration tools appropriate to your stack.
Dimensional modelling, partitioning strategy, and query optimisation for Snowflake, BigQuery, and Redshift.
Real-time event processing with Kafka and Flink for use cases that cannot wait for batch refresh cycles.
Automated quality tests, volume anomaly detection, freshness monitoring, and alerting — catching issues before they reach consumers.
Documentation of datasets, owners, and transformation lineage — so your team knows what exists and where it comes from.
Each engagement is broken into defined phases with reviewable outputs. Scope can adapt, but accountability stays visible.
Map your data sources, assess quality and completeness, and identify the gaps blocking downstream analytics and ML use cases.
Warehouse structure, pipeline design, and tooling selection — documented and reviewed before any build work begins.
Build ingestion from all sources — batch via Fivetran or custom ETL, streaming via Kafka or Kinesis — into the raw layer.
dbt or Spark models transforming raw data into staging and mart layers. Data tests at every layer catch issues before they reach consumers.
Automated quality tests — null rates, referential integrity, volume anomaly detection, freshness checks — with alerts before SLAs are missed.
Technology choices follow your environment, operating constraints, team capability, and long-term ownership requirements.
The same technical capability can require very different controls, integrations, and operating models across industries.
Transaction data pipelines, regulatory data warehouses, real-time fraud feature stores.
Clinical data lakes, FHIR pipeline integration, population health data infrastructure.
Unified commerce data platform, real-time inventory pipelines, personalisation feature stores.
Network event streaming, CDR processing pipelines, usage data warehouse.
The exact architecture and delivery plan depend on your environment. These answers describe how OSYSTIC approaches the work.
A warehouse stores structured, processed data optimised for querying. A lake stores raw data in any format — optimised for cost and flexibility. Lakehouses combine both. We recommend the right architecture for your actual use cases.
Yes, for most warehouse transformation work. dbt provides version control, documentation, testing, and lineage that makes transformation logic maintainable. We use it with your warehouse of choice.
With watermarks and late-event handling in Flink or Spark Structured Streaming. The tolerance window is defined based on your business rules and documented explicitly.
All pipeline code, dbt models, orchestration DAGs, data quality tests, architecture documentation, and runbooks. Your team can maintain and extend the infrastructure without us.
Tell us what data you have and what downstream teams are trying to do with it. We will come back with an architecture proposal.