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
DATA & CLOUD / SYSTEM NODEData Infrastructure
Data infrastructure the rest of the organisation can build on.
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.
Ownership, deployment boundary, acceptance criteria, and handover are defined per engagement.
DATA PIPELINE
WHAT WE BUILD
Data Infrastructure, engineered as an operating system.
Scope follows the real workflow, environment, failure modes, and ownership model rather than a fixed vendor package.
Data Warehouse Design
Dimensional modelling, partitioning strategy, and query optimisation for Snowflake, BigQuery, and Redshift.
- Dimensional modelling
- Partition strategy
- Query optimisation
Streaming Pipelines
Real-time event processing with Kafka and Flink for use cases that cannot wait for batch refresh cycles.
- Apache Kafka
- Apache Flink
- Late-event handling
Data Quality Framework
Automated quality tests, volume anomaly detection, freshness monitoring, and alerting — catching issues before they reach consumers.
- dbt tests
- Great Expectations
- Freshness SLAs
Data Catalogue & Lineage
Documentation of datasets, owners, and transformation lineage — so your team knows what exists and where it comes from.
- Column-level lineage
- Dataset ownership
- Freshness docs
DELIVERY SEQUENCE
From unknowns to production evidence.
01
Data Audit
Map your data sources, assess quality and completeness, and identify the gaps blocking downstream analytics and ML use cases.
All Data Sources Audit Report02
Architecture Design
Warehouse structure, pipeline design, and tooling selection — documented and reviewed before any build work begins.
Audit Report Architecture Doc03
Ingestion Pipelines
Build ingestion from all sources — batch via Fivetran or custom ETL, streaming via Kafka or Kinesis — into the raw layer.
Architecture Raw Layer04
Transformation
dbt or Spark models transforming raw data into staging and mart layers. Data tests at every layer catch issues before they reach consumers.
Raw Layer Mart Layer05
Data Quality
Automated quality tests — null rates, referential integrity, volume anomaly detection, freshness checks — with alerts before SLAs are missed.
Mart Layer Quality Framework06
Serve & Monitor
Warehouse and lake served to BI tools, ML pipelines, and applications. Pipeline monitoring dashboard tracks every run.
Quality Layer Stable InfrastructureTECHNOLOGY SURFACE
Tools selected for the system, not the trend.
Technology follows environment, operating constraints, team ownership, and long-term maintainability.
01Ingestion
Fivetran · Airbyte · Stitch · Custom Python
02Transformation
dbt Core · Apache Spark · SQL · PySpark
03Streaming
Apache Kafka · AWS Kinesis · Apache Flink
04Warehouses
Snowflake · BigQuery · Redshift · Databricks
WHERE IT FITS
The same capability changes by domain.
Finance
Transaction data pipelines, regulatory data warehouses, real-time fraud feature stores.
Healthcare
Clinical data lakes, FHIR pipeline integration, population health data infrastructure.
Retail
Unified commerce data platform, real-time inventory pipelines, personalisation feature stores.
Telecom
Network event streaming, CDR processing pipelines, usage data warehouse.
TECHNICAL FAQ
Questions worth resolving before delivery.
01What is the difference between a warehouse and a data lake?+
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.
02Do you use dbt for transformations?+
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.
03How do you handle late-arriving data in streaming pipelines?+
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.
04What is handed over at project end?+
All pipeline code, dbt models, orchestration DAGs, data quality tests, architecture documentation, and runbooks. Your team can maintain and extend the infrastructure without us.
OSYSTIC / PROJECT CHANNELOPEN
START A PROJECT
Need reliable data infrastructure?
Tell us what data you have and what downstream teams are trying to do with it. We will come back with an architecture proposal.