We design and implement robust data pipelines that gather data from multiple sources and clean and unify it into a central store. Data-quality checks and monitoring catch failures early. The architecture is built with PDPL requirements and in-Kingdom data residency in mind.
What's included
- Design of ETL and ELT pipelines matched to the nature of each source system
- Scheduled batch processing alongside real-time streaming for continuously changing data
- Integration across diverse sources including databases, APIs, flat files, and ERP systems
- Pipeline orchestration and scheduling with tools such as Apache Airflow, and transformation management in dbt
- Data-quality controls with validation rules and detection of anomalies and duplicate records
- A structured warehouse or lakehouse built as a single source of truth
Methodology & standards
Analyse source systems, map data flows, and define the required refresh cadence
Design the target data model and select the processing pattern (batch or streaming) per route
Build and automate the pipelines with a documented, reusable transformation layer
Establish quality controls, monitoring, and early alerting on failures or flow interruptions
Hand over with documented data lineage and recovery and restart procedures
Deliverables
- Automated, production-ready data pipelines
- A unified warehouse or lakehouse with a documented data schema
- A version-controlled transformation layer with data-quality tests
- A monitoring dashboard for pipeline health with automated failure alerts
- A runbook documenting sources, schedules, data lineage, and recovery procedures
Regulatory controls it satisfies
Typical timeline
A typical engagement runs six to twelve weeks, depending on the number of sources, data volume, and transformation complexity.
Common questions
What is the difference between ETL and ELT, and which do you choose?
We select the pattern by source and destination. We favour ELT when a modern warehouse can run transformations efficiently in place, and ETL when data must be cleansed or anonymised before loading. The decision rests on performance, cost, and privacy requirements.
Do you support real-time streaming or only batch?
Both. We use batch processing for periodic reporting and real-time streaming where fresh data is essential, such as monitoring and alerting, and we often combine the two on the same platform.
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