Data Engineering
Most analytics and AI projects stall on data quality. We build the infrastructure that makes data reliable, traceable and reproducible.
Within an organisation, data rarely sits in a single place: some of it lives in relational databases, some in Excel files, some in the APIs of third-party services. Our data engineering work brings these sources together in one warehouse through pipelines that run on a dependable schedule.
Every pipeline we build is observable: which data arrived when, which transformation it passed through, where it failed. A data pipeline that breaks silently is more dangerous than no pipeline at all.
What we do in this area
ETL / ELT pipelines
Data pipelines scheduled with Airflow, safe to re-run and raising alerts whenever something fails.
Data warehouse design
Dimensional modelling, slowly changing dimension (SCD) history tracking and a schema optimised for analytical queries.
Stream processing
Kafka-based real-time data streams; instant processing of sensor and event data.
Spatial database
Indexed and partitioned databases on PostGIS that hold millions of geometries.
Data quality
Schema validation, outlier detection and automated quality tests; corrupt data is caught before it ever enters the warehouse.
Data governance
Data dictionary, lineage tracking and access authorisation.
Where does it deliver value?
ETL pipelines, data warehousing, stream processing and large-scale spatial database design.
Let's discuss this-
Consolidating data from different departments in a single warehouse
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Daily automated reporting infrastructure
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Continuous collection of sensor and IoT data
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Preparing training data for AI models
Why Türkol?
We build data pipelines that do not merely "look like they are running" but "tell you when they break". Monitoring and alerting are an inseparable part of every delivery.