AI & ML · Data Engineering
Reliable data, reliable AI.
Lakehouse architectures, streaming pipelines and data-contracts that make ML and analytics dependable.
AI outcomes are bounded by data quality and freshness. We build modern data platforms — lakehouses, batch and streaming pipelines, data contracts and quality checks — that give ML and analytics teams the dependable inputs they need.
/ Capabilities
What's in scope.
Lakehouse architecture
Bronze/silver/gold layers on Delta / Iceberg / Hudi with governed schemas and time travel.
Batch & streaming pipelines
Airflow / Dagster for batch; Kafka / Flink / Kinesis for real-time; DBT for in-warehouse transforms.
Data contracts & quality
Producer-owned schemas, Great Expectations / Soda quality checks and lineage via OpenLineage.
Governance & catalog
Unity Catalog / DataHub / OpenMetadata for discovery, ownership and access.
Contracts move accountability upstream.
Data contracts push schema and quality accountability to producers, where breakage originates — instead of forcing consumers to reverse-engineer changes downstream.
Real-time when it earns its keep.
Streaming is powerful and operationally expensive. We use it where freshness materially changes decisions, and keep batch where it's cheaper and simpler.
/ Standards & Tooling
Common questions.
Lakehouse or warehouse?
Lakehouse when open formats, ML workloads and unstructured data matter; warehouse when the workload is predominantly SQL analytics. Many enterprises run both, with the warehouse consuming lakehouse gold tables.
Do we need real-time?
Only where decision latency justifies the cost. Fraud, personalisation and operational monitoring often do; monthly financial reporting does not.
Ready to scope a data engineering for ai engagement?
A senior practitioner — not a sales rep — will respond within one business day.
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