Ask any team running AI in production what breaks most often. It is rarely the model. It is the pipeline feeding it — a schema change upstream, a silent null flood, a job that ran twice.
Principles that survive contact with production
Idempotent jobs, schema contracts, data-quality checks at ingestion, and observability on freshness and volume. Boring, disciplined engineering is what makes clever models trustworthy.
Batch, streaming, or both
Most enterprises need a lakehouse-style core with daily batch for analytics and selective streaming for the few use cases that genuinely need minutes-level latency. Buying streaming everywhere is an expensive fashion statement.
Our approach
Datastrel designs pipelines on PostgreSQL, AWS Glue, and modern orchestration with data contracts and automated tests, so your AI initiatives inherit clean, explainable inputs from day one.