How long does a data warehouse migration typically take?

A full migration takes 3-6 months depending on complexity, but we deliver value incrementally. Most clients have their first critical data sources in production within 4-6 weeks, with expansion happening in parallel with business-as-usual operations.

Should we use a data lake or data warehouse?

Modern lakehouse architectures combine both. We typically recommend cloud warehouses (Snowflake, BigQuery) for structured analytics and Databricks/Delta Lake for ML workloads. The right choice depends on your data types, team skills, and use cases.

How do you handle data quality in pipelines?

We implement quality checks at every stage: schema validation on ingestion, data contracts between teams, anomaly detection on key metrics, and automated alerting. Issues are caught before they propagate, not after they've corrupted downstream models.

What's the difference between a feature store and a data warehouse?

Data warehouses serve analytics queries; feature stores serve ML systems. A feature store ensures that the features used to train a model are exactly the features served in production—eliminating training-serving skew, which is a major cause of ML model failures.

How do you approach real-time vs batch data processing?

Start with batch unless you have a clear real-time use case. Batch is simpler, cheaper, and sufficient for most analytics. When real-time matters (fraud detection, personalization), we implement streaming with Kafka or managed services, with batch as fallback.