BigQuery vs Snowflake vs Redshift — which should we choose?
BigQuery wins for serverless simplicity and spiky workloads, Snowflake for multi-cloud flexibility and data sharing, Redshift when you are deeply AWS-committed with steady workloads. We are production-certified across all three and recommend after profiling your query patterns and cost model — not by vendor preference.
What is a data lakehouse and do we need one?
A lakehouse combines cheap object storage with warehouse-grade ACID tables (Delta Lake, Apache Iceberg). It fits teams with mixed workloads — BI, ML, streaming — on the same data. If you only run SQL analytics under ~10TB, a plain warehouse is often simpler and cheaper.
How long does a warehouse migration take?
Our 2TB+ Redshift-to-BigQuery migration completed in 14 weeks including a redesigned dbt layer and FinOps dashboards. Typical ranges: 8–12 weeks for a single-warehouse lift-and-improve, 4–6 months for multi-source platform rebuilds with parallel-run validation.
How do you control warehouse costs?
Partitioning and clustering, materialized aggregates, reservation/auto-scaling strategy, lifecycle tiering, and per-team cost attribution dashboards. FinOps is built into every design — one engagement cut total cost of ownership 62% and saves $125K annually.
Do you handle both structured and unstructured data?
Yes — relational sources, semi-structured JSON/Avro, documents, search indexes, and geospatial data. Our geospatial lakehouse engagement processes high-cardinality spatial telemetry for AI-driven property valuation.