We’ve delivered platforms that meet HIPAA, GxP, and SOC 2 requirements while enabling experimentation, analytics, and ML workflows at scale.
Designed for engineers, analysts, scientists, and auditors to work in parallel — without conflict, bottlenecks, or security gaps.
Our teams handle ingestion, partitioning, pipeline orchestration, and query optimization — and have built lakehouses that support billion-row queries and real-time ML retraining.
We work across Snowflake, BigQuery, Redshift, Iceberg, and Delta Lake — in AWS, Azure, or hybrid setups — without pushing a specific vendor lock-in.
Depends on your workloads, users, and goals. We often recommend a lakehouse approach that combines storage scale with analytics performance.
Yes. We’ve handled schema migration, workload refactoring, and cost-performance optimization across regulated and legacy systems.
We configure autoscaling, suspend/resume, tagging, query governance, and FinOps dashboards — and train your teams to manage usage over time.
Fully. We've structured data for vector search, prompt generation, fine-tuning, and retrieval-augmented generation — all with traceability.
dbt, Airflow, Spark, Kafka, Fivetran, Great Expectations, OpenLineage — and native integrations across cloud platforms.
With a $0 Data Platform Strategy Session to assess current systems, challenges, and roadmap options.
