Replace centralized data warehouses with distributed architecture. Cut cloud costs by 58%, reduce query latency by 94%, and process petabytes at the edge.
Every GB moved to the center burns money. Scaling vertically makes it worse.
Fortune 500 retailers: $10K-$50K/month in egress alone
Round-trips to distant data centers slow decisions and frustrate users.
Average query latency: 3.5 seconds (16× slower than edge)
Regional laws and global operations create legal exposure and operational overhead.
Fines up to €20M or 4% annual revenue
Every query requires expensive network traversal to central location Data transfer costs grow linearly with business success Response times degrade as concurrent users and data volume increase Regional teams wait for centralized resources during peak hours
Resilient distributed processing across locations
Sub-second local query response
Predictable per-node economics
Cloud-agnostic, works with existing tools
Horizontal scaling without architectural changes
10x Faster Queries
85% Less Data Transfer
Horizontal Scaling
Real-time inventory and sales insights per region
Process sensor data at edge, aggregate centrally
Comply with data residency while enabling global analytics
Query patient data locally, share aggregated insights
Analyze production data per facility in real-time
Process call records regionally, aggregate for planning
If your warehouse bills keep climbing while query times stay slow, let's talk. We work with Snowflake, Databricks, and BigQuery.