The Problem
The Cost of Vendor Noise.
Microsoft says Fabric does everything. Databricks says they handle all workloads. Snowflake claims they scale infinitely.
Organizations burn millions migrating to platforms that looked perfect in the sales demo but immediately hit performance scaling issues in production due to their unique pipeline architectures.
Missing Proof
The "Hello World" Demo
Vendor demos use perfectly clean CSVs to show how fast their pipeline engine runs. Your real data comes from an on-premise ERP heavily nested in XML with schema drift and duplicate keys.
Cost Opaqueness
Unpredictable TCO
Compute curves are intentionally complicated. It's nearly impossible to map your expected daily data processing volume to actual dollars without standing up the architecture and running your specific workloads.
System Agnostic. Objective Testing.
We evaluate the big three data platforms against your true technical capabilities, constraints, and budget.
Microsoft Fabric
SaaS Data Analytics
- Immediate time-to-value
- Native Power BI integration
- Unified pricing model
Best for: Power BI Heavy Orgs
Databricks
Unified Data AI
- Extreme scale custom pipelines
- Best-in-class Machine Learning
- Open-source underpinnings
Best for: Engineering & AI Focused
Snowflake
Cloud Data Cloud
- Decoupled compute and storage
- High concurrency handling
- Massive data sharing marketplace
Best for: Massive Analytical Concurrency
[ METHODOLOGY ]
Proof overpromises.
We execute a rapid, sprint-based approach to test your heaviest constraint on multiple platforms before making architecture recommendations.
Architectural Mapping
We map your current source systems, volume peaks, latency requirements, and team skillsets to form the baseline constraints.
Use Case Selection
Identifying the single hardest or most representative data pipeline to test—the one that usually breaks vendors.
Rapid Hands-On PoC
Executing a technical bake-off. We build the exact same pipeline across candidate tools to measure true latency and dev experience.
TCO & Recommendation
Analyzing the performance data, mapping it to your production scale, and projecting 3-year Total Cost of Ownership.
Evaluate With Evidence.
Connect with our architecture team to scope an objective platform evaluation tailored to your data environment.
Architecture Review
Initial Scoping Call
Define constraints
We review your volume, latency, and integrations.
Select candidate platforms
Determine if Fabric, Databricks, Snowflake, or others make sense to test.
Scope PoC timeline
Build a roadmap for performing the hands-on evaluation.