P + C + T Complexity Model

How DataSurface transforms exponential pipeline complexity into linear, manageable connections

❌ Traditional Approach

Point-to-point pipeline chaos

🗄️ 📡 📁 📊 🤖 📈 Producers Consumers

Every producer builds custom pipelines to every consumer. Different teams use different technologies, formats, and schedules. Over time, this becomes unmaintainable "pipeline spaghetti."

P × C
Connections grow exponentially

✓ DataSurface Approach

Centralized data logistics

🗄️ 📡 📁 DATASURFACE 📊 🤖 📈 Producers Consumers

Producers connect to DataSurface once. Consumers connect to DataSurface once. The platform handles all the logistics—routing, transformation, governance, and delivery.

P + C
Connections grow linearly

📊 Real-World Example

Producers (P) 50 data sources
Consumers (C) 100 applications
Traditional (P × C) 5,000 pipelines
DataSurface (P + C) 150 connections
Complexity Reduction 97% fewer pipelines

🚀 An Enabler, Not a Replacement

DataSurface makes building solutions on Snowflake, Databricks, and other platforms easier and faster—while keeping your options open.

Faster Onboarding

New data sources connect to DataSurface once, instantly available to all your platforms—Snowflake, Databricks, or any other.

🏆

Best of Breed

Use multiple platforms concurrently. Snowflake for analytics, Databricks for ML, Postgres for operations—all from the same data.

📊

Commercial Flexibility

Swap platforms over time as it makes business sense. Renegotiate contracts from a position of strength.

The data landscape evolves. HDFS gave way to S3 + Spark. Impala came and went. Then came the cloud data warehouses. The next shift is always around the corner.

With DataSurface, adopting new technology is just adding another consumer—not a multi-year migration.

AWS Postgres RDS On-Premise Oracle DB DATASURFACE Snowflake Analytics Databricks ML / AI Azure Synapse Next Platform... Data Sources Use them all. Concurrently.

What About Transformers (T)?

DataTransformers are a special type of consumer that also produces data. They consume raw data from DataSurface, add value through transformation, and publish derived datasets back into the platform.

📦
Raw Data
(in DataSurface)
⚙️
Transformer
(Consumer + Producer)
Derived Data
(back to DataSurface)

🔄 Dual Role

Transformers consume existing data and produce new, value-added datasets—all through the same DataSurface connection.

📐 Still Linear

Adding transformers doesn't create exponential complexity. Each transformer is just another participant in the P + C + T model.

🏛️ Governed

Derived data inherits governance policies from source data. Full lineage tracking shows exactly how data was transformed.

Ready to Simplify Your Data Architecture?

Stop building pipeline spaghetti. Let DataSurface handle the logistics.

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