The Walled Gardens Are Back (And This Time It's Your Data)

Think Amazon for Data (The eCommerce Site, Not AWS Cloud)

Everyone understands how Amazon.com works. Millions of sellers list products. Millions of buyers specify delivery requirements—1-hour, overnight, 2-day shipping. Amazon handles all the logistics: warehouses, inventory management, route optimization, trucks, last-mile delivery.

Neither buyers nor sellers see the complexity underneath. Sellers don't care which warehouse holds their inventory or which truck delivers it. Buyers don't care about Amazon's logistics algorithms or fulfillment center locations. They just specify what they need and when they need it.

Amazon continuously improves logistics—faster delivery, lower costs—without sellers or buyers noticing. New technologies get adopted transparently. The service just gets better and cheaper over time.

DataSurface: Amazon for Enterprise Data

DataSurface brings the same logistics model to enterprise data:

• Data Producers = Sellers: "I have this data available. Here's what it looks like."
• Data Consumers = Buyers: "I need this data with these requirements (low latency, 5-year retention, forensic history)."
• DataSurface = Amazon Logistics: Handles ingestion, transformation, replication, schema evolution, governance.

Just as Amazon abstracts logistics complexity from both parties, DataSurface abstracts data infrastructure complexity. Producers don't worry about which consumers need their data or how to deliver it. Consumers don't worry about ingestion pipelines or database technologies. DataSurface handles the logistics.

The Technology Underneath Can Evolve

Amazon didn't start with same-day delivery or drone delivery. They improved logistics over time without breaking the experience for sellers or buyers. The "add to cart → checkout → wait for delivery" workflow remained the same even as the underlying systems radically evolved.

DataSurface works the same way. The data model stays stable while the infrastructure underneath evolves. Start on Postgres, migrate to SQL Server, add Oracle consumers—all without changing the producer or consumer workflows. Technology improvements become transparent upgrades, not disruptive migrations.

Why This Matters Now

Today's data landscape is dominated by vendor lock-in. Big Data Vendors want you to move all your data into their proprietary platforms and pay them to control access forever. When they raise prices 3x (and they will), or when better technology emerges, you're trapped in a $10M+ migration project.

Just as Amazon democratized eCommerce by handling logistics so millions of small sellers could compete with Walmart, DataSurface democratizes enterprise data infrastructure so teams can focus on business value instead of maintaining thousands of brittle pipelines.

The data logistics layer has arrived.

Three Types of Data Citizens

DataSurface defines a collaborative model built around three roles:

• Data Producers: "I have data that others need, but I want to control who uses it and for what purpose."
• Data Consumers: "I need data from others in a container that fits my use case. I may need the data with low latency; I need 5 year retention for regulatory requirements; I want to query the data in a transactional way/ a reporting way/ an analytical way."
• Data Transformers: "I want to create value-added data using the right technology for my transformation."

These actors collaborate through a shared model. The model evolves over time, but DataSurface enforces strict rules ensuring model changes respect:

• Who can change what: Full audit trails for all model changes.
• Backwards compatibility: No breaking changes.
• Technology limitations: Only use technology that is trusted for purpose.
• Location requirements: Respect data residency laws/policies.
• Vendor constraints: Certain types of data are only allowed on specific vendor technologies.
• Governance boundaries: Honor organizational policies.
• Purpose controls: Enforce usage restrictions.

The model defines what should happen. DataSurface figures out how to make it happen using the best available technology—and that technology can change over time without impacting the model's users. Change the model and minutes later data is flowing for new use cases.

No walled gardens. No lock-in. High velocity.

The Agentic AI Angle

Here's where it gets interesting: agentic AI is being rapidly adopted across enterprises. For DataSurface, agentic AI is just another customer.

By raising the abstraction level, DataSurface enables not just human actors to utilize data—it enables AI agents to consume and produce data alongside everyone else, all governed by the same rules and policies.

Your AI agents become first-class data citizens, not special cases requiring custom pipelines. They are buffered from production systems, they obey the rules and policies set for them by the model.

A New Way to Think About Data Architecture

This represents a fundamental shift. Instead of building thousands of point-to-point data pipelines (P × C complexity that doesn't scale), you declare what you need in a collaborative model and let DataSurface handle the logistics (P + C + T complexity).

Data is the lifeblood of modern enterprises. The ability to extract value from it rapidly will be a key competitive differentiator. Your data circulatory system can't be built pipeline by pipeline — it needs to be industrialized. DataSurface enables that velocity while maintaining governance, security, and flexibility.

The Choice

You can keep building in the walled gardens, hoping your vendor stays competitive and doesn't jack up prices once you're locked in.

Or you can build on an open protocol layer, where you control your destiny. Use best-of-breed walled gardens, but retain the freedom to shift workloads as the market evolves. DataSurface doesn't compete with vendors—it makes them interchangeable. Deploy solutions faster today. Retrofit new vendors tomorrow. All at low cost, with no rewrites.