In today's distributed architectures, managing data logistics—where data lives, how it moves, and when it transforms—has become increasingly complex. Traditional solutions like Enterprise Service Buses (ESBs) often introduce tight coupling and bottlenecks, while custom API integrations lead to maintenance nightmares.
The Core Problem
Modern enterprises face a fundamental challenge: their data needs to exist in multiple places simultaneously. An order placed online might need to be visible in the warehouse system, the analytics platform, the customer service dashboard, and the accounting software—all in near real-time.
Traditional approaches force you to choose between:
- •Heavy ESBs that create central points of failure and slow down development
- •Custom API integrations that proliferate into unmaintainable spaghetti
- •Cloud-only solutions that force all your data through one provider
A Different Approach
Data Estuary takes a fundamentally different approach. Instead of focusing on how systems communicate, we focus on data logistics—the physical movement and availability of your data.
Think of it this way:
An estuary is where river water meets ocean water. It manages the complex flows between different systems naturally. Similarly, Data Estuary manages how your data flows between your systems—cloud, on-premise, edge—without forcing everything through a central bottleneck.
Three Core Concepts
1. Entities: The What
Entities are your data. An Order, a Customer, a LogEntry—whatever makes sense for your domain. Each entity has a unique ID and type, and can live on multiple clusters simultaneously.
2. Pipelines: The How
Pipelines are step-based workflows that move or transform entities. They can be triggered by events, schedules, or manual invocation. Crucially, your business logic stays in your pipelines— Data Estuary just handles the logistics of where they run and what data they access.
3. Clusters: The Where
Clusters are physical locations where your data lives and pipelines execute. You can use our built-in AWS clusters, or bring your own infrastructure—on-premise, edge, or any cloud provider. This is where Data Estuary really shines.
Why This Matters
This separation of concerns enables something powerful: you can deploy clusters wherever makes sense for your architecture, and Data Estuary handles the replication, consistency, and orchestration automatically.
Want to process IoT data at the edge and only send summaries to the cloud? Deploy an edge cluster and configure replication rules. Need to comply with GDPR? Keep EU customer data in EU clusters. Migrating from on-premise to cloud? Run both simultaneously with bidirectional sync.
What's Next
We're in active development and looking for early adopters. If managing data logistics across distributed systems is a challenge for you, we'd love to hear about your use case.
In upcoming posts, we'll dive deeper into specific architectural patterns, real-world use cases, and technical deep-dives into how Data Estuary works under the hood.