From Process-Centric to Data-Centric Integration
For years, Enterprise Service Buses (ESBs) have been the standard solution for integrating enterprise systems, especially in SOA-driven environments. They provided a central hub to route messages, orchestrate services, and manage complex workflows.
But in the age of real-time applications, microservices, and customer-centric operations, traditional ESBs are falling short. Today’s businesses demand data-first, low-latency, and schema-aware integration — and that’s where Operational Data Hubs (ODHs) come in.
Why Traditional ESBs Fall Behind
Although ESBs were effective in the past, they pose serious limitations in today’s landscape:
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High latency: ESBs are not built for real-time; most rely on message queues and batch processing
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Tightly coupled interfaces: Changes in one service often break others
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Complex governance: Managing message schemas, transformations, and routing rules becomes brittle
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Limited data capabilities: No inherent support for change data capture (CDC), schema evolution, or analytics-driven consumption
As enterprises scale, maintaining ESB logic becomes a bottleneck for both development and innovation.
What Is an Operational Data Hub?
An Operational Data Hub is a modern integration layer designed to synchronize, transform, and serve operational data in real time.
Unlike ESBs, which are focused on services, ODHs focus on data — continuously integrating changes from multiple systems and making them available to downstream consumers such as APIs, microservices, dashboards, and AI models.
Head-to-Head: ESB vs Operational Data Hub
Feature |
ESB |
Operational Data Hub |
Integration Focus |
Process / services |
Data / events |
Latency |
Minutes to hours |
Milliseconds to seconds |
Architecture |
Centralized message bus |
Streaming data pipelines |
Adaptability |
Static interface contracts |
Schema-aware, flexible |
Primary Use Cases |
Workflow orchestration, SOA |
Real-time sync, Customer 360, API data services |
Example Tools |
MuleSoft, WSO2, Tibco |
TapData, Confluent, StreamSets |
Real-Time Is the New Default
Business applications increasingly rely on real-time context:
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Retail personalization engines need up-to-date customer data
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Operations teams need live dashboards on inventory and supply chain
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AI models need continuous data streams to improve performance
ESBs can’t keep up with these use cases. They weren’t designed to handle CDC-based streaming, incremental views, or materialized APIs.
Operational Data Hubs fill this gap.
How TapData Enables a Modern Data Hub Architecture
TapData offers a purpose-built platform for real-time operational integration:
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Log-based CDC support for Oracle, MySQL, PostgreSQL, SQL Server, and more
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Sub-second data sync pipelines to MongoDB, ClickHouse, Kafka, and API targets
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Auto-refreshed materialized views for real-time dashboards and API delivery
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Schema mapping and evolution handling built-in
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No-code pipeline designer for fast deployment across legacy and modern systems
With TapData, enterprises can replace outdated ESBs with a scalable, data-centric integration fabric optimized for both operational use and analytics.
Migration Considerations
Thinking of moving away from an ESB? Consider the following steps:
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Identify existing ESB flows that are data-dominant (e.g., syncing orders, customers, inventory)
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Rebuild these flows as CDC-based pipelines in TapData
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Replace hardcoded interfaces with API-ready materialized views
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Monitor performance and validate with consumers before decommissioning ESB logic
This gradual approach allows for low-risk modernization, especially in enterprises with legacy infrastructure.
Summary: Replace Complexity with Speed and Flexibility
Traditional ESBs were built for an era of static systems and slow data. Today, agility, speed, and real-time insights are critical to business success.
An operational data hub offers the modern enterprise:
TapData helps teams move from process-first to data-first, giving you a platform to power everything from APIs to machine learning — in real time.
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