Data Integration: Connect Systems, Break Down Silos & Automate Data Flows
Data is worthless when it's in the wrong system. Data integration is the process of breaking down these silos and making data available where it's needed – not as a static export, but as a continuous, automated data flow.
Strategically, data integration means: Systems don't work next to each other, but with each other.
What is Data Integration? (Definition & Methods)
Data integration describes the consolidation of data from different sources into a unified, consistent view. The goal is to make data usable across systems without manually copying or maintaining it multiple times.
A helpful analogy is that of an interpreter: Each system speaks its own language (e.g., data formats, interfaces, structures). Data integration translates between these languages so that all participants can understand and use the same information.
Core Data Integration Methods
ETL (Extract, Transform, Load)
The classic approach from the data warehouse world. Data is extracted from source systems, transformed, and loaded into a target system. Robust, but usually batch-based and time-delayed.
ELT (Extract, Load, Transform)
A modern variant for cloud architectures. Transformation occurs in the target system, often in scalable cloud databases. Particularly relevant for large data volumes.
API Integration
Systems communicate directly via interfaces. Changes are transmitted in real-time, such as price, inventory, or status changes. This method is central to operational processes today.
Data Integration Example: ERP Meets Online Shop
A concrete example makes the benefit tangible: A product price is adjusted in the ERP system (e.g., SAP).
Initial situation without integration
An employee must manually enter the new price in the online shop. This leads to:
- ✗Time spent on manual transfer
- ✗Inconsistencies between systems
- ✗Errors in prices and margins
With integrated data architecture
- 1The price changes in SAP
- 2SAP sends the update automatically
- 3DataNaicer receives the change, validates and transforms the data format
- 4The online shop (e.g., Shopify) receives the new price in seconds
Result
Prices are synchronized in under a second. No manual intervention, no duplicate data, no discrepancies.
Data Integration Explained: How Does Data Flow Technically?
Technically, integration architectures can be roughly divided into two models. Which variant makes sense depends on data volume, timeliness requirements, and system load.
Batch Integration (ETL)
In batch integration, larger data volumes are processed on a schedule – classically overnight or at defined time windows.
Real-time Integration (Event-based)
Here, data is transmitted immediately upon a change, e.g., with a new order or status change.
For IT architects, this decision is central: Is 'tomorrow morning' enough – or does it have to be 'right now'?
DataNaicer: The Central Data Hub for Your IT Landscape
For IT managers and architects, what matters is: Data integration is not a single tool but an architectural principle that makes data flows manageable.
Frequently Asked Questions about Data Integration (FAQ)
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