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## 11.3. Data Integration

Most enterprises rely primarily on relational databases to store their data, but this may cause performance limitations. In some of these cases, Neo4j can be used as an extension to supplement search/lookup for faster decision making. However, in any situation where multiple data repositories contain the same data, synchronization can be an issue.

In some applications, it is acceptable for the search platform to be slightly out of sync with the relational database. In others, tight data integrity (eg., between Neo4j and RDBMS) is necessary. Typically, this has to be addressed for data changing in real-time and for bulk data changes happening in the RDBMS.

A few strategies for synchronizing integrated data follows.

### 11.3.1. Event-based Synchronization

In this scenario, all data stores, both RDBMS and Neo4j, are fed with domain-specific events via an event bus. Thus, the data held in the different backends is not actually synchronized but rather replicated.

### 11.3.2. Periodic Synchronization

Another viable scenario is the periodic export of the latest changes in the RDBMS to Neo4j via some form of SQL query. This allows a small amount of latency in the synchronization, but has the advantage of using the RDBMS as the master for all data purposes. The same process can be applied with Neo4j as the master data source.

### 11.3.3. Periodic Full Export/Import of Data

Using the Batch Inserter tools for Neo4j, even large amounts of data can be imported into the database in very short times. Thus, a full export from the RDBMS and import into Neo4j becomes possible. If the propagation lag between the RDBMS and Neo4j is not a big issue, this is a very viable solution.

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