Using the Neo4j graph database doesn’t have to be opposed to your existing Oracle RDBMS infrastructure. In fact, the two can work together. One of many ways Neo4j works alongside Oracle RDBMS is to have all data fully synchronized between the two database technologies.
Through RelGraph, we’ll explore how these two database technologies work together in tandem to deliver the best bottom-line results for both enterprise architects and business teams alike. Also, we’ll discuss the advantages of fully synchronizing the data between your Oracle RDBMS and Neo4j, and how RelGraph effectively helps you in doing so.
Why Sync Data between Oracle RDBMS and Neo4j
Applications that integrate data from multiple data sources are a common use case for full synchronization. Another use case for a full synchronization arises when you have an existing set of applications writing to an Oracle database and changing those applications is cost prohibitive. For the data to add increasing value, new technologies need to be introduced where the Oracle RDBMS is incapable.
Neo4j hosts good article on how both Neo4j and Oracle RDBMS can coexist and 3 advantages of using Neo4j with Oracle. I highly recommend checking out these articles.
Why RelGraph?
RelGraph is a single solution to keep both your relational database and graph database in sync automatically with minimal human intervention. It’s intuitive, simple and works like a charm in every environment (cloud based or on premise databases).
Features of RelGraph
Although RelGraph looks very minimal in design but it rather hosts very powerful features as listed below
- Easy Integration – With RelGraph, you just need to configure topologies with basic information and it does the rest on its own.
- Model relational database to graph database as the way you like – You get complete control over the data you need to synchronize with Neo4j
- Bulk load Relational data – Import the entire dataset in one shot through bulk load feature of RelGraph
- Load incremental events – Load the data as it arrives in relational database through Kafka so as to keep Neo4j up to date at every point
- Timestamping to see how data evolves over time – Import the snapshot data from relational database in Neo4j to understand how data is evolving over time
