Neo4j Graph Database Reviews

We have used Neo4j in microservices. In one of the microservices, we used Neo4j since we have some requirements similar to MongoDB plus Elasticsearch. It performs both functions. Instead of doing those two things, we use Neo4j's in-built Solr for queries, and it has MongoDB-like features. We have used these features as well and found them beneficial. 

For Neo4j, we have used it for smaller applications. I have not tested it with larger applications or big data. Our clients use it for applications with 200 to 300 customers. We have a limited database yet did not find any performance issues. 

It is good for search-based tasks, providing solid search results and aggregate results compared to MongoDB. Aggregating with MongoDB is somewhat difficult, whereas with Neo4j, it's easier. Aggregating, backing up data, and creating a new cluster are very easy from the back end. In our DevOps web deployment, we did not find any issues. We created Docker instances and set up Avaya and data configurations, handling databases up to 50 gigabytes without identifying any problems.

We're building a social media platform, which is a great use case for the product. It helps connect people. For example, if we're friends on Facebook, I can get suggestions for people near me or related to you. I can see suggestions for people I might know based on our connection. This is possible because of the tool, which can use location parameters and other factors. Twitter is another good example. I follow certain hashtags and want suggestions based on them. I can group hashtags and get suggestions from them.

The solution's best feature is how it differs from traditional SQL databases. It's hard to map people and find those near me in SQL, which requires long, complex queries. Neo4j Graph Database makes this easier with simpler queries. It also supports more data types, like JSON, which SQL doesn't.

For now, my company is building a knowledge graph for a client. I don't know if you have heard about GraphRAG by Microsoft, for which I am using Neo4j Graph Database. We are building a big knowledge graph for the LLM, and that is it. We are using Neo4j Graph Database to store the knowledge graph.

The tool is easy to use for me, and it is fast for what I need. I searched for many other applications or databases with some other products, but I still prefer Neo4j Graph Database because it is a strong tool.

About one year ago, some of my customers were interested in a graph database. I found Neo4j and other local Korean graph database solutions. Neo4j is better than the local vendors as it is a global first-ranking solution. 

Last year the FDS project was completed successfullyeith neo4j, and I am proud to be satisfied with the customer.

We have seen that it has helped the way our company functions and sped up results. 

Relationship management: Understanding which clients can buy which products, building eligibility and propensity criteria.

• It enables circumstances which would have been a complex problem to be simplified. 
• It creates a visualization for people to understand and have engagement.

The graph modeling paradigm suits our data set well, where there may be orders of magnitude more connections between data points than data points themselves. The Cypher query language is declarative, expressive and easy to learn the basics. Graph database design also allows for interesting optimization patterns that are more difficult to visualize and model in relational or key/value databases.

We are not transitioning from another database system, so our system will be based on graph database design from the start.