Neo4j Graph Database Reviews & Product Details - Page 11

What I like about this NoSQL database is that nodes are presented in a graph. You can later add on relationships and define the schema. Review collected by and hosted on G2.com.

Education Resources should be widely available aside from the training and documentation available onsite. Review collected by and hosted on G2.com.

The ease of implementing it. It can hold a large amount(millions) of relational data with ease and can retrieval is fast too. Coming to the cypher query language(CQL) it is very convenient to retrieve complex data from the database even if it's highly connected data. Now with the recent releases the APOC procedures make it more simpler to control the basic DMLs and DDLs. This database is a must try for relational data. Review collected by and hosted on G2.com.

OLTP is not that flexible if used with neo4j. As the relational data grows we started facing problems with the implementation of OLTP part with Neo4j. And it still can face problems with Super Nodes(most highly connected single node). The schema should be properly designed to hold off millions of data without ambiguity. Review collected by and hosted on G2.com.

Neo4j really excels at answering complex queries, which would never work in traditional SQL. Our team has also received great support from Neo, which has helped us get the most out of the technology. Whenever we want to extend functionality, it always seems possible, which has really helped our business. Review collected by and hosted on G2.com.

Our team is really happy with the Neo4j solution, no complaints. Review collected by and hosted on G2.com.

I started using Neo4J to represent various facilities ( of a startup that I work at) across the city as nodes and represent their relationships in terms of mode of commute available and the associated costs.

The brilliant thing about Neo4J, or graph DB for that matter of fact, is that it represents a graph like database as a graph and not a format manipulated to be used like a graph. This is turn makes it extremely practical for various graph based use cases like route optimization, connectivity networks etc

One more added advantage with using neo4j is the ability to use years of research done on graphs & its algorithms.

Now, what sets neo4J apart :

1) The cypher query language is very natural to write and easy to understand.

2) Allows many RDBMS like constraints to be applied to a graph.

3) The in-built query browser eliminates the use of 3rd party tools for simple graph visualizations Review collected by and hosted on G2.com.

1) Not optimized for long traversal queries. Queries that require multiple routes inherently.

2) Very few DB drivers available, mostly are SDKs wrapped over the cypher query language (and communicates with the DB over HTTP & hence slightly slower).

3) cypher queries though simple to get started with, quickly turns into a complicated one due to very few methods. Review collected by and hosted on G2.com.

Property graph model is great as it is isomorphic to datastructures (knowledge graphs) that we model which lets us avoid writing much glue code and awful joins. I very much like the query language for its flexibility and of course the web interface is super useful for exploration. Review collected by and hosted on G2.com.

I wish there was some room for hybrid representation - like in document-based dbs. In some cases, the fact that it's a connected graph rather than a collection of ad-hoc rows, triples or whatever puts additional responsibility in certain fairly basic use cases. Review collected by and hosted on G2.com.

Great community, awesome in city meetups. The language is really simple compared to other data languages. Once you get into graphs, you need a framework that can express your thoughts easily. The neo4j graph language is fairly easy to understand and there are a LOT of toolkits available to map it to your environment. (graphs)-[:ARE]->(everywhere) Review collected by and hosted on G2.com.

I wish there was a vis tool to help learn the language. Although I said it's easy, I should have said it is the easiest. Without an instructor like at the city meetups, it is probably tough to learn it over a weekend. But WAY easier than other systems. At least you don't need to install java or some other crazy dependency. :) Review collected by and hosted on G2.com.

The dashboard interface is really nice. Really user friendly to write Cypher queries to retrieve graph subsets. The graph is also made interactive and this helps easy presentation. Review collected by and hosted on G2.com.

There could be more flexible implementations of algorithms in the library. This is possible, but usually with the loss of readability. Review collected by and hosted on G2.com.

The well-designed concepts and implementation of Neo4j makes it easy to model the domain data I had. It also turned to be easier to develop the data model using a graph database than an SQL database,. Oftentimes, SQL database schema become brittle and hard to modify after a certain level of complexity. I did not experience this problem with Neo4j. The Cypher query language took a little time to figure out, but in the end turned out to be powerful and easy to use. Review collected by and hosted on G2.com.

With the current implementation, one Neo4j server has one and only one data space. For use cases where one has two or more distinct data domains, one would sometimes be able to use one single server for more than one data space. It is of course possible to work around this in different ways, but that is messy. Review collected by and hosted on G2.com.

Whenever we begin a project we usually think about the relationships between all the various actors. We draw them on the white board. With Neo4j it's really easy to translate that mental model directly into a database schema. Review collected by and hosted on G2.com.

The tooling itself was an issue. We used the neo4j ruby bindings to create an embedded neo4j database in rails. This required us to use jruby instead of regular ruby. Using jruby created a lot of other issues with compatibility with the various gems out there that we needed to use. Also there was a lot of uncertainty about licensing which was a business concern. Just by using it were we in the clear or did we have to pay out if we were commercial? It was a headache the legal department did not want to deal with. Review collected by and hosted on G2.com.

RDBMS are now the de-facto industry standard and they shine in calculations through BIG arrays of data packed into rectangular tables (consider mega- hyper-Excel on steroids). But neither real world nor business logic is rectangular by their nature. The World consists of flexible structures like lists, like trees (be it a plant, or organisational structure, or tree of possible decisions), like networks and lace, or - what a horror! - like fractals. RDBMS, with its tabular rectangular nature, is able to emulate these with its tables - but it requires deep and complex programmatical magic, which also involve complexity, computational and human resources, and... software errors.

Being a native graph database, Neo4j allows you to reflect complex real-world graph structures of entities and their relationships in an easy and natural way, close to 1:1 mapping - and thus, avoid bulky emulation of ethereal spiderweb structures with heavy rectangular bricks made of SQL. This allows you to make your systems faster, more responsive and smarter - because they reflect the reality better. Also, graph data model is much agiler than relational and tolerates many real-world conditions which RDBMS can not.

Using Neo4j since 2013 I confirm all the above myself. It is perfectly suitable for being a kernel for enterprise Master Data Management, integrating different business systems around it. Neo4j is perfectly suitable for modern microservice architectures of enterprise solutions, this technology is also native to it.

And the price-performance ratio is impressive. Neo4j is very, very fast, and (with Enterprise license) it smoothly scales horizontally (you also get HA as a bonus). Databases scaling a billion of nodes and several billions of relationships are perfectly realizable (I personally have used a database of about 200 million nodes and Neo4j scale extremely well - it's performance does not depend on the size of the database, only diameter of the graph really matters).

What captivated me the most is how easy and natural graph technology allows you model complex realities and discover non-obvious interconnections and relationships between the entities, together with hidden patterns of facts. OLAP cubes too are graph structures. after all.

Technology is new - but the learning curve is not too steep, as soon as you catch up with this new, different graph attitude. Also online support from the community and directly from Neo4j team members makes learning Neo4j a fascinating and pleasant experience. Review collected by and hosted on G2.com.

Neo4j is industrial grade, commercial quality product. But it is at the stage of rapid development just now, upcoming version 3.0.0 promises new features and performance boosts. Also the graph query language - Cypher - is under extensive development, new "sugar" is being added to it together with performance enhancements. So it may be hard to choose which version to take for you project - more mature with fewer features, or the newest and powerful, but not proven bullet-proof yet?

Anyway, I can not call it a disadvantage, you just need to think two steps ahead, not one. Review collected by and hosted on G2.com.