BigData Vs Neo4J

1.1k views Asked by At

I´ve been looking for a triple store for my project. In this project i want to store my data according to certain ontologies (OWL).

From my research i ended up with two tecnologies Neo4J and BigData that seems to fit well in this case.

I want to know if any of this two is more apropriated to use with RDF, RDFS, OWL and SPARQL Queries.

3

There are 3 answers

0
Peter Neubauer On

You might want to try out the SparQL plugin for Neo4j, see here for a HTTP based test, and this Berlin Dataset Test for embedded usage.

0
Rahul Rawat On

Neo4j can be used to store as entity-relationship-entity form. In case of Bigdata, you should not be upload your whole data into Neo4j because it will become very heavy and process will be very much slow. You should use complimentary db for storing actual data and store ids and some params into Neo4j for Graph traversal to perform sort of Graph Analytics. Neo4j is mainly build up for Graph Analytics that its power or you have to use Graph engine e.g GraphX (Spark).

Thanks,

0
Steve sarsfield On

Neo4J is a specific technology, while big data is more a generic term. I think what you're asking about OLAP and OLTP. As data gets bigger, there are differences between use cases for RDF style graph databases, which are often used for OLAP (On-line Analytical Processing) style analytics. In short, OLAP is designed for analytics that look across an big data set, while OLTP is more aimed at INSERT/DELETEs (on potentially big data).

OLAP-based traversals tend to process the entire graph, while OLTP based traversals tend to process smaller data sets by starting with one or a handful of vertices and traversing from there.

For example, let’s say you wanted to calculate the average age of friends of one particular user. Great use case for OLTP, since the query data set is small. However, if you wanted to calculate the average age of everyone on the database, OLAP is the preferred technology.

OLAP is optimal for deep analysis of a lot of data, while OLTP is better suited for fast running queries and a lot of INSERTs. If you’re trying to achieve a SLA where the analytics must complete within a certain timeframe, consider the type of analytics and which one is better suited. Or maybe you need both.