As the Neo4j partners we can help with the licence purchase or in the access to the data base. Additionally, we can perform the following:
Graph database main features
Graph database is made along a different concept in comparison to relation database. They accumulate the information in the nodes (which corresponds to single records in RDBMS) and by means of relations (which corresponds to the connections between tables in RDBMS). Each node and relation may have the common attributes (which corresponds to the columns in RDBMS), but the list of attributes may differ even between the nodes and relations of the same kind.
The features of graph database, crucial for its users are as follows:
With the inquiries involving numerous tables,m the construction of the graph database allows for reaching the performance degree significantly higher than in the RDBMS case. It happens in spite of the lack of the dedicated indexes.
No pre-defined data model; the actual model is being made with the in-flow of the new data. Intuitive „reading” the data in the model, both by the IT specialists and by business partners.
No pre-defined scheme results in quick reacting to the needs for changes in the applications (CR). Data in the graph database can be shaped according to needs and requirements.
Graph database is characterised by the high level of accessibility, transactionality and scaling. Billions of nodes and relations can be kept inside of them. They can be used on laptop or any workstation, and also in the corporations, as the mass data stores.
Thanks to the in-built devices, data extraction and analysis can be performed even by persons not familiar with the IT knowledge.
Based on the information about the real beneficiaries in the companies and about the relations between companies we can create a network of the mutual connections in the shape of a graph. We can search the relations between different persons and/or companies, defining conditions (with conditions being e.g. specific name or surname, name of a company or a number of relations stemming out of a node) and the number of relations understood by the distance between objects (e.g. we look for the shortest path between a user named Jan Kowalski and the Alfa company, with maximum distance of 5 relations). In the end, we can create a graph with millions of nodes and relations (Neo4J is able to serve many billions of nodes in one base) and analyse it.
We can introduce to our graphs persons or relations which do not result directly from the official registers. Such relations are those of kinship, neighbourhood, close cooperation of separate business entities, supply chains etc. We can make use of those relations or leave them out. Making up new or additional relations may be performed by defining the rules for data in the base and their implementation. These data may come from institution’s own resources or from the outside.
The Neo4j base has over 500 functions implemented. Apart from that, in the APOC library we have the access to more than 600 additional functions.
Thanks to the structure of the graph database and the already implemented inner mechanisms (Neo4j Graph Data Science Library) we can: