I have some normalized Master Data in PostgreSQL.
I want a graph visualization layer in Neo4j without migrating any Data to Neo4j. Kind of like a view. Lazy fetching of data at runtime.
Neo4j will not commit any changes and only meant for viewing.
Can Neo4j use something like a PostgreSQL JDBC connector and provide a visualization?
Thanks.
You could with apoc.load.jdbc and virtual nodes/relationships created from the data.
But it would be a bit involved as you need to load all tables and then connect them.
With the Neo4j-ETL tool you can do a quick (few min) one-time import to visualize.
https://neo4j.com/blog/neo4j-etl-1-2-0-release-whats-new-and-demo/
Esp. if you don't just visualize but also query you need to transfer the data anyway.
You can use ETL tool, from Neo4j.
You need to ask for an activation key via email at devrel#neo4j.com
Related
I want to present release data complexity which is associated with each node like at epic, userstory etc in grafana in form of charts but grafana do not support neo4j database.Is there any way Directly or indirectly to present neo4j database in grafana?
I'm having the same issues and found this question among others. From my research I cannot agree with this answer completely, so I felt I should point some things out, here.
Just to clarify: a graph database may seem structurally different from a relational or time series database, but it is possible to build Cypher queries that basically return graph data as tables with proper columns as it would be with any other supported data source. Therefore this sentence of the above mentioned answer:
So what you want to do is just not possible.
is not absolutely true, I'd say.
The actual problem is, there is no datasource plugin for Neo4j available at the moment. You would need to implement one on your own, which will be a lot of work (as far as I can see), but I suspect it to be possible. For me at least, this will be too much work to do, so I won't use any approach to read data directly from Neo4j into Grafana.
As a (possibly dirty) workaround (in my case), a service will regularly copy relevant portions of the Neo4j graph into a relational database (or a time series database, if the data model is sufficiently simple for that), which Grafana is aware of (see datasource plugins), so I can query it from there. This is basically the replication idea also given in the above mentioned answer. In this case you obviously end up with at least 2 different database systems and an additional service, which is not so insanely great, but at the moment it seems to be the quickest way to resolve the problem with the missing datasource plugin. Maybe this is applicable in your case, too.
Using neo4j's graphite metrics you can actually configure data to be sent to grafana, and from there build whichever dashboards you like.
Up until recently, graphite/grafana wasn't supported, but it is now (in the recent 3.4 series releases), along with prometheus and other options.
Update July 2021
There is a new plugin called Node Graph Panel (currently in beta) that can visualise graph structures in Grafana. A prerequisite for displaying your graph is to make sure that you have an API that exposes two data frames, one for nodes and one for edges, and that you set frame.meta.preferredVisualisationType = 'nodeGraph' on both data frames. See the Data API specification for more information.
So, one option would be to setup an API around your Neo4j instance that returns the nodes and edges according to the specifications above. Note that I haven't tried it myself (yet), but it seems like a viable solution to get Neo4j data into Grafana.
Grafana support those databases, but not Neo4j : Graphite, InfluxDB, OpenTSDB, Prometheus, Elasticsearch, CloudWatch
So what you want to do is just not possible.
You can replicate your Neo4j data inside of those database, but the datamodel is really different ... (timeseries vs graph).
If you just want to have some charts, you can use Apache Zeppeline for that.
I have a scenario where I would like to model my IoT asset in a the graph database of DataStax Enterprise. This is a perfect fit for my hierarchical data structure. However, when it comes to my time series data I already have that stored in a separate Cassandra table. Is there a way to bridge the gap between data in the graph database and data in a standard cassandra table?
Thanks
At this current moment, all data needs to reside in DSE Graph tables to be available via Gremlin traversals for OLTP or OLAP use cases. We have features coming out soon though that will help provide an OLAP scenario. We'd love to learn more about your use case though to enhance the product for this type of scenario. If you'd like, please join the DataStax Academy Graph channel and we can discuss this requirement further - https://academy.datastax.com/slack
I am working on a research domain called knowledge managment and i am using neo4j.
I want to link my neo4j base with other database that requires physical data storage (PostgreSQL, MySQL...). Is this possible?
In general sure, it depends on how you want to set up the linking.
Perhaps you can detail your use-case more?
Normally people sync data between other datastores and Neo4j e.g. by triggering updates or polling.
For Postgres there is also a foreign data wrapper.
You can also use an event-sourced system, where data is written to your relational databases and relationships also to Neo4j. (also)
I'm currently doing some R and D regarding moving some business functionality from an Oracle RDBMS to Neo4j to reduce join complexity in the application queries. Due to the maintenance and visibility requirements for the data, I believe the stand alone server is the best option.
My thought is that within a java program I would pull the relevant data out of the Oracle tables, map it to a node object and persist it to neo4j (creating the appropriate relationships in the process).
I'm curious, with SDN over REST not being an optimal solution, what options are available for persistence. Are server plugins or unmanaged extensions the preferred method or am I overcomplicating the issue as tends to happen from time to time.
Thank you!
REST refers to a way to query the data over a network, not a way to store the data. Typically, you're going to store the data on some machine; you then have the option of either making it accessible via RESTful services with the neo4j server, or just using java applications to access the data.
I assume by SDN you're referring to spring data neo4j. Spring is a framework used for java applications, and SDN then refers to a plugin if you will for spring that allows java programmers to store models in neo4j. One could indeed use spring-data-neo4j to read data in, and then store it in Neo4J - but again this is a method of how the data gets into neo4j, it's not storage by itself.
The storage model in most cases is pretty much always the same. This link describes aspects of how storage actually happens.
Now -- to your larger business objective. In order to do this with neo4j, you're going to need to take a look at your oracle data and decide how it is best modeled as a graph. There's a big difference between an oracle RDBMS and Neo4J in terms of how the data is represented. Once you've settled on a graph design, you can then load your data into neo4j (many different options for doing that).
Will all of this "reduce join complexity in the application queries"? Well, yes, in the sense that Neo4j doesn't do joins. Will it improve the speed/performance of your application? There's just no way to tell. The answer to that depends on what your app is, what the queries are, how you model the data as a graph, and how you express the resulting queries over that graph.
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.
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,
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.
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.