Understanding DETACH DELETE in Cypher

DETACH DELETE in Cypher is an example of why Cypher is one of my favorite ways of interacting with the Neo4j graph database. The declarative graph query language is constantly evolving to ease the requirements of querying Neo4j. This benefit in ease of interaction, however, can often further remove the query writer from needing to understand the inner  Read More......

Pairing Neo4j ElasticSearch: The Basics

There are a number of ways of integrating Neo4j with ElasticSearch. One common way was through the use of the Rivers plugin, but that was deprecated in ElasticSearch 1.5 and will likely be fully removed shortly after ElasticSearch 2.0. Going forward any integration will require a more sophisticated integration to index the desired nodes and relationships from Neo4j to ElasticSearch.

For those that don’t know, ElasticSearch is an open  Read More......

Graph Advantage: Master Data Management

Master Data Management (MDM) is an increasingly complex topic for organizations today. The rate at which data in an enterprise to is flowing and evolving as a business asset, requires a the need for a more flexible and connection-centric master data storage solution. Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and  
Read More......

Connected Data Analytics: Basics

As organizations adopt graph databases, their available connected data will grow, which will drive the need for analytics to leverage the connected data as a core component of their analysis. The key to unlocking new insights is to leverage the connectedness of the data as part of a graph analytics solution. Through graph analytics enterprises have gained competitive advantages because they are now discovering the cause, effect, and influence of certain patterns 
Read More......

Graph Advantage: Fraud Detection

Graph Advantage: Fraud Detection

Financial institutions and insurance firms with traditional fraud detection capabilities lose billions of dollars to fraud. Traditional approaches in detecting fraud play a critical aspect in minimizing financial losses. However, an increasing number of fraudsters have created different methods to avoid being discovered. In order to gain the upper hand again these financial institutions are need to combine the traditional subject matter expertise of an analyst with enhanced exploration and discovery capabilities enabled through a highly connected data set in agraph database Read More......

Data Validation and Testing Your Graph Data State

Data validation lets you gain insight on the quality of your dataassets. This involves grading your organization consistently to monitor your progress. When testing data, it’s essential to set metrics, as well as succeeding steps and goals to drive improvements. Data testing is even more crucial when loading data into a schema free graph database like Neo4j. So how do we it efficiently and continuously?

Schema-Free Nature of Neo4j and Data Validation

Neo4j is schema-free by nature, but does provide some schema concepts that can be enforced. This means, when your data flows via your Neo4j data pipeline and graph Read More......

Neo4j Production Ready: Enterprise Cloud

The cloud today has become the primary deployment option for startups and is gaining adoption across the worlds largest enterprises. As with other critical infrastructure holding sensitive organization or customer data, there are several key questions enterprises must consider when evaluating the Neo4j graph database cloud Read More.....