The Power of Enterprise-Ready Graph Databases

Graphing databases represent one of the fastest growing areas of the database market. MarketsandMarkets report on graphing databases predicts that graph databases will grow from $1.9 billion in 2021 to $5.1 billion in 2026. Organizations understand the power of graphs as a better way to store and manage information across the enterprise. They are used to handle a number of data situations such as 360-degree views, recommendations, data network methods, and information integration. Given the popularity of these tools, it is important to understand what they are, how they can best be used, what is happening in the market, and where their strengths and weaknesses are.

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Graph databases (aka triple buffers / RDF [Resource Description Framework] Data models, or property graph storage) are data management systems that store information about digital entities and how they relate to each other. While relational databases use primary and foreign keys and queries to link information items together, graph databases show clear relationships. As a result, graph databases naturally store information in groups of three. There is a topic and a news and a topic. The subject is a specific declaration of the type of relationship. The predicate is a description of what that relationship is. The object is the element associated with it.

There are three things to note in the way graphs store information. First, because the type of relationship is clearly defined, it is easy to quickly query common relationships. Second, information is stored the way people naturally think and ask questions. Third, the subject and subject in this example can be anything. The example I used was a product and part of it. It can easily be a data set or a field in the data set. What this means is that graph databases are very good at identifying relationships between disparate data and information. It is very good at collecting information from different isolated applications and making it more useful for business users.

Where graphs are used

Graph databases can be used for a variety of solutions including data network architectures (an approach that creates domain-based connections for distribution
Data sets for data consumption scenarios such as machine learning, analytics, or data-intensive applications across the enterprise).

Graph databases act as a network in a data network solution. The graph database is
It is designed using ontology which represents the way business works. Then the graph is mapped to specific data items using a feature called default graphs. Business users can then query the graph without having to understand how the underlying data is structured. The graph understands where the data is and returns it in a format that makes sense to the user.

Additionally, charts can be used as recommendation engines by blending data and content and using relationships in the chart to identify relevant products or information. Charts are also used as grouping tools. Organizations looking to get 360-degree views of customers, employees, products, services, and important topics aggregate this information from multiple sources by dragging it into a graph with the customer, employee, product, service, or subject as a node or subject in the graph.

Finally, graph databases are integrated into company search engines to reveal aggregated 360-degree views, improve search relevance, integrate data into search results, and provide better support for natural language queries like those of chatbots. Organizations that choose to invest in charting technology use all of these different methods to get significant value out of those investments.

Getting started with graph databases

Once an organization has decided that it needs a graph database, it must decide what type of graph database is best for its needs. The graph database market is divided into two product types: RDF graphs and property graphs.

RDF graphs are based on the W3C standard Resource Description Framework and store all information in the form of triples. Neo4j created property graphs which are designed to store triples as well as properties about each entity (object or object) in the graph. While Neo4j has the largest market share of any graph database, many organizations prefer to stick with RDF graphs because the standards facilitate integration with them and allow for easier migration if the organization decides to switch graph database tools.

Organizations that choose to use property graphs tend to select them because they are architectures that are more familiar to their developers to work with and because products like Neo4j have a number of ready-made and very tempting visualization capabilities.

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