Why Graph Technology is the future? – Graph database explained!
History of Graph Database:
  • In the 1960s, navigational databases such as IBM’s Information Management System (IMS) supported tree-like structures in its hierarchical model, but the strict tree structure could be circumvented with virtual records.
  • Graph structures could be represented in network model databases from the late 1960s. CODASYL, which had defined COBOL in 1959, described the Network Database Language in 1969.
  • Labelled graphs could be represented in graph databases from the mid-1980s, such as the Logical Data Model.
  • Several improvements to graph databases appeared in the early 1990s, accelerating in the late 1990s with endeavours to index web pages.
  • In the 2000s, commercial graph databases with ACID (Atomic, Consistent, Isolated, Durable) guarantees such as Neo4j and Oracle Spatial and Graph became available.
  • In the 2010s, commercial ACID graph databases that could be scaled horizontally became available. Further, SAP HANA brought in-memory and columnar technologies to graph databases. In the 2010s, multi-model databases that supported graph models (and other models such as a relational database or document-oriented database) became available, such as ArangoDB, OrientDB and MarkLogic (starting with its 7.0 version). During this time, graph databases of various types have become especially popular with social network analysis with social media companies’ advent.
What is a Graph Database?

A Graph is composed of two elements, namely a Node and a Relationship. Each node represents an entity (a person, place, thing, category or any other piece of data), and each relationship represents how two nodes are associated with reach other. E.g., the two nodes, ice-cream and dessert, would have a relationship, is pointing from ice-cream to dessert. Therefore, in computing, a Graph Database is a database that uses graph structures for semantic queries with nodes edges and properties to represent and store data. A vital concept of the system is the Graph or edge or relationship. The Graph relates the data items in the store to a collection of nodes and edges. The edges are representing the relationships between the nodes.

Graph databases are built with the purpose to store data and navigate their relationships. The Relationships are first-class citizens in a graph database, and most of the value of graph databases is derived from these relationships. Graph databases use nodes to store data entities and edges to store relationships between entities. An edge always has a start node, end node, type, and direction. The edge can describe parent-child relationships, ownership, actions, and the like. There is no limit to the number and kind of relationships a node can have. Graph databases are used often to analyze relations within highly interconnected datasets. Recommendation engines, Social networks, fraud detection, corporate hierarchies, or querying a bill of materials are common use cases. But these datasets change over time, and the developer or data scientist may want to time travel and analyze these changes.

Therefore, Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and their relationships into edges:

  • Nodes: represent instances or entities, such as people, accounts, businesses,
    Or any other things to be tracked. They are roughly the equivalent of a record, row or relation, in a relational database or a document in a document-store database.
  • Edges: Edges are also termed graphs or relationships, are the lines that connect nodes to other nodes, representing the relationship between them. Meaningful patterns emerge when examining the connections and interconnections of nodes, properties and edges. The edges can either be directed or undirected. In an undirected graph, an edge connecting two nodes has a single meaning. In a directed graph, the edges connecting two different nodes have different meanings, depending on their direction. Edges are the key concept in graph databases, representing an abstraction that is not directly implemented in a relational or document-store model.
  • Properties: Properties are information associated with nodes. For example, suppose Investopedia were one of the nodes. In that case, it might be tied to properties such as website, reference material, or words that start with the letter I, depending on which aspects of Investopedia are Germane to a given database.
How does the Graph Database function?

A better way to store connected data. Graph databases excel at exploring highly connected data. Learn how to leverage the futuristic and transforming technology in your organization. Unlike other database management systems (DBMS), relationships take first priority in graph databases. In the world of Graph Database, connected data is equally or more important than individual data points.

This connections-first approach to data means relationships and connections are persisted and (not just temporarily calculated) through every part of the data lifecycle: from idea to design in a logical model, to implementation in a physical model, to operation using a query language and to persistence within a scalable, reliable database system. Unlike other database systems, this approach means your application doesn’t have to infer data connections using things like foreign keys or out-of-band processing, like MapReduce.

The result:

Your data models are simpler yet more expressive than those you’d produce with relational databases or NoSQL (Not only SQL) stores.

Why Graph Database or Technology?

The potential for ‘Graph Database’ and ‘Graph Analytics’ is phenomenal. They significantly expand the ability to find connections in large amounts of data. A Graph database concentrates on all possible combinations of two or more data objects to a subset of the preferred combinations of objects in a dataset. Therefore with Graph database management, it is all vs preferred.

Key Advantages of Grap Database for Enterprises:
  1. Graph technologies are specifically useful in discovering minority positions in not just a dataset but the entire body of data. Thought minority positions can be meaningful and essential in many ways; other technologies cannot spot them as Graph technology.
  2. Data Scientists can use Graph Databases to accelerate identifying patterns and relationships in a dataset during discovery. Data scientists also can use Graph databases for real-time analysis in complex and massive datasets.
  3. A Graph database gives an idea from the data, whereas with most other databases, we have to have the concept and then request the data. Graph Database helps users find customer service problems, underserved market segments or supply-chain issues they didn’t know existed.
  4. Graph representation works with diverse and incomplete datasets. It can work with information skew. E.g., A few individuals may have complete profiles and a lot of links, while others may have sparse representations. Other databases would struggle to deal with this kind of skewed information. At the same time, Grap databases provide a robust representation mechanism that lets data scientists or developers diverse entities and relationships in an intuitive way.
  5. Hierarchical, relational and document databases suffer from model rigidity, which simply means the underlying model based on which the data is stored is predefined. One of the top Graph Database advantages is the way they represent data. The lack of rigidity enables more agility and flexibility in storing and developing relationships between the data points. Nodes can be stored independent of their edges or relationships. The relationship can be integrated when one develops the Graph. This key feature makes Graph Databases have other benefits like Agility, Flexibility. and Performance.

To Summarize, the real world is richly interconnected. Graph Databases aim to these sometimes consistent and erratic relationships intuitively, making Graph Database a paradigm different from other database models. It maps more realistically to how the human brain maps and processes the world around it. When we start seeing Graphs of interconnected data in one place (the recommendation engine, for, e.g.), we start seeing them in other places, too, like the fraud detection efforts or in the master data management. Soon you will have the epiphany – Graphs are everywhere. Therefore, it comes as no surprise that Graph Technology is on the rise. There is a huge possibility that the enterprises are evaluating or exploring a graph database’s deployment. Hence, this is the opportunity to step up in the game and join leading companies like

Leverage Graph Technology today and make the business retain its competitive advantage and be ahead of its competitors.