Is Your Business Future-Ready? Can Graph Database be a Solution?

Manju Naglapur | 5 min read

Relational Database Management Systems (RDBMS) has been driving software applications since the 1980s. However, their need for organized and predictable data limits their capabilities – as Big Data floods the market with its large volumes of structured and unstructured data. 

Furthermore, the exhaustive interdependencies and query connections render RDBMS inefficient, thereby birthing the demand for newer technologies – technologies like graph db. 

Graph Database – A Brief Overview 

The definition of graph db can be summarized in a single statement: 

Graph Database comprises two primary elements, namely a node (data vertices) and an edge (relationships). The former can be any entity, such as a person, thing, place, or data set, while the latter signifies the relationship between the nodes. Such a system allows businesses to generate a vast and extensive database of related data.  

Graph Database deploys a Create, Read, Update, and Delete (CRUD) model where relationships assume the first priority. Graph Storage and Graph Processing Engine are the two technologies prevailing within the framework. 


Why Should Your Enterprise Evaluate Graph Database? 

Modern-day enterprise CIOs and CTOs who deal with number of hierarchical relationships and customer or financial data are always dealing with making sense of the data, extracting, and managing maximum data while interpreting it at scale to derive immediate value. As such, they need to monitor the data points along with the relationships, if any. Hence, executing such a model is only possible through graph db that treats both data and relationships as first-class entities. 

Apart from the fact that graph db is a new and upcoming technology, especially in relation to RDBMS (the 1980s vs. 2000s), it offers the following advantages to your business: 

1.Sustained Performance

With RDBMS, handling relations becomes progressively difficult with the rise in the number and depth of relationships. Against this same background, graph db can be a blessing while dealing with intensive data and relational queries. As a result, even as your business progresses and expands to accommodate greater complexities, graph db will continue performing at optimum levels. 

2.Greater Flexibility

Graph Databases are created to scale. Hence, the pace at which the data architecture and IT teams move corresponds to the rate of business transformation. Therefore, rather than speculating and building a domain pre-emptively, your teams can modify the existing data sets and relationships to update it without affecting the functionality. 

3.Cutting-Edge Agility

Graph Database follows the basic tenets of agile principles, such as continuous improvement, continuous delivery, flexibility, sustainability, etc., through its test-driven, ever-improving development models. Thus, it corresponds to frictionless business development and easier maintenance. 

Use Cases of Graph Database – Amazon Neptune 

As a leading AWS partner, CompuGain works extensively with Amazon Neptune and Neo4j for setting up Graph Databases. For simplicity, we will mostly focus on the premium and fully managed graph db in the AWS ecosystem – Amazon Neptune. 

Here are some practical use cases where you can apply Amazon Neptune to draw more value:


1.Knowledge Graphs

Knowledge graph applications allow businesses to map data over a graphical model, and use queries to support navigation through the heavily connected nodes. Such a system is useful for leveraging existing information to make accurate recommendations or inferences through open-source APIs. 

For instance, if a viewer likes Dragon Ball Z, there are greater chances that they will also like other Japanese anime like Naruto, Bleach or One Piece. 

2.Fraud Detection

Relationships can be a key determining factor while identifying fraud. Amazon Neptune runs graph queries to evaluate real-time transactions and take appropriate action. It executes the process by cross-verifying the credit card details and email ID from the database. 

As a result, your store can flag transactions that take place from different locations or IP addresses and seek approval before proceeding. 

3. Recommendation Engines

Much like knowledge graphs, Graph Database can also help in developing recommendation engines for eCommerce websites. Stores can visualize customer history, past purchases, interests, and other such valuable information to make personalized and relevant product or service recommendations. 

Suppose a user purchases a digital camera. They might get recommendations on the lens and the kit, depending on the purchase patterns of other users who have similar interests and purchase history. 

4. Network and IT Operations

Businesses can use graph db to store a layout of their networking devices and terminals while using graph queries to manage or secure these networks. Upon detecting anomalies, businesses can also use the database to determine its effect and impact by running queries on the graph pattern. 

For example, if you detect malware in one of your networked devices, you can check the extent of infection and trace the device that originally downloaded it. 

5. Social Networking

Amazon Neptune is particularly useful in accessing and processing large volumes of data and calibrate it as per the user’s preferences. Gaining such granular insights into the user behavior is possible by identifying relationships with the user with other users or content. 

Say, you have a social media platform. A certain user, X, often interacts with Y and their posts. They follow ABC celebrities and play games in their free time. Based on this data alone, your engines can ensure that information related to Y, ABC, and games is readily available on their feed for constant engagement. 

6.Life Sciences

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Relational data can lend insight into the data patterns existing in diseases and their spread based on several variables such as genetic predilection, interactions, protein pathways, etc. Accordingly, you can run queries to test responses. 

For example, Amazon Neptune played a vital role in aiding companies like AstraZeneca in developing the vaccine for COVID-19. 

Concluding Thoughts 

Tech giants such as Google, Facebook, eBay, LinkedIn, and PayPal leverage graph db to handle interconnected data. Technologies like IoT further consolidate the role of graph db – as connected data is the future. Contact the experts at CompuGain to migrate to a graph db with no losses. Enjoy upfront value and immediate benefits to gain a competitive edge today!