Graph Database
Use Cases –
Match Your Project
with The Best Skills

Graph database use cases

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Learn from our success story and discover the benefits
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Graph database use cases

Graph Database Use Cases for Building a Project Team

Building the right development team can make or break a project. That’s why we developed SynergyCodesSquadan internal project that uses cutting-edge knowledge graph technology to recommend the best developers for specific project’s requirements. Our application recommends literally the perfect match. Read our story to see how we did it. 

Client Synergy Codes
Business need an application for recommending the best resources for projects
Solution building a graph database, data review, real-time data updates, data modeling, data visualization, knowledge graph
Scope design, UI/UX, frontend, backend
Technology neo4j, gojs
Graph database use cases

The Challenge – Complex Database Management

Our development team encountered three major obstacles while working on this project. Read further to know them and get to know the solution.


Integration of available databases


Data review


Real-Time graph database updates with microservers

The first big challenge we encountered was figuring out how to gather and integrate data scattered around different sources into one database.

The second one was conducting a data review, that is manual completing data with lacking information (for instance, about employee current and archival projects).

The third challenge was to code a microserver that would manage gathered data and be easy to maintenance (wouldn’t require a high tech knowledge) 


The Solution – How to Match The Best Skills for a Project Automatically?

Graph database use cases

See how our development team overcame the obstacles. Get to know the soultion that stands behind creating a perfect match between specialist, their skills and specific project’s requirements.

Graph database use cases

Integration of autonomous databases

Graph database use cases

As part of our application development process, we faced the challenge of integrating two internal databases – Hydra (a databse supporting our leave management application) and Tech-radar. While Hydra collected comprehensive organizational data, including employee information, Tech-radar focused on individual employees’ soft and hard skills and tracked their personal development in these areas.  

To overcome this challenge, we utilized our expertise in integrating diverse tech stacks and successfully combined these autonomous databases into a unified environment. This integration allowed us to establish semantic connections between the data, enabling efficient analysis and improved insights.

Graph database use cases
Graph database use cases

Real-time data updates

Graph database use cases

In order to maintain accurate and up-to-date data, we developed a microserver that receives events from both Hydra and Tech-radar databases. Using these events as input, the microserver updates the graph database and initiates calculations such as inferences. 

The microserver operates through a straightforward process. Upon receiving an event, it conducts an inference to determine the relevant updates necessary for both the databases and the graph database. For example, when Hydra records a new employee’s information, the microserver automatically integrates this data into the graph database without any additional manual input. 

This process is designed for ease of use and requires minimal maintenance and knowledge regarding graph database updates. By automating the integration process, the microserver significantly reduces the potential for human error and ensures accurate data representation within the graph database.

Creation of a data model based on the gathered data

Graph database use cases

We followed these six steps to create a data model for our application. 

  • Firstly, we gathered business requirements and then defined the business processes without worrying about specific variables.   
  • Secondly, we created a conceptual data model to outline the data’s entities, including employees, projects, project owners, timesheets, and users.   
  • Thirdly, we defined entities and attributes for each entity.    
  • Fourthly, we chose the data structure, which is a non-relational database.   
  • Fifthly, we created a physical data model to translate the logical model into actual graph and relationships.  
  • Finally, we conducted tests of the data model to ensure it was working as expected and revise it if necessary. 
Graph database use cases
Graph database use cases

Visualization of the gathered data

Graph database use cases

When the “backend” was ready, we needed to care about the visual part of our application. To visualize our graph database, we created a knowledge graph that presents all gathered data and their connections.   

Visualization maximizes analysis quality. Our product designers’ creative ideas improve usability, lowers the entry threshold for new users, and accelerates the process of getting insights to help and accelerate choosing a specialist for specific project

Graph database use cases

Graph Databse Use Cases – The Final Results

Get to know the final results which describe the advantages of the solutions implemented by our development team.


Technology that Matches Skills with Projects Requirements

Graph database use cases

Our solution creates a perfect match between projects and employees. It means it automatically suggests employees for specific projects relying on similarities between their skills and projects requirements.  

The application provides easy and quick access to current employee information with an integrated database that streamlines data collection and management and connects them.  

Automatic updates ensure timely tracking of employment changes, enabling efficient planning for project allocation based on skill sets. 

Graph database use cases
Graph database use cases

Ready to Use Know-how About Autonomous Database Integrations

Graph database use cases

We created a custom solution to integrate two separate databases into one environment. This makes collecting and managing data more straightforward and more effective. In addition, we created a microservice that enables automatic and real-time data updates that allow you to monitor changes in employment on an ongoing basis and plan the organization’s activities, which is a crucial support for business


Battle-Tested Data Model

Graph database use cases

Our team has developed an effective data model that enables the organic scalability of the database and controls the addition of new data. With the support of our software architects and graph theory specialists, we avoided the creation of illegible data nodes (“data hairballs“) to ensure a clear understanding of the database structure. Moreover, our solution allows for fast and efficient database queries, accelerating information processing.

Graph database use cases
Graph database use cases

Intuitive UI and a Knowledge Graph

Graph database use cases

Our solution includes a visually intuitive and easy-to-use interface for accessing employee data and the knowledge graph presenting information in a clear and organized way. It allows users to easily navigate the data and gain insights into relationships and connections between various data points. Knowledge graph enhances the user experience and provides a powerful tool for data analysis and decision-making. In addition, our team can implement an interactive employee management panel that will allow users to easily and quickly analyze employee data, including the level of qualifications, experience, and location, through graphical visualizations such as charts, diagrams, and tables.

Graph Database Use Cases –
Match Your Project with
The Best Skills

In our company, we faced a critical business need to build an application that could recommend the best resources for our projects. We encountered challenges in gathering and integrating data from different sources, conducting manual data reviews, and updating data in real time.

To address these challenges, we developed a knowledge graph-based solution that enabled us to connect and visualize data semantically. We integrated two internal databases into one environment, created a microservice to update data in real time, and designed a data model based on the gathered data. Moreover, we visualized the gathered data using a knowledge graph, which enhanced the analysis quality and improved usability for our users.

In summary, our knowledge graph-based solution helped us to overcome complex database management challenges and deliver an application that recommends the best resources for project teams, thus helping us build top-performing teams for our projects.

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