Managing an organizational data like reporting hierarchy, like products under managers, like managing the transactions of an organization etc. are pretty difficult to design when will think of RDBMS. in case of graph database managing all hierarchy and design makes pretty simple. As we maintain the relationship as labels between the nodes. The basic challenges we used to face using RDBMS in designing would be:
Complex and hierarchical data sets. Master data, such as organizational and product data, has deep hierarchies with top-down, lateral and diagonal connections. Managing such data models with a relational database results in complex and unwieldy code that is slow to run, expensive to build and time-consuming to maintain.
Real-time query performance. Master data systems must integrate with and provide data to a host of applications within the enterprise sometimes in real time. However, traversing a complex and highly interconnected data set to provide real-time information is a challenge.
Dynamic structure. Master data is highly dynamic with constant addition and re-organization of nodes, making it harder for your developers to design systems that accommodate both current and future requirements.
Impact analysis and network planning. Quickly determine the impacts of a node failure, maintenance outage or incursion and recommend alternate routes around your most relied-upon components.
Root-cause analysis. Immediately identify the root cause of any network or infrastructure problem by tracing back dependencies quickly and easily. Also provide service desks greater visibility of all components and relationships that make up your IT infrastructure.
Routing and quality-of-service (QoS) mapping. Find the best, shortest or least busy path; pinpoint the best location in the network to introduce a new service; and complete your QoS mapping from the segment level to the entire network.
IT infrastructure management. Map IT services to the chain of dependent physical and virtual infrastructure components, optionally mapping all the way up to cost centers.
Highly interrelated elements. Whether youre managing a major network change; providing more effective security-related access; or optimizing a network, application infrastructure or data center the physical and human inter dependencies are extremely complex and challenging to manage.
Non-linear and non-hierarchical relationships. Relationships among the various nodes in your network are neither purely linear nor hierarchical, making it difficult to model using traditional RDBMS. In addition, when two or more systems are brought together, these relationships become even more complex to describe.
Growing physical and virtual nodes. With rapid growth in network sizes and both the number and types of elements added to support new network users and services, your IT organization must develop systems that accommodate both current and future requirements.
In our real life will face many issues which can be optimized with graph database. I personally feel Neo4j is best graph database.