A graph is built from a collection of nodes and relationships. Entities such as people, locations, items, or categories of data are represented by nodes; and the association between them reflects a relationship. A versatile structure like a graph enables us to model real-world applications–computer networks, social media recommendation engines, bitcoin blockchains, and more. Basing this very structure as a template, we can bring it to life by performing C.R.U.D operations through a unique management system–a graph database.
A graph database is a NoSQL database that uses the art of graph theory to store, map, and search existing relationships within the data set, or discover new ones. The relationships drawn from the connected nodes highlight the fundamental value of graph databases. Unlike other databases, graph databases treat relationships as first-class citizens. Consequently, this equates the importance of relationships to the very data itself. Numerous types of relationships such as ownership, transactions, and social interactions can be portrayed in such a model.
Using native graph storage is ideal for graph-like data. Designed precisely for the management of graph-like data, this approach optimally renders the storage with the nodes and relationships closely knit. In some cases non-native graph storage can be used. However, there is a considerable performance trade-off incurred dealing with translation from tabular data (relational), making it much slower and less efficient.
Native graph processing leverages the concept of index-free adjacency to write and retrieve data in the most efficient way. During write processing it ensures each node is stored corresponding to its adjacent nodes and relationships. When processing queries, index-free adjacency retrieves data quickly without a strong dependency on indexes. In contrast, non-native graph processing relies heavily on indexes to execute transactions, yielding a significant impact to latency.
When dealing with graph-like data that is dynamically interwoven and connected, graph databases perform robustly when contrasted with relational databases. Queries in a graph database are proportional to the size of the graph that is being searched. Consequently, the time needed to execute queries is also proportional to the amount of the graph searched and remains consistent even as the data set grows.
Graph databases follow a schema-less structure. Thus, they store data as an object rather than inserting it in a tabular format, providing great flexibility in performing data operations. IT and data architects can move at the same pace as the business without having to exhaustively model the schema ahead of time. As a result, changes in structure to address dynamic business needs, and significant expansion in the size of the data set, are handled painlessly.
We live in a world where every corner is connected in some way or form, and keeping up with the pace of such a dynamic atmosphere will only get tougher. Additionally, datasets keep growing in complexity, making it more challenging to process and query.
Graph database technology helps businesses leverage relationships that are underutilized yet predictive. Having nodes and relationships eloquently expressed, graph databases make it easier for users, IT operators, architects, and the machines to understand the data with its flexible framework and adaptable schema. Ultimately, this leads to uncovering already existing relationships as well as discovering new ones. The enhanced understanding and visualization allows for deeper insight and contributes profoundly to machine learning initiatives. This has facilitated context-based machine learning which encompasses feature engineering to improve algorithms and machine-based inferencing, to name a few.
Relational databases struggle performance-wise when handling large sets of data. The rigid schema simply cannot accommodate the evolving needs of our businesses. Relying on larger JOIN tables will degrade performance, where highly connected data repetitively demands multiple hops across the tables. While that may sound as a solution, our end-users value time-efficiency–a necessity for highly connected data.
Relationships between the data have become more important than the data points themselves. Today’s businesses need a framework that excels at linking diverse data to leverage their business needs. Graph databases, with such a flexible and cost-effective approach, have played a major role in finding and developing deeper insights. What follows is a mere preview of how graph databases are transforming the industries we rely on.
These examples of mainstream companies merely scrape the tip of the iceberg–the practical applications are limitless. From nonprofits and small startups to large enterprises, graph databases are transforming the way we manage and use our data. In fact, there are a number of highly visible industries where the use of graph database technology has become prevalent, and is often considered mission critical:
Providing a flexible framework to work with, graph databases represent an optimized, efficient, and cost-effective approach to handle today’s dynamic challenges. With the capability of storing billions of relationships and querying the graph under milliseconds of latency, this versatile technology empowers businesses by revealing untapped yet valuable insights buried in complex and highly-connected data.
Now that we have explored how important and useful graph databases really are, it's time to take the next step. Stay tuned for the next blog in our graph database series, and we’ll dive deeper into data modeling and compare the different approaches.
