What do LinkedIn, Walmart and eBay as well as many academic and research projects have in common? They all depend upon graph databases as a core part of their technology stack.
Why have such a wide range of industries and fields found a common relationship through graph databases?
The short answer: graph databases offer superior and consistent speed when analyzing relationships in large datasets and offer a tremendously flexible data structure.
As many developers can attest, one of the most tedious pieces of applications - specfically those dependent on relational databases - is managing and maintaining the database schema. While relational databases are often the right tool for the job, there are some limitations - particularly the time as well as the risk involved to make additions to or update the model - that have opened up room to use alternatives or, at least, consider complimentary databases. Enter NoSQL!
When NoSQL databases, such as MongoDB and Cassandra, came along they brought with them a simpler way to model data as well as a high degree of flexibility. While document and key-value databases remove many of the time and effort hurdles, they were mainly designed to handle simple data structures.
However, the most useful and insightful applications require complex data as well as allow for a deeper understanding of the connections and relationships between different data sets.
For example, Twitter's graph database - FlockDB - more elegantly solves the complex problem of storing and querying billions of connections than their prior relational database solution. In addition to simplifying the structure of the connections, FlockDB also ensures extremely fast access to this complex data. Twitter is just one use case of many that demonstrate why graph databases have become a draw for many organizations that need to solve scaling issues for their data relationships.
Graph databases offer the blend of simplicity and speed all while permitting data relationships to maintain a first-class status.
While offering fast access to complex data at scale is a primary driver for graph database adoption, another reason is they offer the tremendous flexibility. The schema-free nature of a graph database permits the data model to evolve without sacrificing any the speed of access or adding significant and costly overhead to development cycles.
With the intersection of graph database capabilities, the growth of graph database interest and the trend towards more connected, big data, graph databases increase the speed of applications as well as the overall development cycle - specifically how graph databases will grow as a leading alternative to relational databases.