A New Paradigm for Data Processing: Graph Databases

Companies have many platforms at their disposal for processing big data. However, it’s still far from easy to efficiently examine highly connected data for real-time recommendation engines, personalization, fraud detection, cyber security, master data management, social networking or 360-degree customer views. These use cases often span hundreds of millions of data points, each with its own relationships.

Since the speed with which distributed big data can be analyzed is often critical to these kinds of applications, many developers are turning to graph database technology. Emerging solutions look at the complex and dynamic connections within and across data sets to deliver new insights and intelligence.

Today’s fastest graph database with the smallest memory footprint is RedisGraph. Its efficiency and query simplification enable applications to rapidly collect, process and analyze complex, connected data and understand data relationships with record-setting performance. With this kind of power, developers can build the richest possible experiences for users.

RedisGraph also lowers data storage costs by up to 60% through the optimized graphical representation of large data sets. And by supporting the widely adopted Cypher query language, which automatically translates queries into linear algebraic expressions, it helps improve productivity so developers can develop new applications even more quickly.

Download this white paper to learn more about:

• A key use case for graph processing—real-time fraud detection
• How graph theory and the database ecosystem have evolved to open up interesting possibilities for exploring connected data in real time
• RedisGraph’s combination of data representation and traversal approaches, along with linear algebra algorithmics
• A performance benchmark in which RedisGraph performs up to 600X faster than other graph databases