Graph Streaming Pipelines with Kafka and AllegroGraph

Nowadays, enterprises are starting to adopt streaming pipelines to provide insights that adapt to new data in real-time. This goes beyond traditional approaches that operate on data batches. Compared to batch processing, the advantage of streaming is obvious. For example, in the manufacturing area, analyzing data from various sensors in real-time allows a manufacturer to spot problems and correct them before a product leaves the production line. This improves the efficiency of operations — and saves money. When “real-time experience” is important (or mandatory), a flexible, scalable and robust streaming platform is always more suitable.

AllegroGraph is used very often as an Entity Event Knowledge Graph platform. Our customers use the entity-event approach in diverse settings like a call center, a hospital, in insurance, in aviation and even in finance. AllegroGraph as an entity-event knowledge graph will accept incoming events, do instant queries and analytics on the new data and then store events and results. AllegroGraph is geared to high speed inserts so in general can keep up with high business loads but for several reasons it is advantageous to couple AllegroGraph with Apache Kafka. Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

Visit our Github example page for more details.