Kafka’s distributed processing, scalability, and fault tolerance led TigerGraph to make it part of our internal design from day one. Our Kafka data ingestion connector is built-in; nothing to install. Based on the trusted Kafka Connect framework, the TigerGraph Kafka Connector ensures seamless and reliable data integration.
Today’s data lakes — or lakehouses — marry storage capabilities of traditional data lakes with data processing capabilities of Spark. A Spark-based connector is a natural fit for these environments. The TigerGraph Spark connector connects directly to Spark, transforming dataframes into graph data. You can also use it as a high-throughput, bidirectional portal to transport data to and from a wide range of data platforms.
The company quickly saw the benefits of using graph database and analytics to manage its data, but quickly ran into problems scaling with its initial graph database vendor. Loading the data took a lot of time and once it was loaded, computing either didn’t finish or was extremely slow. TigerGraph provided Amgen with the speed and scale they were looking for.
