์‹ค์‹œ๊ฐ„ ์›€์ง์ด๋Š” ๋ฐ์ดํ„ฐ๊ฐ€ ๊ฐ€์ ธ๋‹ค ์ค„ ๊ฐ€์น˜, Data in Motion Tour์—์„œ ํ™•์ธํ•˜์„ธ์š”!

Comparing three data ingestion approaches where Apache Kafka integrates with a distributed graph database in real time

ยซ Kafka Summit Europe 2021

Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customersโ€™ data architecture. We have seen the integration in three different layers around TigerGraphโ€™s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraphโ€™s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.

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