This document discusses using Kafka for processing big data and check-in locations. It proposes using Kafka to build a "Gogobot Checkins Heat-Map" service that collects check-in addresses from various microservices at high volumes, geocodes the addresses, and displays the locations as a heat map. Kafka is presented as a good fit because it can reliably handle very high volumes of data ingestion and can scale to support many producers and consumers. The document also briefly outlines LinkedIn's original architecture challenges at high data volumes that led them to adopt Kafka as a better paradigm for messaging that is stateless, fault-tolerant, and can scale without limiting consumer throughput.
5. 5
Key Notes
● Collector Service - Collects checkins as text addresses
– We need to use GeoLocation ServiceWe need to use GeoLocation Service
● Upon elapsed interval, the last locations list will be
displayed as Heat-Map in GUI.
● Web Scale service – 10Ks checkins/seconds all over the
world (imaginary, but lets do it for the exercise).