Big Data Real-Time Clickstream Data Ingestion Paradigm for E-Commerce Analytics

E-Commerce websites track visitors browsing records to uncover their intent and preferences for effective online marketing. User clicks or clickstream is a series of page requests which generates a URL on each click event. Clickstream data is an information trail a user leaves behind while visiting websites. Click URLs can be graphically represented for clickstream reporting. In this work, we study Big Data real-Time clickstream data ingestion model in e-commerce Domain, which builds on top of standard Big Data tools like Kafka, Flume, Spark and Cassandra. In particular, we focus on high velocity, fault-tolerant streaming data acquisition pipelines in a distributed setup rather than mining and searching patterns in it.