The explosion of data volume and velocity since 2015 has led to a shift in consumer expectations, with real-time experiences from companies like Uber, Grubhub, Amazon, and Netflix setting a new standard for digital products. Timely, individualized experiences have become table stakes in marketing, driving the need for high-performance personalization and recommendation algorithms. In response to this trend towards low latency, businesses are increasingly seeking robust, efficient data tools and frameworks to support real-time use cases. This emphasis on real-time data integration has sparked the rise of event streaming.
The demand for real-time visibility and response has been driven by changing consumer expectations, as noted by Decodable CEO, Eric Sammer. The current expectation for up-to-the-second tracking of orders and activities was accelerated by the COVID-19 pandemic. This shift towards real-time data has made real-time experiences a necessity for companies like GrubHub and Netflix.
In this article, we will cover the fundamentals of streaming data and discuss two common types: time series streaming data and event-driven streaming data. Then, we will compare Apache Kafka with RudderStack and highlight their strengths. We will also explore how these two tools complement each other, and how RudderStack’s Kafka integrations provide reliable connectors that make it easy to unify customer data with other sources and create complete customer profiles to send downstream destinations.
Time series streaming data focuses on capturing data points at successive time intervals, allowing for tracking changes over time, spotting trends, and detecting anomalies. On the other hand, event-driven streaming data places the primary focus on the events that occur, especially in applications following the Event-Driven Architecture (EDA) design pattern.
Apache Kafka, a pioneer in streaming data, provides a robust, scalable, distributed messaging system grounded in the event stream model. Kafka allows users to publish and subscribe to streams of events, store events for as long as necessary, and process them in real time. The data structure in Kafka is defined by a schema and can be registered and adapted as needed.
Using Kafka for eCommerce event stream data involves setting up Kafka, configuring it for your use case, and integrating your data stream from your data source. For example, if you own an e-commerce web store, you can capture user behavior data and publish it to Kafka producers, which will then store the data within partitions of a specific topic. Kafka consumers can then subscribe to these topics and process the data for real-time analytics or personalization services.
Overall, the growth in data volume and velocity has created a demand for real-time experiences, driving the need for efficient data tools and frameworks. Apache Kafka and RudderStack are two powerful tools that can help businesses meet the demands of real-time data integration and processing.