Apache Kafka is a message broker that has served as a de-facto open-source standard for real-time data for nearly a decade. This message broker is ideal for use cases that rely on real-time data integration across multiple applications. When Kafka is integrated into a real-time analytics infrastructure, it serves as the basis for high-business value applications. This leads to greater business intelligence that can better leverage big data analytics to make business decisions. There are three key areas of innovation when it comes to the Kafka broker: data virtualization, streaming business intelligence for self-service analytics, and Artificial Intelligence as it applies to scalability in data science.
What is Apache Kafka?
What is Apache Kafka? Basically, it’s an open-source publish-subscribe message platform designed to handle real-time data feeds for distributed streaming, developing data pipelines, and on-demand replay of data streams for operational scalability. Kafka is a broker that maintains streams of data as records within a cluster of servers that span multiple data sources and provide data persistence.
The Apache software foundation is designed to provide a publish-subscribe messaging model for data distribution and consumption, allow for long-term data storage, and support the access of data in real-time for real-time stream processing. The Kafka broker is comprised of Kafka topics that can be subdivided into a series of queues called partitions. It provides persistence by maintaining Kafka clusters that provide scalability as producers and consumers publish and subscribe to Kafka topics.
Use Cases for Apache Kafka
Kafka is one of the most popular open-source messaging systems on the market given its benefits for distributed systems. It’s ideal for different applications in need of reliable data exchange between disparate data sources, the ability to partition messaging workloads, real-time data streaming and processing, and self-service options data and message replay.
This distributed streaming platform was designed to address the limitation of messaging systems, such as managing the message queue and publish-subscribe patterns. Kafka has improved upon the best practices of messaging systems by defining a distributed cluster architecture for scalability and reliability consistent with other messaging systems in the big data open-source realm. It set the standard for reliable storage with replication and configurable persistence settings and introduced stream processing services that support real-time analytics.
New technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are being adopted across industries to leverage big data and gain deeper business intelligence. Artificial Intelligence simulates human intelligence by detecting patterns in large volumes of data to learn how to complete tasks. AI provides reliable automation of manual tasks, adds intelligence to existing products, adapts through progressive learning algorithms, analyzes more data for deep learning, and leverages the most out of big data.
AI offers assistance advantages to call centers that prioritize delivering a great customer experience. AI and bots offer automation, customer service agent empowerment, and improved customer interactions that enhance human interaction. An AI call center can decrease wait times, speed up customer service, and improve customer satisfaction by increasing the efficiency of the call center.
Call center agents can use speech recognition to authenticate customers efficiently and deliver quality customer service faster. Bright Pattern’s virtual agents and Interactive Voice Response (IVRs) are powered by Natural Language Processing that can route callers with a live agent through any digital channel in real-time for a self-service approach to queries. AI empowers contact center agents to deliver better customer support in less time than a traditional omnichannel platform that doesn’t offer full self-service and automation.
Kafka suits streaming architectures that consume a large volume of data into analytics clusters, stream extract, transform, load (ETL) for platform integration, Internet of THings (IoT) sensor data processing, analytics, and device management. Real-time applications powered by an event-driven architecture that provides automation benefit from the implementation of Kafka.