Apache Kafka
Apache Kafka

Apache Kafka is a messaging system that when integrated into a real-time analysis infrastructure serves as the foundation for high-business value applications. Such integration results in a continuous intelligence environment that elevates analytics and aids in business decisions. Kafka provides data virtualization, business intelligence streaming for self-service analytics, and AI-augmented intelligence, and data science scalability.

The message broker was designed to address the limitations of traditional messaging systems, including the use of a message queue and publish-subscribe patterns. Units of data are called Kafka messages, which are created by Kafka producers and subscribed to by Kafka consumers. Kafka improves and expands upon best practices of messaging systems by defining a distributed cluster architecture that provides scalability and reliability comparable with other platforms in the big data open-source realm. It offers scalable, reliable storage that supports replication and configurable persistence settings and stream processing services that support real-time analysis.

Apache Kafka 101

What is Apache Kafka used for? Apache Kafka is an open-source distributed streaming platform designed to handle real-time streams of data for distributed streaming, pipeline development, and replication of data streams for operational scalability. The broker-based solution maintains streams of data as records within server clusters. Kafka servers can span multiple data centers and data sources for data persistence by storing streams of records across multiple servers in Kafka topics.

The Apache software foundation is built on several core concepts. Kafka topics are addressable abstractions that show interest in a given data stream. These topics can be subdivided into order queues called partitions that can be scaled to share workloads. Kafka producers define what Kafka topic a message should be published on and Kafka consumers manage how a workload is processed by subscribing to topics.

Use Cases for Apache Kafka

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Kafka is the most popular open-source distributed messaging system on the market thanks to the superior logging mechanism it provides distributed systems. The message broker is purpose-built for real-time log streaming and is ideally suited for applications that need reliable data exchange between disparate systems, the ability to partition workloads to meet customer demand, real-time streaming for data processing, and support for data replay.

Contact centers rely on contact center solutions to help exceed customer expectations and deliver an exceptional customer experience. An omnichannel contact center solution allows agents to connect with customers on any communication channel and switch seamlessly across any digital channels during a live chat. Agents can connect with customers via phone calls, web chat, SMS, live chat, mobile apps, and social media. An omnichannel experience streamlines the customer journey using a unified architecture that manages customer data to maintain context and create a personalized customer experience.

Bright Pattern offers contact center solutions that ensure customer needs are met through a seamless experience. Omnichannel contact centers that integrate artificial intelligence for automation can monitor agent productivity, ensure consistent customer interactions, and implement workforce optimization. The omnichannel solution features a unified agent dashboard, CRM integration, intelligent routing, IVR, built-in quality management, and drag and drop scenario builder. Call centers can add new digital channels easily, eliminate the frustration of latency, utilize sentiment analysis to rate a customer’s mood, utilize speech and text analysis, and gain valuable insights from KPIs and analytics.

Apache Kafka is suited for different applications built on an event-driven architecture, including real-time message distribution and event streaming. Communication is essential to data streams and processing within event-driven architecture, and the automation of communication streamlines distribution. Real-time notifications can detect anomalies and provide clear insights that enhance the customer experience. The message broker serves as the nervous system for data distribution and communication across disparate data stores while increasing time to market and reducing costs.

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