In the era of real-time data, Apache Kafka is not just a message broker, but a comprehensive streaming platform. It is designed to handle various applications beyond just transferring data from A to B. Kafka provides a resilient, fault-tolerant, and highly scalable ecosystem that empowers businesses to create real-time data pipelines and streaming applications.
Apache Kafka can cater to a variety of use cases, transforming the way businesses handle their data. Here are a few key applications of Kafka:
Kafka’s decoupled nature enables loose coupling between microservices. By leveraging Kafka as a messaging layer, microservices can communicate and exchange data without strong dependencies, promoting system flexibility and scalability.
Kafka is suitable for real-time analytics and monitoring. Services can publish relevant metrics, logs, or events to Kafka, and analytics or monitoring systems can consume these streams to gain insights, detect anomalies, or trigger alerts.
Kafka can serve as a foundation for building stream processing applications. By integrating Kafka with stream processing frameworks like Apache Flink, Apache Samza, or Apache Spark, you can perform real-time analytics, transformations, aggregations, and complex event processing on the data streams.
For data exchange and communication between data domains in a Data Mesh architecture Kafka can act as a backbone. Teams can publish data events, changes, or streams to Kafka, which can then be consumed by other teams or downstream data products.
With Kafka Connect, Apache Kafka enables you to migrate data across heterogeneous database systems using Change-Data-Capture (CDC) pipelines. Next to moving large datasets across your systems this enables you to easily make data from your existing databases available for stream processing.
When migrating from a legacy system to a new architecture, Kafka can act as an intermediary, allowing data to be ingested from the target system and transformed into a format compatible with the legacy system ensuring a fallback possibility at any time.
Apache Kafka comes with powerful built-in components that enhance its capabilities as a real-time data streaming platform. Here’s an overview of a few key components:
Apache Kafka is the core component of the streaming ecosystem. It is a message broker that distributes data to consumers with high throughput and fault tolerance. Consumers utilize a poll-based approach to read data from Kafka.
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other data systems. It provides a framework for building large scale, real-time data pipelines by enabling the connection of a wide variety of data sources and sinks into Kafka.
ksqlDB is an open-source streaming SQL engine by Confluent that enables real-time data processing against Apache Kafka. It provides an interactive SQL interface for stream processing on Kafka, making it easier to build real-time, scalable streaming applications.
Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It offers a high-level stream processing DSL, allowing users to perform transformations on incoming data, such as map, filter, and aggregate.
Software Development Kits (SDKs) for Apache Kafka provide developers with a set of tools and libraries in various programming languages to facilitate the interaction with Kafka clusters. These SDKs support numerous languages such as Java, C/C++, Python, C#, Node.js, Go, and more.
Fully managed Apache Kafka hosting services, like Confluent Cloud, AWS MSK, AWS MSK Serverless and Aiven, offer scalable, reliable, and secure Kafka hosting without the operational overhead. These platforms handle the Kafka infrastructure management tasks such as setup, maintenance, scaling, and recovery.
We provide end-to-end Apache Kafka consulting and integration services to help you unlock the full potential of your real-time data. From strategy and planning to implementation and optimization, our team of experts will guide you through every step of the process, ensuring seamless integration of Apache Kafka into your business operations.
Learn how to test Flink SQL scripts with either unit or integration tests. ...
In this post, we present a case study concerning one of our customer in the domain of Internet of ...
Learn how to connect Kafka to Apache Druid and Superset to produce awesome BI ...