Understanding the Basics of Apache Kafka

The scalable, fault-tolerant, publish-subscribe messaging system allows you to build distributed applications and powers web-scale internet companies. Since Kafka debuted in 2010 on LinkedIn, its value has caused the platform to become popular. Take a look at why Kafka is so popular and how it can help your business.

Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. It’s a broker-based solution that maintains data streams as records in Kafka clusters of servers. Data streams are recorded across multiple server instances in topics to provide data persistence.

There are several key use cases for Apache Kafka. It’s one of the fastest-growing, open-source messaging solutions for real-time log streaming. Apache Kafka is suited for applications that rely on reliable data exchange between disparate data sources, real-time data streams for processing, the ability to partition messaging workloads, and native support data and message replay.

The Apache software foundation was designed with three key requirements in mind: to provide a publish-subscribe messaging platform for data distribution and consumption, long-term storage of data and data replay, and access to real-time data for real-time stream processing. The message broker provides seamless streams of messages, time-based data retention, a foundation approach for stream processing, and native integration support.

built-in stream processing, including the ability to process streams of events with joins, aggregations, filters, and more through event-time and replication processing. Kafka integrates with hundreds of event sources and sinks, such as JMS.

Permanent storage keeps data streams safe in a distributed, durable, fault-tolerant cluster, and high availability efficiently stretches and connects Kafka clusters across geographic areas. Permanent storage keeps data streams safe in a distributed, stable, fault-tolerant group, and high availability efficiently extends and connects Kafka clusters across geographic regions. The high throughput of Kafka streams allows you to deliver messages with latencies as low as 2ms. It features Kafka clusters’ scalability of up to a thousand brokers so that you can expand and contract data storage and processing.

log data structure. The log refers to a time-ordered, append-only sequence of data inserts. Databases typically change events in a record, but with Kafka, messages are written to a specific Kafka topic where the user group can read and interpret the data. Kafka is a good fit for big data ingestion, but the log data structure is also useful for applications related to the Internet of Things, microservices, and cloud-native architecture. Kafka also features log compaction, which preserves events for the lifetime of an application.

What Kafka doesn’t do.

The log data structure makes Kafka stand out from traditional message brokers because it’s fast. Instead of using individual message IDs, Kafka addresses messages by their offset in the log. It also doesn’t track the consumer group activity on each Kafka topic. The confluent platform lightens an application’s workload by allowing each Kafka consumer to specify offsets to receive message streams in order. Kafka keeps a given topic for a specified period rather than having deletes. Thanks to Kafka’s scalability, the message broker is popular in big data as a reliable way to ingest and move a high volume of data quickly. Kafka is useful for batch analytics, real-time analytics, the ingestion of APIs, and data stores.