Kafka uses data replication to provide fault tolerance and high availability. Data replication ensures that data is stored on multiple brokers, so that if one broker fails, data can still be accessed and processed by other brokers. When data is written to a Kafka topic, it is replicated to multiple brokers according to the configured … Read more
The Kafka Schema Registry is a component of the Kafka ecosystem that provides a centralized repository for the management of Avro schemas used in Kafka messages. The purpose of the Schema Registry is to ensure that data sent between producers and consumers is properly formatted and compatible between different systems. Here are some of the … Read more
Achieving exactly once message processing in Kafka is a complex task, but Kafka provides several features and mechanisms that can be used to ensure that messages are processed exactly once. Here are some of the ways that exactly once processing can be achieved in Kafka: 1. Idempotent producers: Kafka 0.11 introduced support for idempotent producers, … Read more
Kafka Connect source and sink connectors are plugins that allow developers to easily integrate Kafka with external systems, making it possible to read data from external systems and publish it to Kafka (source connectors), or to read data from Kafka and write it to external systems (sink connectors). Source connectors are used to collect data … Read more
Kafka Streams is a lightweight Java library that is part of the Kafka ecosystem, and it is used for building real-time stream processing applications. Kafka Streams provides a simple and powerful way to process and analyze data in real-time as it flows through Kafka. The role of Kafka Streams in the Kafka ecosystem is to … Read more
Kafka handles backpressure through its built-in flow control mechanisms, which are designed to ensure that producers do not overwhelm consumers with too much data. Kafka uses a combination of buffering and throttling to control the flow of data between producers and consumers. Producers write data to Kafka topics, and consumers read data from those topics. … Read more
Kafka and Apache Pulsar are both distributed messaging and streaming platforms that are designed to handle high volumes of data and support real-time data processing and streaming applications. However, there are some key differences between the two platforms. 1. Architecture: Kafka and Pulsar have different architectures. Kafka is designed as a distributed log-based messaging system, … Read more
Kafka supports real-time stream processing through its integration with Kafka Streams, a lightweight Java library for building real-time stream processing applications. Kafka Streams allows developers to process and analyze data in real-time as it flows through Kafka, without needing to use a separate processing engine or framework. Here are some of the ways that Kafka … Read more
Kafka Connect is a framework and set of APIs that allows developers to easily and reliably integrate Kafka with external systems. Kafka Connect enables data integration between Kafka and other systems, allowing data to flow into and out of Kafka in a scalable and fault-tolerant manner. The purpose of Kafka Connect is to simplify the … Read more
Kafka is designed to handle high throughput and scalability by utilizing a distributed architecture that allows it to easily scale horizontally by adding more brokers to the cluster. This distributed architecture is combined with a number of features and optimizations that allow Kafka to handle millions of messages per second. Here are some of the … Read more