Apache Kafka basics

Apache Kafka is a distributed streaming platform that is widely used for building real-time data pipelines and streaming applications. It's designed to handle high-throughput, low-latency data streaming and provides a way to publish, subscribe to, store, and process streams of records in real-time.

Core Concepts

1.     Producer:

• Role: Producers are applications that send (or "produce") data to Kafka topics.
• Example: An application that sends user activity data to Kafka for real-time analytics.

2.     Consumer:

• Role: Consumers read (or "consume") data from Kafka topics.
• Example: An application that reads data from Kafka topics to perform real-time analytics or update a database.

3.     Topic:

• Role: A topic is a logical channel to which records are sent. Topics are categorized by a name, and producers write data to a topic while consumers read from it.
• Example: A topic named user-activity could be used to collect and distribute user activity logs.

4.     Partition:

• Role: Topics are divided into partitions. Each partition is a log of records and is an ordered, immutable sequence of records that is continually appended to. Partitions allow Kafka to scale horizontally and handle large amounts of data.
• Example: A topic with high traffic might be divided into multiple partitions to balance the load and improve performance.

5.     Broker:

• Role: A Kafka broker is a server that stores data and serves clients. A Kafka cluster consists of multiple brokers that work together to distribute and replicate data.
• Example: A Kafka cluster with multiple brokers provides fault tolerance and scalability.

6.     Cluster:

• Role: A Kafka cluster is a group of Kafka brokers working together. It is the fundamental unit of Kafka's distributed architecture.
• Example: A Kafka cluster can be scaled out by adding more brokers to handle more data and provide redundancy.

7.     Zookeeper:

• Role: Apache ZooKeeper is used by Kafka for distributed coordination. It helps manage Kafka brokers, topics, partitions, and their state.
• Example: ZooKeeper helps in electing the leader of each partition and maintaining metadata about brokers and topics.

8.     Offset:

• Role: An offset is a unique identifier for each record within a partition. Consumers use offsets to keep track of which records they have read.
• Example: A consumer might use offsets to resume processing from where it left off in case of a failure.

 

Key Features

High Throughput

Kafka is capable of handling high-throughput data streams, making it suitable for large-scale data processing.

Scalability

Kafka can scale horizontally by adding more brokers to the cluster. Partitions allow data to be spread across multiple brokers.

Durability and Reliability

Data in Kafka is replicated across multiple brokers to ensure durability and fault tolerance. Records are persisted on disk.

Fault Tolerance

Kafka’s architecture ensures high availability. If a broker fails, other brokers with replicas can take over.

Stream Processing

Kafka Streams is a library that enables real-time stream processing. It allows for building applications that process and analyze data streams in real-time.

 

Use Cases

Real-Time Analytics

Kafka is used for processing and analyzing data in real time. For example, it can handle data from web applications to provide real-time analytics.

Data Integration

Kafka acts as a central hub for integrating data from various sources. It can be used to ingest data from different systems and feed it into data lakes, databases, or other systems.

Event Sourcing

Kafka is often used in event-driven architectures to capture and store events. This allows for building systems that react to events and perform actions based on event data.

Log Aggregation

Kafka can aggregate logs from different services and applications into a central repository, making it easier to analyze and monitor system activity.