Apache Kafka

What is Apache Kafka?

Apache Kafka is an open-source platform developed by LinkedIn and later donated to the Apache Software Foundation. It is designed to handle real-time data feeds with high throughput and low latency. Kafka's core use cases include activity tracking, log aggregation, stream processing, and event sourcing.


Apache Kafka was first developed by LinkedIn in 2011 to resolve the challenges of data flow latency. Apache Kafka became a part of the Apache project in 2012 due to its popularity and diversified use cases. Today, it's widely adopted by numerous large-scale organizations, including Netflix, Airbnb, and Uber.

Functionality and Features

Apache Kafka's key features include its ability to process trillions of events per day, support real-time and batch processing, and provide durable storage with replication and fault tolerance.

  • Stream Processing: Kafka can be used to build real-time streaming applications that can transform or react to the streams of data.
  • Fault Tolerant: Kafka can handle failures with the brokers in a Kafka cluster with no data loss.
  • Durability: Kafka uses a distributed commit log, enabling messages persist on disk as fast as possible providing intra-cluster replication to support data durability.
  • Performance: It has high throughput for both publishing and subscribing messages even in high volume traffic.


Kafka operates based on the publisher-subscriber model and the storage layer is essentially a "massively scalable pub/sub message queue architected as a distributed transaction log."

Benefits and Use Cases

Kafka is used in real-time analytics, transforming and reshaping data, and making real-time decisions. Some popular use cases include message brokering, activity tracking, metrics collection and monitoring, and log aggregation.

Challenges and Limitations

Despite its benefits, Kafka has several limitations. It requires careful maintenance, as the system is complex with many configurations. Kafka also lacks advanced querying capabilities and, although it supports stream processing, it isn't optimized for large data analytics.

Integration with Data Lakehouse

In a data lakehouse architecture, Kafka serves as a reliable source of real-time data. For instance, Kafka can ingest streaming data into a data lakehouse, where it can later be processed for analytics. It supports decoupling of data pipelines and systems, allowing for flexible data architecture.

Security Aspects

Kafka includes features for authentication via Kerberos, authorization through Access Control Lists, and encryption with SSL/TLS. However, enabling these features requires careful setup and maintenance.


Apache Kafka has a strong reputation for high performance. It’s designed to deliver messages with low latency and high throughput even in instances of heavy traffic.


What is Apache Kafka mainly used for? Apache Kafka is primarily used for real-time analytics, log aggregation, and event stream processing.
Is Apache Kafka a database? No, Apache Kafka is not a database. It's a distributed streaming platform for publishing and subscribing to streams of records.
How does Apache Kafka handle large amounts of data? Kafka uses partitioning, allowing it to handle large amounts of data and distribute it across a cluster of computers.
What makes Apache Kafka fast? Kafka's speed comes from its ability to handle real-time data feeds through its distributed commit log.


Broker: A server in a Kafka cluster.
Producer: An entity that publishes data to a Kafka topic.
Consumer: An entity that reads data from a Kafka topic.
Topic: A category to which records are published by producers in Kafka.
Partition: A sequence of records, ordered, immutable, and continually appended to a structured commit log.

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