Catalog
technology#Data#Platform#Real-time#Streaming

Apache Kafka

Kafka is a distributed streaming platform designed for processing real-time data streams.

Kafka enables the publishing, storing, and processing of data streams in real-time.
Established
Medium

Classification

  • Medium
  • Technical
  • Technical
  • Advanced

Technical context

Databases for data storage.Analytics tools for data processing.Monitoring tools for Kafka oversight.

Principles & goals

Promote event-driven architecture.Enable scalability through distributed systems.Support real-time data processing.
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data loss due to misconfiguration.
  • Performance issues under high load.
  • Complexity can lead to errors.
  • Regular monitoring of the Kafka instance.
  • Use partitions for load distribution.
  • Document configuration and processes.

I/O & resources

  • Data sources such as sensors or applications.
  • Configuration settings for Kafka.
  • Network infrastructure for data transmission.
  • Processed data streams.
  • Events for other systems.
  • Analytical reports.

Description

Kafka enables the publishing, storing, and processing of data streams in real-time. It is commonly used in modern architectures to integrate and process data between various systems.

✔

  • High availability and fault tolerance.
  • Real-time data processing.
  • Scalability for large data volumes.

✖

  • Complexity in setup and management.
  • Requires knowledge of distributed systems.
  • Not suitable for all use cases.

  • Throughput

    Number of messages processed per second.

  • Latency

    Time taken to process a message.

  • Error Rate

    Proportion of failed messages.

Real-time Analysis of Social Media Data

A company uses Kafka to analyze data from social media platforms in real-time and identify trends.

Centralized Logging

A company uses Kafka to centrally collect and analyze log data from various applications.

Real-time Fraud Detection

A financial institution uses Kafka to monitor transaction data in real-time and detect potential fraud.

1

Set up the Kafka environment.

2

Configure the data sources.

3

Implement producers and consumers.

⚠️ Technical debt & bottlenecks

  • Insufficient documentation.
  • Outdated configurations.
  • Lack of personnel training.
Data collision issues.Network bottlenecks.Complexity of data integration.
  • Using Kafka for batch processing.
  • Ignoring error messages.
  • Insufficient testing before production.
  • Assuming Kafka solves all problems.
  • Overlooking the need for monitoring.
  • Underestimating the complexity of integration.
Knowledge of distributed systems.Experience with data integration.Skills in data analytics.
Event-driven communication.Distributed data processing.Real-time data availability.
  • • Requires a stable network connection.
  • • Must be operated in a suitable infrastructure.
  • • Compliance with data protection regulations.