Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
| Product | Market Share (%) |
|---|---|
| Apache Kafka | 3.9% |
| Apache Flink | 14.4% |
| Databricks | 11.8% |
| Other | 69.9% |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Databricks | 4.1 | 11.8% | 96% | 91 interviewsAdd to research |
| Confluent | 4.1 | 7.8% | 95% | 25 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 17 |
| Large Enterprise | 34 |
| Company Size | Count |
|---|---|
| Small Business | 177 |
| Midsize Enterprise | 95 |
| Large Enterprise | 446 |
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
Uber, Netflix, Activision, Spotify, Slack, Pinterest
| Author info | Rating | Review Summary |
|---|---|---|
| Technology Leader at eTCaaS | 4.5 | I worked with Apache Kafka for financial and OTT platforms, primarily for data streaming. It's valuable for propagating data in motion but could improve performance, aiming for microseconds instead of milliseconds. No other solutions were considered and deployment is cloud-based. |
| Senior Manager at Timestamp, SA | 4.5 | I've used Apache Kafka since 2018 for high-volume stream data and transactions; it's improved greatly over time, though scaling and the interface need work. Setup is now simpler, and I rate it 9 out of 10 overall. |
| Works | 3.5 | In our Telco projects, Apache Kafka efficiently handled large volumes of streaming data, particularly for real-time customer airtime purchases. Although its usability could be enhanced, it outperformed RabbitMQ for our needs, despite no immediate ROI observed. |
| DevOps Engineer | 4.0 | In our big project, Apache Kafka is essential for message exchange. I value its speed and security with AWS, though we could benefit from an integrated UI. Its scalability suits varying throughput needs, and I have observed some ROI. |
| R&D Director at a tech vendor with 201-500 employees | 4.5 | I work for an observability company utilizing Kafka for real-time data processing and aggregation. Kafka's streams client is invaluable for transformations without a separate engine. However, fine-tuning is necessary for optimal architecture, and scaling down can be challenging. |
| Team Lead, Data Engineering at Nesine.com | 4.5 | We use Apache Kafka for real-time scenarios, making it ideal for detecting website anomalies. Its speed, stability, scalability, and fault tolerance are valuable, though config management needs improvement. Apache Airflow replaced Cron Tab for easier task management. |
| Sr. Lead - Engineering at GlobalLogic | 4.5 | We primarily use Kafka for event streaming, valuing its scalability, language integration, and stability. While we've seen increased productivity, the UI could be improved for better user appeal. We haven't considered other streaming solutions. |
| Group Manager at a media company with 201-500 employees | 4.5 | In my experience with Apache Kafka, it is effective for real-time data streaming, notably benefiting fraud detection in the banking sector. However, improvements are needed in handling large data volumes, restart capabilities, and resource monitoring to enhance performance and integration. |
