Apache Kafka Reviews

Organizations utilizing Apache Kafka achieved quick returns in high-value projects, leveraging real-time capabilities. Cost savings stemmed from its open-source nature allowing internal customizations. While challenges in configuration exist, Kafka enhanced productivity and insightful decision-making, enabling critical data-driven actions within months instead of years. Kafka buffered high-traffic data efficiently, yielding substantial returns. Although less significant in batch processing environments, its extensive data processing contributed positively. Overall, Apache Kafka offered significant flexibility and time-saving benefits.

Enterprise users report that Apache Kafka is primarily open-source and free of charge, making costs minimal for setup and licensing. However, managed services or third-party support often incur additional expenses, such as fees for Confluent's services, which are considered expensive and based on subscription. For cloud-based solutions, providers like AWS and Azure offer supported Kafka clusters with variable costs. Companies generally find the open-source version more cost-effective, but enterprise features may lead to higher expenses.

• "I was using the product's free version."
• "Apache Kafka is an open-source solution."
• "It's quite affordable considering the value it provides."

Companies use Apache Kafka primarily for data streaming in real-time, facilitating integration, processing, and messaging. It handles high-volume message exchange, enabling systems to manage financial transactions, event monitoring, microservices architecture, and analytics. Organizations deploy Kafka for both cloud and on-premise setups, allowing functional flexibility in sectors like finance, logistics, and IoT. Users leverage Kafka for streaming data between microservices, ETL-like transformations, real-time data ingestion, and as an event bus.

Apache Kafka's customer support is largely community-driven, accessible through forums and documentation. Confluent, founded by Kafka engineers, offers paid support which some find beneficial. However, many users rely on internal expertise or third-party services. Satisfaction varies; some praise responsiveness, others find documentation insufficient. Turnaround time differs by service or region. Many choose not to use support, finding online resources adequate. Some note improvements in recent months, while others feel enhancements are needed.

Initial setup experiences with Apache Kafka vary. Many consider it straightforward, especially with tools like Docker or documentation assistance. Some mention complexities in production setups, particularly involving distributed systems or advanced configurations. Knowledge and familiarity with tools like ZooKeeper can ease the process. Despite challenges in larger or more intricate deployments, experienced users find the setup manageable. Deployment timeframes range from minutes to weeks based on infrastructure and use cases.

Apache Kafka is highly scalable, praised for its ability to handle increased workloads by adding hardware. Users report managing millions of events per second without issues. While some manual management is necessary, Kafka's architecture supports horizontal scaling effectively. It is adaptable for both small and large enterprises, consistently meeting data streaming demands. The cloud version, Confluent, further enhances scalability, making Apache Kafka a leading choice for users needing robust infrastructure.

Apache Kafka demonstrates high stability, with many not experiencing any issues. Minimal bugs or glitches are reported. Performance may vary due to configurations and data volumes. Users mention stable operation under high data sizes and strong performance, but note potential challenges with evolving APIs, complex configurations, and occasional problems when improperly set up. Stability ratings generally range from seven to ten out of ten, highlighting resilience and reliability in handling large-scale data streaming tasks.