Apache Flume vs Kafka: What are the differences?

# Introduction

1. **Scalability**: Apache Flume is limited in scalability compared to Kafka. Flume is typically used for collecting and moving log data to centralized storage while Kafka is designed for high-throughput, real-time data feeds.
2. **Data Persistence**: Apache Kafka provides data persistence by storing messages on disk, making it more suitable for scenarios where data loss prevention is crucial. In contrast, Flume relies on external storage solutions for message durability.
3. **Processing Guarantees**: Kafka offers strong processing guarantees through mechanisms like message acknowledgments and configurable replication, ensuring data reliability. Flume, on the other hand, does not provide as robust guarantees for data processing.
4. **Data Transformation**: Apache Flume offers built-in functionality for simple data transformations during data ingestion, making it suitable for straightforward ETL processes. Kafka, however, primarily focuses on the transportation of data without extensive transformation capabilities.
5. **Community Support**: Kafka has a larger and more active community compared to Flume, resulting in more frequent updates, better documentation, and a wider range of plugins and integrations available for users.
6. **Use Cases**: While Apache Flume is commonly used for log aggregation and simple data movement tasks within organizations, Apache Kafka is preferred for real-time data streaming applications, event sourcing, and building data pipelines.

In Summary, Apache Flume and Apache Kafka differ in aspects such as scalability, data persistence, processing guarantees, data transformation capabilities, community support, and preferred use cases.