- Unified Model: Provides a unified programming model for both batch and stream processing.
- Portability: Enables writing pipelines in multiple languages (Java, Python, Go) and executing them on various runners (Flink, Spark, Google Cloud Dataflow, etc.).
- APIs and SDKs: Rich APIs for creating complex data processing tasks, supporting both bounded and unbounded data.
- Suitable for projects requiring flexibility in choosing the underlying execution engine.
- Ideal for developing pipelines that can be run on different environments without changing the code.
- Stream Processing: Excels at handling high-throughput, low-latency stream processing.
- Stateful Computations: Robust support for stateful stream processing, event time processing, and windowing.
- Scalability and Fault Tolerance: Designed for scalable, fault-tolerant stream processing applications.
- Real-time analytics and monitoring.
- Event-driven applications and complex event processing.
- Applications requiring advanced state management and event time processing.
- Batch and Stream Processing: Provides APIs for both batch (using RDDs and DataFrames) and stream processing (using Spark Streaming).
- Ease of Use: High-level APIs in Java, Scala, Python, and R. It supports SQL queries, machine learning (MLlib), graph processing (GraphX), and more.
- Unified Engine: Can process data in-memory for fast computation and supports large-scale data processing.
- Batch processing of large datasets.
- Machine learning and advanced analytics.
- Stream processing, but generally with higher latencies compared to Flink.
- Message Broker: Distributed event streaming platform, primarily used as a messaging system.
- Real-time Data Pipelines: Enables building real-time data pipelines and streaming applications.
- Durability and Scalability: Ensures data durability and can scale to handle high-throughput data streams.
- Messaging and event streaming.
- Log aggregation and real-time analytics.
- Building data pipelines for moving data between systems.
- Apache Beam: Unified model for batch and stream processing; requires an underlying execution engine.
- Apache Flink: Primarily focused on stream processing with strong support for batch processing as well.
- Apache Spark: Strong in batch processing with capabilities for stream processing (Structured Streaming).
- Apache Kafka: Primarily a messaging system, but with Kafka Streams, it can perform stream processing.
- Apache Beam: Depends on the chosen runner; generally more flexible than performant.
- Apache Flink: Low latency, high throughput, optimized for stream processing.
- Apache Spark: In-memory processing for fast batch jobs; stream processing with moderate latency.
- Apache Kafka: High throughput, designed for real-time data ingestion and event streaming.
- Apache Beam: Limited state management; relies on the capabilities of the runner.
- Apache Flink: Advanced state management, ideal for complex stream processing.
- Apache Spark: Limited state management compared to Flink, primarily focused on micro-batch processing.
- Apache Kafka: Stateless in nature; stateful stream processing via Kafka Streams, but not as advanced as Flink.
- Apache Beam: Integrates with multiple execution engines (Flink, Spark, Dataflow).
- Apache Flink: Integrates with various data sources and sinks; part of the broader Apache ecosystem.
- Apache Spark: Extensive ecosystem including MLlib, GraphX, and integration with Hadoop, Hive, etc.
- Apache Kafka: Integrates well with numerous data systems; Confluent ecosystem enhances Kafka’s capabilities.
- Apache Beam is best for flexibility and portability across different execution engines.
- Apache Flink is ideal for real-time, low-latency stream processing and applications needing advanced state management.
- Apache Spark is great for large-scale batch processing, machine learning, and graph processing, with good stream processing capabilities.
- Apache Kafka excels as a messaging system and event streaming platform, often used in conjunction with other processing engines like Flink and Spark for comprehensive data processing solutions.