Apache Storm vs Apache Flink

Struggling to choose between Apache Storm and Apache Flink? Both products offer unique advantages, making it a tough decision.

Apache Storm is a Ai Tools & Services solution with tags like realtime, analytics, distributed, faulttolerant.

It boasts features such as Distributed and fault-tolerant, Processes unbounded streams of data, Real-time analytics and machine learning, Processes data rapidly, Integrates with queueing and database technologies and pros including Highly scalable, Fast processing of streaming data, Fault tolerance avoids data loss, Integrates with many data sources and technologies, Open source and free.

On the other hand, Apache Flink is a Development product tagged with opensource, stream-processing, realtime, distributed, scalable.

Its standout features include Distributed stream data processing, Event time and out-of-order stream processing, Fault tolerance with checkpointing and exactly-once semantics, High throughput and low latency, SQL support, Python, Java, Scala APIs, Integration with Kubernetes, and it shines with pros like High performance and scalability, Flexible deployment options, Fault tolerance, Exactly-once event processing semantics, Rich APIs for Java, Python, SQL, Can process bounded and unbounded data streams.

Feature	Apache Storm	Apache Flink
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Development

Product Overview

Apache Storm

Description: Apache Storm is an open source distributed realtime computation system. It processes unbounded streams of data, doing realtime analytics, machine learning, etc. Storm is fault-tolerant and guarantees your data will be processed.

Apache Flink

Description: Apache Flink is an open-source stream processing framework that performs stateful computations over unbounded and bounded data streams. It offers high throughput, low latency, accurate results, and fault tolerance.

Key Features Comparison

Apache Storm Features

• Distributed and fault-tolerant
• Processes unbounded streams of data
• Real-time analytics and machine learning
• Processes data rapidly
• Integrates with queueing and database technologies

Apache Flink Features

• Distributed stream data processing
• Event time and out-of-order stream processing
• Fault tolerance with checkpointing and exactly-once semantics
• High throughput and low latency
• SQL support
• Python, Java, Scala APIs
• Integration with Kubernetes

Pros & Cons Analysis

Apache Storm

Pros

• Highly scalable
• Fast processing of streaming data
• Fault tolerance avoids data loss
• Integrates with many data sources and technologies
• Open source and free

Cons

• Complex to set up and manage
• Requires DevOps skills to operate and tune
• Only guarantees at-least once processing semantics

Apache Flink

Pros

• High performance and scalability
• Flexible deployment options
• Fault tolerance
• Exactly-once event processing semantics
• Rich APIs for Java, Python, SQL
• Can process bounded and unbounded data streams

Cons

• Steep learning curve
• Less out-of-the-box machine learning capabilities than Spark
• Requires more infrastructure management than fully managed services

Pricing Comparison

Apache Storm

• Open Source

Apache Flink

• Open Source
• Pay-As-You-Go