Apache Flink vs Apache Storm

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

Apache Flink is a Development solution with tags like opensource, stream-processing, realtime, distributed, scalable.

It boasts features such as 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 pros including 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.

On the other hand, Apache Storm is a Ai Tools & Services product tagged with realtime, analytics, distributed, faulttolerant.

Its standout features include 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 it shines with pros like Highly scalable, Fast processing of streaming data, Fault tolerance avoids data loss, Integrates with many data sources and technologies, Open source and free.

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

Product Overview

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.

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.

Key Features Comparison

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

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

Pros & Cons Analysis

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

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

Pricing Comparison

Apache Flink

• Open Source
• Pay-As-You-Go

Apache Storm

• Open Source