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.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Apache Flink

Apache Flink

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.

Categories:
Development Big Data & Analytics
opensource stream-processing realtime distributed scalable

Apache Flink Features

  1. Distributed stream data processing
  2. Event time and out-of-order stream processing
  3. Fault tolerance with checkpointing and exactly-once semantics
  4. High throughput and low latency
  5. SQL support
  6. Python, Java, Scala APIs
  7. Integration with Kubernetes

Pricing

  • Open Source
  • Pay-As-You-Go

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

Apache Storm

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.

Categories:
Ai Tools & Services Data Processing
realtime analytics distributed faulttolerant

Apache Storm Features

  1. Distributed and fault-tolerant
  2. Processes unbounded streams of data
  3. Real-time analytics and machine learning
  4. Processes data rapidly
  5. Integrates with queueing and database technologies

Pricing

  • Open Source

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