Apache Storm vs Apache Spark

Struggling to choose between Apache Storm and Apache Spark? 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 Spark is a Ai Tools & Services product tagged with distributed-computing, cluster-computing, big-data, analytics.

Its standout features include In-memory data processing, Speed and ease of use, Unified analytics engine, Polyglot persistence, Advanced analytics, Stream processing, Machine learning, and it shines with pros like Fast processing speed, Easy to use, Flexibility with languages, Real-time stream processing, Machine learning capabilities, Open source with large community.

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 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

Apache Spark

Apache Spark

Apache Spark is an open-source distributed general-purpose cluster-computing framework. It provides high-performance data processing and analytics engine for large-scale data processing across clustered computers.

Categories:
Ai Tools & Services Data Processing
distributed-computing cluster-computing big-data analytics

Apache Spark Features

  1. In-memory data processing
  2. Speed and ease of use
  3. Unified analytics engine
  4. Polyglot persistence
  5. Advanced analytics
  6. Stream processing
  7. Machine learning

Pricing

  • Open Source

Pros

Fast processing speed

Easy to use

Flexibility with languages

Real-time stream processing

Machine learning capabilities

Open source with large community

Cons

Requires cluster management

Not ideal for small data sets

Steep learning curve

Not optimized for iterative workloads

Resource intensive