Apache Spark vs Apache Storm

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

Apache Spark is a Ai Tools & Services solution with tags like distributed-computing, cluster-computing, big-data, analytics.

It boasts features such as In-memory data processing, Speed and ease of use, Unified analytics engine, Polyglot persistence, Advanced analytics, Stream processing, Machine learning and pros including Fast processing speed, Easy to use, Flexibility with languages, Real-time stream processing, Machine learning capabilities, Open source with large community.

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 Spark	Apache Storm
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

Apache Spark

Description: 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.

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 Spark Features

• In-memory data processing
• Speed and ease of use
• Unified analytics engine
• Polyglot persistence
• Advanced analytics
• Stream processing
• Machine learning

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 Spark

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

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 Spark

• Open Source

Apache Storm

• Open Source