Need advice about which tool to choose?Ask the StackShare community!

Apache Storm

202
282
+ 1
25
AWS Lambda

23.7K
18.4K
+ 1
432
Add tool

AWS Lambda vs Apache Storm: What are the differences?

Key Differences between AWS Lambda and Apache Storm

AWS Lambda and Apache Storm are both popular platforms used for processing and analyzing big data in real-time. However, there are several key differences between the two:

  1. Execution Model: AWS Lambda follows a serverless execution model, where functions are written and deployed to the service without the need to provision or manage servers. On the other hand, Apache Storm follows a distributed execution model, where a cluster of machines is required to run the processing tasks.

  2. Event-Driven vs Stream Processing: AWS Lambda is specifically designed for event-driven computing, where it automatically triggers the execution of a function in response to an event. It works well for processing discrete events and is optimized for low-latency and small-scale operations. Meanwhile, Apache Storm is a stream processing framework that provides a powerful way to process continuous streams of data in real-time. It excels at handling high-velocity, high-volume data streams.

  3. Managed Service vs Framework: AWS Lambda is a fully managed service provided by Amazon Web Services. It allows developers to focus solely on writing the code without worrying about managing infrastructure. On the other hand, Apache Storm is an open-source framework that requires installation, configuration, and management of the underlying infrastructure.

  4. Supported Languages: AWS Lambda supports a wide range of programming languages including Python, Java, Node.js, C#, and Go. It provides flexibility for developers to choose the language they are most comfortable with. In contrast, Apache Storm primarily focuses on Java for writing topologies, although there are some third-party libraries available for other languages.

  5. Scalability: AWS Lambda provides automatic scaling, allowing functions to handle varying workloads without manual intervention. It automatically provisions the required resources based on the incoming requests. Apache Storm also offers scalability, but it requires manual configuration and management of the cluster to handle the load.

  6. Fault-Tolerance: AWS Lambda provides built-in fault tolerance by replicating the function instances across multiple availability zones. If one instance fails, the workload is automatically shifted to another healthy instance. Apache Storm relies on the acknowledgment mechanism to ensure message reliability and handles failures through manual intervention.

In summary, AWS Lambda and Apache Storm differ in their execution models, purpose (event-driven vs stream processing), management approach, language support, scalability, and fault-tolerance mechanisms. Choosing between them depends on specific requirements and use cases.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Apache Storm
Pros of AWS Lambda
  • 10
    Flexible
  • 6
    Easy setup
  • 4
    Event Processing
  • 3
    Clojure
  • 2
    Real Time
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
  • 12
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 6
    Extensive API
  • 6
    Auto scale and cost effective
  • 6
    Easy to deploy
  • 5
    VPC Support
  • 3
    Integrated with various AWS services

Sign up to add or upvote prosMake informed product decisions

Cons of Apache Storm
Cons of AWS Lambda
    Be the first to leave a con
    • 7
      Cant execute ruby or go
    • 3
      Compute time limited
    • 1
      Can't execute PHP w/o significant effort

    Sign up to add or upvote consMake informed product decisions

    - No public GitHub repository available -

    What is Apache Storm?

    Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

    What is AWS Lambda?

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Apache Storm?
    What companies use AWS Lambda?
    See which teams inside your own company are using Apache Storm or AWS Lambda.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Apache Storm?
    What tools integrate with AWS Lambda?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    GitHubPythonNode.js+47
    55
    72425
    GitHubDockerAmazon EC2+23
    12
    6579
    JavaScriptGitHubPython+42
    53
    21956
    What are some alternatives to Apache Storm and AWS Lambda?
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Amazon Kinesis
    Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
    Apache Flume
    It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
    See all alternatives