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.