What are the differences between Storm and Hadoop?

9 months ago

Benjamin Taylor

2 minutes

Storm and Hadoop are two open source frameworks used for handling big data, but they have some significant differences.

Data processing model:
Storm is a real-time stream processing framework designed to handle live data streams. It is capable of processing endless data streams and can immediately process incoming data.
Hadoop is a batch processing framework that is designed to handle large-scale datasets by splitting the data into smaller chunks for processing and then merging the results together.
Speed of data processing:
Storm processes data faster than Hadoop because it is a real-time processing framework that can handle data streams immediately.
Hadoop’s processing speed is relatively slow because it is a batch processing framework that requires waiting for all data to be processed before outputting results.
Data processing method:
Storm processes data in an event-driven manner, handling it immediately once it arrives.
Hadoop processes data using the MapReduce method, where data is divided into smaller blocks, processed separately, and then merged.
Applicable scenarios:
Storm is suitable for scenarios that require real-time data processing, such as real-time monitoring and real-time analysis.
Hadoop is ideal for scenarios that involve processing large-scale data sets, such as data mining and data analysis.

Overall, Storm is suitable for processing real-time data streams, while Hadoop is suitable for processing large-scale datasets. When choosing which framework to use, it is important to consider the specific business requirements and data processing methods.\