Apache Storm

What is Apache Storm?

Apache Storm is a free and open-source distributed real-time computation system. It's designed to process vast amounts of data, simplifying the process of operating and maintaining distributed systems. Apache Storm is used for processing real-time big data analytics, online machine learning, distributed RPC, ETL, and more.


Apache Storm was originally created by Nathan Marz and team at BackType. The project was open sourced after being acquired by Twitter. It became a top-level Apache project in 2014.

Functionality and Features

Apache Storm offers robust and fault-tolerant features that cater to the needs of processing large-scale real-time data. Some of these features include:

  • Hadoop Integration: Apache Storm works seamlessly with Hadoop to analyse and transform data.
  • Real-Time Processing: It’s capable of processing over a million tuples per second per node.
  • Fault Tolerance: In case a node dies, the system automatically reassigns tasks to other nodes.
  • Scalability: You can dynamically scale the system by adding or removing resources without interrupting the processing.


Apache Storm uses a master-slave architecture. The master node runs a daemon known as Nimbus, and worker nodes run a daemon known as a Supervisor. The Nimbus is responsible for distributing code across the nodes, assigning tasks, and monitoring failures. The Supervisors listen for tasks from the Nimbus and execute them with separate JVM processes called 'workers'.

Benefits and Use Cases

Apache Storm provides real-time data processing and analytics, which is vital in today's big data landscape. It's used in real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Its ability to process massive amount of data in real time makes it ideal for monitoring, alerting, and real-time decision making.

Challenges and Limitations

While Apache Storm is powerful, it's not without its limitations. The system requires manual intervention for optimal partitioning and repartitioning of data. Also, it doesn't support event time processing and windowing out of the box, and doesn't have a built-in mechanism for persisting its internal state.

Integration with Data Lakehouse

In the context of a data lakehouse, Apache Storm can be used to ingest and process real-time data before it's stored into the data lakehouse. Its real-time processing capacity can enhance the timeliness and quality of data available in the lakehouse, thereby improving data analytics and business intelligence efforts.

Security Aspects

Apache Storm provides security features like authentication, authorization, and encryption over the wire. It supports different types of authentications including Kerberos, SASL, and SSL encryption to ensure secure data transfer.


Apache Storm's performance shines in real-time data processing, as it can handle millions of tuples per second per node. Its distributed nature allows it to manage large volumes of data across clusters, making it a scalable solution for big data processing.


Is Apache Storm open source? Yes, Apache Storm is an open source project under the Apache Software Foundation.

What kind of data does Apache Storm process? Apache Storm can process any type of data, and it's particularly useful for real-time and streaming data processing.

Does Apache Storm provide data security? Yes, Apache Storm provides security features such as authentication, authorization, and SSL encryption for secure data transfer.

Can Apache Storm be integrated with a data lakehouse? Yes, Apache Storm can be integrated with a data lakehouse to ingest and process real-time data before it's stored in the lakehouse.

What are some limitations of Apache Storm? Some limitations of Apache Storm include the necessity for manual data partitioning, lack of support for event time processing and windowing, and no built-in mechanism for persisting its internal state.


Tuple: In the context of Apache Storm, a tuple is an ordered list of elements. In Storm, data is processed as streams of tuples.

Nimbus: In Apache Storm architecture, the Nimbus is a daemon that runs on the master node, responsible for code distribution, task assignments, and monitoring failures.

Supervisor: In Apache Storm architecture, the Supervisors run on worker nodes, listening for tasks from Nimbus and executing them with workers.

Data Lakehouse: A data lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses. It enables a single, unified platform for all types of data and all kinds of analytics.

Hadoop: Hadoop is an open-source software platform for distributed storage and distributed processing of very large data sets on computer clusters.

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