The Apache Storm interview questions and answers blog provides comprehensive coverage on this open-source distributed real-time stream processing framework, from features, architecture and data processing through interviews with key personnel from within its community.

Apache Storm is an open-source distributed streaming platform designed for real-time data. Storm is intended to process streaming information quickly with low latency processing with high throughput processing capability and great flexibility; in addition, its highly scalable nature enables it to manage large volumes of information effectively.

Scala-written and built atop Apache Kafka streaming platform. Designed as an extensible and flexible framework enabling developers to easily add features or tailor it for specific purposes, making the system highly extensible.

This architecture also utilizes the Master-subordinate model, in which one node coordinates data processing while its subordinate nodes participate directly in doing so.

Apache Storm can be employed for various applications, including real-time analytics, continuous intelligence gathering and machine learning.

1. What is Apache Storm used for?

Apache Storm is used for processing data streams in real time.

2. How does Storm divide input data?

Storm divides input data into small steps and defines what each step will do.

3. How can Apache Storm be used in the financial industry?

Apache Storm can be used in the financial industry to detect security frauds, prevent compliance violations, optimize routing pricing, optimize offers, and set pricing based on competitor pricing.

4. What are the challenges posed by big data?

Big data poses challenges in terms of capturing, storing, sharing unstructured data, and analyzing vast amounts of unstructured data.

5. What are the different types of analytics used for unstructured data?

The different types of analytics used for unstructured data are business intelligence and predictive analysis.

6. Outline a system using Apache Storm to analyze social media data for sentiment analysis.

A system using Apache Storm to analyze social media data for sentiment analysis can be designed by parsing the social media feeds, performing sentiment analysis on the text data, and aggregating the results in real time.

7. What is the purpose of batch processing?

The purpose of batch processing is to efficiently process large volumes of data over a period of time.

8. What is the difference between batch processing and real-time processing?

Batch processing collects transactions over time and processes them separately, whereas real-time processing continuously processes data to provide live views.

9. Devise a situation where batch processing would be more suitable than real-time processing.

Asituation where batch processing would be more suitable than real-time processing is processing payroll data for an organization.

10. Compare the advantages and disadvantages of batch processing and real-time processing.

Advantages of batch processing include increased efficiency and the ability to process data during less busy times, while disadvantages of real-time processing include time delays between data collection and results.

11. Evaluate the impact of real-time data mining on a software as a service company.

Real-time data mining can have a positive impact on a software as a service company by improving customer satisfaction, conversion rates, and revenue.

12. Plan a data processing architecture that combines batch processing and real-time processing.

A data processing architecture that combines batch processing and real-time processing could involve using the Lambda architecture, where data is sent to both a batch layer and a speed layer for efficient data management and querying.

13. What are the two main components of Apache Storm?

The two main components of Apache Storm are spouts and bolts.

14. Explain the role of Nimbus in Apache Storm.

Nimbus in Apache Storm is responsible for uploading competitions, sending and distributing code across the cluster.

15. Describe the steps to write great storm topologies in Apache Storm.

To write great storm topologies in Apache Storm, developers must consider five components: tuples, streams, spouts, bolts, process input streams, and produce output streams.

16. How does Apache Storm handle real-time stream processing?

Apache Storm handles real-time stream processing by handling producer and consumer queues between components.

17. Compare and contrast the local mode and remote mode of Apache Storm.

The local mode of Apache Storm is used during development time for testing topologies on one machine, while the remote mode distributes the topology code to different machines.

18. Develop a storm topology for a real-time data analysis dashboard.

To develop a storm topology for a real-time data analysis dashboard, define the spouts and bolts, connect them to process input and produce output streams, and create a directed cyclic graph representation of the overall calculation.

19. What is the purpose of a topology in Storm?

The purpose of a topology in Storm is to specify how data will flow in the system by defining the spouts and bolts.

20.  What are the steps to create a Storm Topology in Java?

The steps to create a Storm Topology in Java are:

1. Create a Maven project with Storm Core dependency.
2. Create spouts and bolts as Java classes.
3. Implement the necessary methods in the spout and bolt classes.
4. Use a TopologyBuilder to define the spouts and bolts and specify the data flow.
5. Submit the topology to a Storm cluster.

21. What is the purpose of declaring output fields in a bolt in Storm?

The purpose of declaring output fields in a bolt in Storm is to specify the structure and format of the tuples emitted by the bolt.

22. Why is it important to explicitly assign groupings in a Storm Topology, instead of relying on sequence?

It is important to explicitly assign groupings in a Storm Topology because it ensures that data from the spout is sent to the appropriate bolts, even if there are multiple instances of bolts.

23. Design a Storm Topology that uses a different type of spout instead of the integer spout.

To design a Storm Topology with a different type of spout, create a new Java class that extends the BaseRichSpout and implement the necessary methods. Modify the use of  the new spout instead of the integer spout.

Thank you for reading our Apache Storm interview questions and answers, we trust they helped in your preparations for an Apache Storm interview.

This blog concludes with several Storm usage recommendations, such as understanding your system’s architecture and design as well as performance/scalability needs, working with experienced teams, and using online resources in order to get started with Storm.