Top 25 Big Data and Hadoop Interview Questions and Answers

Hello guys, as a Java developer you not just prepare for Java questions but also for different areas related to Java like Big Data and Hadoop depending upon job requirements. If you are going for a Job where Kafka, Apache Hadoop, or Spark is listed as required skill then it make sense to prepare for Big Data Interview questions covering Kafka, Spark and Hadoop. Big Data is e of the most rapidly growing technology is big data. With the rise of Big Data comes a widespread adoption of Hadoop to address important Big Data issues. Hadoop is one of the most widely used frameworks for storing, processing, and analyzing Big Data. As a result, there is always a need for professionals in this industry. However, how do you get a job in the Hadoop field?
At present, one of the fifth companies is moving to Big data analytics. Hence, demand for big data analytics is rising like anything. Therefore, if you want to boost your career, Hadoop and Spark are just the technology you need. This would always give you a good start either as a fresher or experienced.

In the past, I have shared many resources to learn Big Data like these free Big Data courses as well these best Big Data courses and in this article, I am just focusing on Big Data interview questions. 

If you have worked in Big Data then you can easily answer these questions but if you cannot then it make sense to learn or revise essential Big Data concepts by joining one of these courses. 


25 Big Data and Hadoop Interview Questions with Answers

Preparing for the interview is the best thing that you will get from this tutorial. We will discuss mostly 10 questions that are related to big data. If you want to boost your career, Hadoop and Spark are just the technology you need. This would always give you a good start either as a fresher or experienced.

1. What is the difference between relational databases and HDFS in Big DAta?
Here are the key difference between a Relational Database and HDFS:

Relational Database:

1. Relational databases rely on structured data and the schema of the data is always known.

2. RDMS reads are fast because the schema of the data is already known.

3. RDBMS is based on ‘schema on write’ where schema validation is done before loading the data.

4. RDBMS provides limited or no processing capabilities.


Hadoop

1. Hadoop relies on both structured and unstructured data. (any kind of data)

2. No schema validation happens in the process, so Hadoop is also fast.

3. Hadoop follows the schema on reading policy

4. Hadoop allows us to process the data which is distributed across the cluster in a parallel fashion.




2. Explain what is Big Data and five V's of big data?
Big data is a collection of large data sets, which makes it difficult to process using relationship management tools or traditional data processing tools. It is difficult to capture, transfer, analyze and visualize big data.

So let's have a look at the 5V's in such questions.

1. Volume - The volume reflects the amount of data that is exponentially increasing.

2. Velocity - The rate at which data grows, which is extremely fast, is referred to as velocity. Data from yesterday is considered old data today.

3. Variety - The heterogeneity of data types is referred to as variety. In other words, the data collected can be in a number of formats, such as movies, audios, csv, and so on.

4. Veracity - Due to data inconsistency and incompleteness, veracity refers to data that is in dispute or doubtful.

5. Value - Need to add value for the data to get benefits to the organization. So this will add more benefits to the organization.


3. In what all modes Hadoop can be run?
Hadoop's default mode is standalone, which uses a local file system for input and output operations. This mode is primarily intended for debugging and does not support the use of HDFS. In addition, the mapred-site.xml, core-site.xml, and hdfs-site.xml files do not require any unique setting in this mode. When compared to other settings, this one operates much faster.

Pseudo-distributed mode (Single-node Cluster): In this situation, all three files indicated above must be configured. All daemons are running on one node in this scenario, therefore both the Master and Slave nodes are the same.

Fully distributed mode (Multi-node Cluster): This is the Hadoop production phase, where data is used and distributed over several nodes on a Hadoop cluster. Master and Slave nodes are assigned separately.




4. What is HBase?
Apache HBase is a Java-based distributed, open-source, scalable, and multidimensional NoSQL database. It runs on HDFS and gives Hadoop Google BigTable-like capabilities and functionality.


5. Why is it necessary to delete or add nodes from a Hadoop cluster on a regular basis?
The Hadoop frameworks' use of commodity hardware is one of its most appealing aspects. In a Hadoop cluster, however, this results in frequent "DataNode" crashes. Another notable feature of the Hadoop Framework is its ability to scale in response to significant increases in data volume.

6. What are the differences between Hadoop and Spark SQL?
This question was asked to me on a recent Java developer interviews. Basically, If you need to find data quickly, Spark SQL is the way to go. If you need complex processing using a number of tools that operate on datasets in parallel, such as Hive and Pig, and want to combine them, Hadoop is the way to go.

Hadoop is a system for processing massive amounts of data using basic programming concepts across clusters of machines. It can be run on standard PC hardware. Everything you need to transport and store your data is included in the framework, including machine learning, search indexing, and warehousing services (storage).

7. What is a Zookeeper?
This is another important Big Data question which was often asked to senior Developers. ZooKeeper is a distributed coordination service for managing a large set of hosts. It provides a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. 

ZooKeeper is also used by many distributed systems, including Apache Hadoop and Apache Cassandra, to manage their distributed cluster environment. For example, Zookeeper can manages and co-ordinate Kafka cluster, keep records of when a broker enter or leave cluster or dies. 

8. Explain the distributed Cache in the MapReduce framework?
The Distributed Cache is an important feature of the MapReduce framework that allows caching read-only files required by a MapReduce job on each node of the cluster. The files are copied to each node prior to the start of the job which considerably reduce the network overhead in accessing the files from a central location and thus improves the performance. 

The cached files are automatically made available to the Map and Reduce tasks running on each node, providing quick access to the required data without the need for repetitive data transfers. The distributed cache is particularly useful for small, frequently accessed data files, such as lookup tables or reference data.


9. What is Map Reduce in Hadoop?
If you don't know Map reduce is a common programming technique for processing large data sets in parallel across a cluster of computers, using a divide-and-conquer approach. It was first used in Google, which is also responsible for making it popular. 

In Hadoop, it is a core component of the processing framework. The MapReduce program takes in a large data set, divides it into smaller pieces and processes the data in parallel across multiple nodes. The results are then combined and written to the output. MapReduce enables efficient and scalable processing of large data sets in a distributed computing environment.

10. When to use MapReduce with Big Data?
As I said, MapReduce is used for processing large datasets in a distributed computing environment. As a software architect, you can typically use  with Big Data when data processing needs to scale horizontally and run efficiently on a cluster of computers, rather than on a single machine. 

The MapReduce framework separates data processing into two phases: mapping and reducing. Mapping involves transforming the input data into a intermediate format that can be processed in parallel, while reducing involves aggregating the intermediate results into a final output. 

This enables data to be processed in parallel, reducing the overall processing time and allowing for 
efficient handling of very large datasets.


More Big Data, and Hadoop questions for Interviews

If you need more questions for practice, here are few other common questions related to Big Data and Hadoop for interviews:

11. Can you explain How Hadoop Works? Explains Hadoop Architecture and its components (e.g. HDFS, MapReduce, YARN)?


12. What is HDFS and how does it store and process data in a distributed manner?


12. What is YARN and how does it manage and allocate resources in Hadoop?


13. Can you explain different data processing methods available in Hadoop?


14. What is a Hadoop cluster and how does it function?


15. What is a Hadoop Distributed File System (HDFS) block and how is it different from a traditional file system?


16. What is the role of a NameNode in HDFS?


17. What is the role of a DataNode in HDFS?


18. What is a Hadoop job and how is it executed?


19. What is Hadoop MapReduce and how does it process big data?


20. What is a Hadoop framework and what are its components?


21. What are the differences between Hadoop 1 and Hadoop 2?

Here are key difference between Hadoop 1 and Hadoop 2, basically Hadoop 2 is newer version of Hadoop framework and its faster and should be preferred:

1. Hadoop 2 uses YARN (Yet Another Resource Negotiator) for resource management and job scheduling, while Hadoop 1 used the MapReduce framework for this.
2. Hadoop 2 is more scalable than Hadoop 1, as it can handle more nodes and larger clusters.3. Hadoop 2 introduces the concept of NameNode High Availability (HA), which provides automatic failover in the event of a NameNode failure. Hadoop 1 did not have this feature.
4. Hadoop 2 is generally faster than Hadoop 1 due to improvements in the MapReduce framework and the addition of YARN.
5. Hadoop 2 supports multiple data access patterns such as batch processing, interactive SQL, real-time streaming and graph processing. Hadoop 1 primarily supported batch processing.
6. Hadoop 2 provides support for new technologies such as HBase, Hive, and Pig, while Hadoop 1 primarily supported MapReduce jobs.

22. What is a Hadoop MapReduce job tracker and how does it work?


23. What is a Hadoop MapReduce task tracker and how does it work?


24. What is a Hadoop MapReduce combiner and how does it work?


25. What are the benefits and limitations of using Hadoop for big data processing?


26. What is a Hadoop ecosystem and what are the key components of this ecosystem?


That's all about the common Big Data and Hadoop Interview Questions. I know that I have not share many questions but this is just a start, I am feeling bit lazy now so I decided to publish this article and later update it otherwise it will remain in the draft state for years. You can also easily find answers of last 4 questions online but if you struggle, just ask in comments and I will add it along with more questions. 

 If you also have Big data and Hadoop questions which was asked to you during interviews feel free to share with us. 

All the best with your interview. 

Other  Java Interview Questions you may like to prepare


Thanks a lot for reading this article so far. If you like these Big Data and Hadoop Interview Questions  then please share with your friends and colleagues. If you have any questions which is not answered here, feel free to ask in comments, I will try my best to answer your doubts.    

P. S. - If you want to learn the Apache Kafka platform and Big Data in depth then you can also checkout these best Big Data Courses for Java developers to start with. It's not free but quite affordable, and you can buy it for just $10 on Udemy sales. 

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