Hadoop admins are counted as one of the highest-paid professionals in the industry. On top of this, the collection and usage of data have been exponentially increasing day by day. With this increase, the demand for people who can easily work with Hadoop is also on the rise. In this blog, we will walk you through some of the important interview questions asked for Hadoop professionals.
Must Read Hadoop Interview Questions & Answers
Q1. Explain some industry applications of Hadoop.
A: Apache Hadoop, popularly addressed as Hadoop, is an open-source programming stage for adaptable and disseminated analysis of huge volumes of information. It gives quick, superior, and practical investigation of organised and unorganised information produced within the organisation. It is utilised in practically all offices and domains today.
Some major industrial uses of Hadoop:
- Overseeing traffic on roads.
- Streaming preparations.
- Content administration and filing mails.
- Preparing rodent cerebrum neuronal signs utilising a Hadoop cluster.
- Fraud identification.
- Promotions focusing on stages are utilising Hadoop to catch and break down snap transfer, exchange, video, and online media information.
- Overseeing content, posts, pictures, and recordings via online media stages.
- Investigating client information continuously for improving business execution.
- Public area fields, for example, insight, guard, digital protection, and logical exploration.
- Gaining admittance to unstructured information, for example, the yield from clinical gadgets, specialist’s notes, clinical correspondence, clinical information, lab results, imaging reports, and monetary information.
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Q2. Compare Hadoop with parallel computing systems.
A: Hadoop is a distributed record framework that allows you to store and deal with monstrous volumes of information on remote machines, taking care of any unwanted repetitions of information.
The essential advantage of Hadoop is that since information is stored in a few hubs, called as nodes, it is easier to deal with it in an appropriate way. Every hub or node can deal with the information stored on it rather than investing energy in moving the information over and over again.
Surprisingly, in the RDBMS processing framework, we can make queries about information continuously. However, it isn’t productive to store information in tables, records, and sections, especially when the data is in large volumes.
Read: How to become a Hadoop administrator?
Q3 Name different modes in which Hadoop can be run.
A: Standalone mode: The default method of Hadoop it makes use of a local storage framework for taking in the input and giving out the output. This mode is essentially utilised because of easy debugging options, and it doesn’t support HDFS.
There is no custom setup needed for mapred-site.xml, centre site.xml, and hdfs-site.xml records. This mode works a lot quicker than other modes.
- Pseudo-distributed mode (Single-node Cluster): In this mode, for all the 3 records we talked about earlier, we need a separate setup. For this mode, all daemons are running on one node, and along these lines, both Master and Slave hubs essentially become the same.
- Fully distributed mode (Multi-hub Cluster): This mode is defined as the creation period of Hadoop where information is utilised and dispersed over a few nodes on a Hadoop cluster. Separate hubs are apportioned as Master and Slave.
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Q4: Explain the major difference between InputSplit and HDFS block.
A: A block can be defined as a physical representation of information and data while the split is the logical representation of whatever data is present in the block. Split goes about as a bridge between the block and the mapper.
Assume we have 2 blocks:
- ii nntteell
- i ppaatt
If we go by the principles of the map, it will read Block 1 from ii to ll but would not figure out how to read Block 2 in that situation. To solve this, we will need a logical bundle of Block 1 and Block 2 that can be easily read as a single block. This is where Split comes into play.
Furthermore, split forms a key-value pair by utilising the InputFormat and makes multiple records of the reader and processes this further to the map for subsequent processing by InputSplit. It also gives us the flexibility of storage, enabling us to increase the split size to decrease the total number of maps being formed.
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Q5: Name some common input formats used in Hadoop.
A: There are primarily 3 input formats in Hadoop:
- Text Input Format: This is used as a default in Hadoop.
- Key-Value Input Format: Majorly preferred when the text files are broken into several lines.
- Sequence File Input Format: It is majorly used for reading files in sequence.
Also Read: Hadoop Project Ideas & Topics
Q6: List out the major components of any Hadoop Application.
A: The major components of the Hadoop are-
- HBase for storing data
- Apache Flume, Sqoop, Chukwa – used as the Data Integration Component
- Ambari, Oozie and ZooKeeper – component used for Data Management and Monitoring
- Thrift and Avro – Data Serialization components
- Apache Mahout and Drill – for Data Intelligence purposes
- Hadoop Common
- HDFS
- Hadoop MapReduce
- YARN
- PIG and HIVE
Q7: What is “Rack Awareness”?
A: The NameNode in Hadoop uses Rack Awareness system to decide how the blocks and their copies are in the Hadoop group. The traffic between DataNodes inside a similar rack is limited by rack definitions. In this system, the first two replicas of a block will be stored in one rack, and the third replica will be stored in a different block.
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Conclusion
Hope you liked our blog on Hadoop admin interview questions. However, it is really important to have an exhaustive set of Hadoop skills and knowledge before you appear for the interview. You can refer to some of the important Hadoop tutorials on our blog here,
Hadoop Tutorial: Ultimate Guide to Learn Big Data Hadoop 2024
What is Hadoop? Introduction to Hadoop, Features & Use Cases
If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.
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Why is Hadoop so important to businesses?
Hadoop is a platform offered by Apache that uses basic programming concepts to spread the processing of massive data volumes across clusters of machines. Common, Hadoop Distributed File System, YARN, and MapReduce are Hadoop's four components. Companies utilize Hadoop for a variety of reasons. Hadoop allows enterprises to process and extract value from petabytes of data stored in the HDFS. It offers versatility by allowing simple access to a variety of data sources and data kinds. Hadoop also allows enormous volumes of data to be handled quickly due to parallel processing and minimum data transportation. Finally, Hadoop is noted for its versatility since it supports a wide range of programming languages such as Python, Java, and C++.
What are the skills required to learn Hadoop?
Everyone can master Hadoop if they're dedicated and believe it will help them advance their business or career. While there are no specific requirements for learning Hadoop, having a basic understanding of coding and SQL can help you comprehend it more quickly. Hadoop needs the expertise of multiple programming languages, depending on the role you want it to play. Knowledge of SQL or SQL-like querying languages is required for Big Data systems that use the Hadoop environment. Because most Hadoop implementations across sectors are built on Linux, having a basic working understanding of Linux is advantageous.
What is the scope of Hadoop?
Hadoop is one of the most important big data technologies, with a bright future ahead of it. Most of the world's largest enterprises use Hadoop technology to deal with their huge data for research and production because it is cost-effective, scalable, and dependable. It entails storing data on a cluster without a single computer or piece of hardware failing, as well as adding new hardware to the nodes. Compared to other big data technologies, this generation of vast data uses the Hadoop technology, which is widely used. However, alternative technologies compete with Hadoop since it has yet to achieve traction in the big data industry. It is still in the early stages of adoption and will take some time to stabilize and seize the lead in the big data industry.
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