Hadoop serves as an open-source programming model for storing and processing data on distributed hardware sets. It is one of the leading distributed file management solutions. Hadoop offers the ability to utilize this massive storage to access data flexibly across a network of computers (also known as clusters). Thus, it can accommodate almost infinite simultaneous computing processes or operations using this collective data. A big data Hadoop certification is highly recommended as this allows you to facilitate data storage and segregation for companies without the need for pre-processing. Many enterprises have started adopting Hadoop due to not being expensive to sustain, the ability to process massive amounts of data, and the enormous storage capabilities the framework offers.
Who is a Hadoop Administrator?
There are many challenges a company faces when handling large volumes of data. Hadoop administrators are Hadoop architecture experts who can facilitate the smooth installation of Hadoop clusters for enterprises from various industries. Implementing Hadoop into the IT architecture would not be effective without a Hadoop administrator. Notably, Hadoop administrators are largely in charge of keeping Hadoop stacks up and functioning in production. They are given the training of clustering the Hadoop data into groups and managing the assets in the Hadoop environment. They must also have a strong understanding of data governance and should be comfortable working with big data.
Hadoop administrators should think of the benefits of users (external or from the organization) and must sort out the different challenges associated with data. They must also work on improving the performance of the overall Hadoop cluster in the long run. This requires frequent and daily monitoring of the existing Hadoop clusters. Besides, Hadoop administrators are absolutely necessary for creating and sustaining the data infrastructure of the company.
Hadoop Administrator Interview Questions
Before diving into the questions that can be asked in interviews, you should also consider practicing the necessary skills associated with this specialized job role. A candidate appearing for Hadoop administrator interviews must be an expert in data management. The interviewer will also be evaluating how fluent you are in networking. You must have a strong foundation in using Linux OS and Unix-based file systems. The candidate must be able to manage open-source configuration and deployment tools. And, the questions that interviewers ask are based on these requirements.
Further, Hadoop administrator job interviews always involve data and cluster management questions that ask for brief but technical answers. Candidates might also be asked questions about simple CRUD operations or basic networking. The interviewer needs to be confident in the candidate’s ability to fulfill the sensitive job role of handling an enterprise’s data. Configuration of NameNodes and taking backups are also important topics that are discussed in these interviews. Here are some important recurring questions:
Q1. What daemons will you use while running a Hadoop cluster?
NameNode, Secondary NameNode, DataNode, TaskTracker, and JobTracker are daemons to be used while running a Hadoop cluster.
Q2. What are the popular input formats used in Hadoop?
TextInputFormat, FileInputFormat, SequenceFileInputFormat, and KeyValueTextInputFormat are popular input formats in Hadoop. Other than these, CombineTextInputFormat, CombineFileInputFormat, CompositeInputFormat, and CombineSequenceFileInputFormat are also popular.
Q3. What do you mean by big data?
Fundamentally, big data as a field is associated with handling massive volumes of data. However, big data is generally huge volumes of unstructured and structured data that need to be dealt with by processing them as datasets. Unlike smaller amounts of data, big data cannot be tackled with traditional software and techniques. More advanced data processing and big data methodologies must be applied to work with big data.
Q4. How will you differentiate Hadoop from RDBMS?
The basic difference between the two is that Hadoop is a distributed file system that can utilize multiple computers on its network and RDBMS are relational databases. However, the main difference between Hadoop and RDBMS is that the former is more scalable than the latter. You need to normalize data in RDBMS but not in Hadoop.
Q5. Can you tell me the most important and useful features of Hadoop?
Hadoop is an open-source framework that can be adapted to any company’s data infrastructure. It is also a simple framework and very easy to maintain. Hadoop's low latency and data integrity are its most well-known features. Clusters of Hadoop are also highly scalable. Parallel computing ensures quick data analysis and fast operations. Hadoop’s locality feature lessens the bandwidth usage of the system.
Q6. What is a checkpoint in Hadoop?
Clipping followed by compressing a fsimage and forming an absolute new fsimage is known as checkpointing. This allows NameNode to retrieve the ultimate in-memory state straight from the fsimage instead of looping a practically limitless edit log. Thus, checkpoints minimize the time needed for NameNode to begin its work.
Q7. What is a NameNode and how will you use it in the Hadoop cluster?
An HDFS file system is built around NameNode. It maintains the directory structures of all the files reflecting in the operating system and records where the file data is stored across the cluster. It does not save the contents of these files.
Q8. What will you do when NameNode fails to work?
When NameNode fails to work, it is the job of a Hadoop administrator to restart it manually.
Q9. List the modes in which you can run a code written in Hadoop.
Modes in which a code written in Hadoop can run include the standalone mode, pseudo distributed mode, and fully distributed mode.
Q10. How will you copy files from one cluster to another one?
Using the Hadoop distcp function, files or directories can be copied from one cluster to another. For the source cluster to verify that you are registered in both the source and the target clusters, a credentials file has to be attached to your copy request.
Q11. What are the components used in Hadoop administration?
● HDFS: Hadoop distributed file system
● MapReduce: Hadoop runs on the MapReduce programming model based on data processing
● Spark: For in-memory data processing
● Solar, Lucene: To facilitate searching and indexing
● PIG and HIVE: These are query-based processing of data services
● YARN: This stands for yet another resource negotiator
● HBase: A NoSQL Database.
● Spark MLLib and Apache Mahout: Machine learning algorithm libraries
● Oozie: For job scheduling
● Zookeeper: To manage clusters
If you have a strong foundation with big data Hadoop certification and firm base knowledge of Hadoop architecture, then the questions asked will not seem complex or tough to you. Some of these questions will surely help you out in the eleventh hour. Prepare yourself well and do not get panicked or tense before the interview. Be confident in what you know about networking, distributed file systems, and data management. Best of luck!
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