In writing this blog, I aim to equip you with all the knowledge required for exceptional Hadoop Administration interview questions answers, which are provided by sample questions and answers. I hope this blog can help you do that.

Big data is stored and managed using Hadoop, an open-source Java platform; information is housed on inexpensive commodity cluster servers.

Hadoop handles massive data sets reliably and scalable; companies can utilise unstructured and semi-structured information for meaningful insights to be gained; the framework supports data lakes, batch processing streams and interactive query processing services as needed.

1. What are the two core components of Hadoop?

The two core components of Hadoop are the Hadoop Distributed File System and Resource Manager.

2. Explain the concept of big data. What are its characteristics?

Big data refers to large and complex data sets that are difficult to process using traditional database management tools or traditional data processing applications, its characteristics include volume, variety, velocity, value, and veracity.

3. How can organisations leverage big data for decision-making?

As information-centric companies, organisations can leverage big data by analysing it to derive insights that inform decision-making.

4. Compare and contrast the Hadoop ecosystem components Hive and Spark. How do they provide analytical capability?

Hive and Spark are both Hadoop ecosystem components that provide analytical capability but differ in their approach.

Hive allows database-based users to write SQL queries on top of Hadoop, while Spark is an in-memory data flow engine that analyses data.

5. What are two tools are used to import and export data in the Hadoop cluster?

The two tools used to import and export data in the Hadoop cluster are Flume and Scoop.

6. What are the components of the Hadoop Distributed File System (HDFS)?

The Hadoop Distributed File System (HDFS) components are the name node, data node, and secondary name node.

7.What is the role of the Resource Manager in the YARN architecture?

The Resource Manager in the YARN architecture is responsible for resource management and allocation, ensuring efficient utilisation of hardware resources.

8. Create a Hadoop cluster architecture that includes high availability mechanisms for critical components.

A Hadoop cluster architecture with high availability mechanisms for critical components could include multiple name nodes for HDFS, backup instances of the resource manager, and redundancy in application masters for fault tolerance and uninterrupted operations.

9. What is the name of the storage system in the Hadoop ecosystem?

The name of the storage system in the Hadoop ecosystem is the Hadoop Distributed File System (HDFS).

10. How does the Hadoop cluster handle resource management and job management?

The Hadoop cluster handles resource management through the resource manager and job management through the Node Manager, the resource manager is responsible for allocating resources and managing the hardware, while the Node manager oversees job management and coordinates resources with the resource manager.

11. Devise a strategy for improving the availability of the Hadoop cluster.

To improve the availability of the Hadoop cluster, you can implement high availability measures such as deploying multiple name nodes, configuring standby name nodes, and ensuring reliable hardware with redundant power supplies, additionally, you can leverage tools like Apache Ambari or Cloudera Manager to monitor and manage the cluster efficiently.

12. What are the components of the Hadoop Distributed File System (HDFS)?

The components of the Hadoop Distributed File System include the name node, the data nodes, and the secondary name node, the name node stores metadata about file locations and permissions, while the data nodes store the actual data blocks.

The secondary name node performs periodic backups of the metadata from the name node.

13. Evaluate the advantages and disadvantages of the Hadoop cluster’s standalone mode.

The advantages of the Hadoop cluster’s standalone mode include easy setup and use for development and testing purposes, the disadvantages include limited scalability, as it runs on a single node, and lack of fault tolerance, as no redundant demons are running in the background to ensure high availability.

14. What is a hardware specification for a multi-node Hadoop cluster?

A hardware specification for a multi-node Hadoop cluster could include using reliable hardware with Xenon processors, octa-core CPUs, 64-bit operating systems, and redundant power supplies.

The primary name node should have at least six disks of appropriate storage capacity, while the data nodes should be capable of efficient data processing and house the node managers.

15. What is the first step in deploying configurations on a single machine in the Hadoop environment?

The first step in deploying configurations on a single machine in the Hadoop environment is to enable password SSH by generating a public-private key pair using the SSH -t j command.

16. What is the purpose of enabling password-less SSH for the Hadoop host?

Enabling password-less SSH for the Hadoop host allows the demons to access the node without a password, facilitating automatic deployment of configurations and the cluster’s start.

17. What are the advantages of password less SSH in the Hadoop environment?

Password less SSH in the Hadoop environment provides improved security by allowing demons to access nodes without storing passwords, simplifies configuration deployment, and enables smooth cluster startup without manual authentication.

18. Assess the impact of configuring the replication factor in the HDFS site.xml file.

Configuring the replication factor in the HDFS site.xml file determines the number of copies of data stored in the Hadoop Distributed File System, this impacts data reliability and availability, as well as storage cost considerations.

19. Design a strategy to optimise the performance of a Hadoop cluster by adequately configuring the MapReduce job.

A strategy to optimise the performance of a Hadoop cluster by configuring the MapReduce job can include determining the optimal map task size and optimising data spillage with MapReduce.task.io.sort. spill. Percent, and tuning other parameters such as the block size and compression options for efficient data management and processing.

20. What is the role of the Map task in the MapReduce process?

The Map task processes data in memory and spills it onto the disk when the memory buffer is filled.

21. What is the purpose of the reducer function in the MapReduce process?

The reducer function copies and merges the map outputs sorts them, and runs the reduce function on the sorted data.

22. How can the MapReduce framework increase the performance of map tasks?

The MapReduce framework can increase the performance of map tasks by increasing the task IO factor, which controls the maximum number of streams to connect simultaneously when spilling files to merge.

23. What are the critical components of the MapReduce process, and how do they interact with each other?

The critical components of the MapReduce process are the mapper, reducer, MapReduce framework (YARN), resource manager, application master, and container.

24. How does configuring the MapReduce site contribute to successfully executing MapReduce jobs?

Configuring the MapReduce site, including setting the appropriate memory allocation for containers, yarn addresses, and log configurations, ensures that MapReduce jobs are executed efficiently and effectively.

25. Create a configuration setup to create six hosts and node arrays in the MapReduce process.

To create six hosts and six node arrays in the MapReduce process, you will need to stop the job history node, create six copies of the job history node, create six node arrays, configure the necessary nodes and update the YARN XML configuration file with the appropriate settings.

26. What is the initial step in creating a data node tree using VMware Workstation?

The initial step in creating a data node tree using VMware Workstation is to create a virtual machine clone.

27. Explain why updating the host’s IP address and hostname is essential when creating a data node tree.

It is essential to update the host’s IP address and hostname when creating a data node tree to ensure proper communication between the nodes and avoid network conflicts.

28. Describe the process of running the FORMAT command to initialise the file system in Hadoop.

Running the FORMAT command to initialise the file system in Hadoop involves executing the HDFS Name Node dash command to format the name node, if successful, a message will be displayed confirming the successful formatting of the storage directory.

29. What challenges can arise if the slave file is not configured correctly when building a Hadoop cluster?

Suppose the slave file is not configured correctly when building a Hadoop cluster, it can result in connection issues between the data nodes and the name node, leading to a failure in the distributed data processing.

30. What steps can be taken to increase the storage capacity of a Hadoop cluster?

To increase the storage capacity of a Hadoop cluster, one can commission a new node and add a new node manager, update the configuration files, add the new data node IP addresses, and run a balancer utility to move old data to the new node.

31. Explain the process of creating a new machine to add a new data node to a Hadoop cluster.

Creating a new machine to add a new data node to a Hadoop cluster involves creating a clone of the original virtual machine, updating the host’s IP address and hostname, updating relevant configuration files, and updating the DFS admin reports.

32. What is the first step in creating a new node in a cluster using HDFS and Hadoop?

The first step in creating a new node in a cluster using HDFS and Hadoop is changing the node’s IP address and port name.

33. Explain how data replication is maintained when adding a new data node to the cluster.

Data replication is maintained when a new data node is added to the cluster by running a “balancer” to move existing blocks to the latest data node and maintain replication and balance between data nodes.

34. Propose a strategy to decommission a data node from a cluster for maintenance activities while automatically replicating the data to other data nodes.

A strategy to decommission a data node from a cluster for maintenance activities while automatically replicating the data to other data nodes is to update the HDFS site dot XML and create an excluded file with the IP address of the data node.

35. What are the job responsibilities of a Hadoop administrator?

The job responsibilities of a Hadoop administrator include implementing and supporting the enterprise Hadoop environment, managing user job settings, ensuring the environment is healthy, running jobs, adding users, creating scripts to add users, monitoring clusters, providing recommendations to developers.

36. What is the importance of capacity management in the Hadoop environment?

Capacity management is essential in the Hadoop environment as it ensures that the resources are utilised efficiently, prevents resource bottlenecks, and allows for proper scaling of the Hadoop cluster to meet the demands of the applications and users.

37. Create a plan to enhance the Hadoop environment’s security.

Enhancing the Hadoop environment’s security includes implementing access controls, encryption for data at rest and in transit, monitoring and auditing mechanisms, regular security updates and patches, and user authentication and authorisation mechanisms.