Uploaded on
24 May 2023
Skill-Lync
Hadoop is an open-source framework that allows storing and processing of large amounts of data in a distributed computing environment. Organizations widely use it to handle big data and gain insights from it. Hadoop has become an essential tool for data analysts and data scientists.
As a Hadoop Developer or Analyst, you can easily earn around INR 3 lakhs to INR 11 Lakhs annually, with an average salary of INR 5.5 lakhs.
If you are a fresher looking for a job in the field of big data, you may be asked several Hadoop interview questions during the selection process.
In this blog, we will discuss the top 10 Hadoop interview questions with their answers to help you prepare for your interview.
Hadoop is an open-source software framework that allows distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale from single servers to thousands of machines, each offering local computation and storage. Hadoop provides a reliable, scalable, and fault-tolerant platform for handling big data.
The key components of Hadoop are:
NameNode is a master node in Hadoop's HDFS architecture that manages the file system metadata and coordinates client data access requests. It maintains the directory tree of all files in the file system, and it tracks the location of each block of data. NameNode also decides which DataNode should receive data from a client, and it monitors the health of DataNodes.
DataNode is a worker node in Hadoop's HDFS architecture that stores data in a distributed manner. Each DataNode stores a portion of the data stored in HDFS. DataNodes communicate with the NameNode to report their health status and receive instructions for data replication and rebalancing.
Hadoop MapReduce supports three types of joins:
HBase is a NoSQL database that runs on top of Hadoop. It provides random and real-time access to large datasets. HBase is used for storing and retrieving large amounts of structured data. It is scalable, fault-tolerant, and provides automatic sharding of tables. HBase is ideal for applications that require low-latency data access.
Hadoop provides data security through various mechanisms, such as:
A Secondary NameNode in Hadoop is a helper node that performs periodic checkpoints of the Hadoop file system metadata. It merges the edits log with the file system image to create a new snapshot of the file system metadata. This helps to prevent data loss and improve the performance of the NameNode.
Hadoop supports various compression techniques to reduce the size of data stored in HDFS. Some of the popular compression techniques are:
Data skew is a common problem in Hadoop, where a few keys or partitions contain a disproportionate amount of data, leading to uneven workload distribution among nodes in the cluster. Hadoop offers several techniques to handle data skew, such as:
A Hadoop online workshop from Skill-Lync can help students prepare well for interviews by comprehensively understanding Hadoop concepts and practical experience with Hadoop tools and technologies. The workshop covers all the essential topics, including HDFS, MapReduce, Hive, and Pig, along with hands-on projects that enable students to apply their knowledge to real-world scenarios.
Moreover, it includes mock interviews, practice exercises, and career guidance, which help students prepare for Hadoop interviews and stand out from other candidates. By enrolling in a computer science engineering online course from Skill-Lync, students can gain the skills and confidence they need to ace Hadoop interviews and secure their dream jobs. You can also take a demo class with us to learn about it better.
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Anup KumarH S
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Skill-Lync
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