Big data is only tremendous arrangements of client information. Information can be acquired from different sources like web based shopping, web perusing, overview structures, and occupation entrances. Customary information frameworks are not effective in managing these tremendous datasets, and to that end Hadoop has turned into a decent decision for putting away and handling Big data.
HDFS, the record arrangement of Hadoop, utilizes block design to deal with enormous documents, i.e., documents having a size more noteworthy than 64 or 128MB. The humongous records are parted into documents of lesser size (i.e., 64MB and 128MB) and put away in HDFS. Along these lines, the handling should be possible on the split information utilizing the guide lessen (mapper) work. MapReduce performs planning, handling, consolidating, arranging, and decreasing.
Organizations additionally use Mahout to perform prescient investigations. RHadoop and Hadoop with Python are the most well-known ways for handling, mining, and examining Big data.
List of Hadoop interview questions:
- What is big data? Explain the characteristics of big data.
- What are the main components of the Hadoop ecosystem?
- Explain the architecture of Hadoop.
- What is the underlying architecture of the Hadoop cluster?
- Explain the difference between single-node and multi-node Hadoop clusters.
- State some differences between a relational database and Hadoop.
- Describe the structure of HDFS and how it works.
- What is the difference between copyToLocal and copyFromLocal commands?
- Why does HDFS store data using commodity hardware?
- Since Hadoop contains massive datasets, how can you ensure that the data is secure?
- Which Hadoop version introduced YARN? How was resource management done before that?
- What are the components of YARN?
- Explain the benefits of YARN.
- What is Hbase? What are the benefits of Hbase?
- Where does Hbase dump all the data before performing the final write?
- Explain how the Hadoop system stores the data.
- Write simple Hbase commands to import the contents of a file into an Hbase table.
- How does MapReduce work?
- What are the components of a MapReduce job?
- What are counters in MapReduce? What are their types?
- What does the ResourceManager include?
- What is the purpose of a distributed cache?
- Explain how word count is done using the MapReduce algorithm.
- How can you improve the efficiency of the MapReduce algorithm?
- What is Mahout? How does it help in MapReduce algorithms?
- Is it possible to write MapReduce programs in a language other than Java?
- What is Hive Query Language (HiveQL)? Give some examples.
- How to create partitions in Hive?
- What are the differences between static and dynamic partitioning?
- How do you handle the situation where partitioning is not possible?
Hadoop is not a tough topic. However, it is vast and involves knowledge of many components put together. You should know about each of them to be able to explain the architecture to the interviewer. Most of the concepts are straightforward. The above set of Hadoop interview questions are the most commonly asked questions. Nonetheless, the list is not exhaustive.