Apache Hadoop is one of the most popular and useful technology in Data Science and Data engineering world. Big companies like Amazon, Netflix, Google etc use Apache Hadoop. This course is designed to help you achieve your goals in Data Science field. Data Engineer and Software Engineers with Apache Hadoop knowledge may get more salary than others with similar qualifications without Apache Hadoop knowledge.

In this course, you will learn how to handle interview questions on Apache Hadoop in Software Development. I will explain you the important concepts of Apache Hadoop.

You will also learn the benefits and use cases of Apache Hadoop in this course. 

What is the biggest benefit of this course to me?

Finally, the biggest benefit of this course is that you will be able to demand higher salary in your next job interview.

It is good to learn Apache Hadoop for theoretical benefits. But if you do not know how to handle interview questions on Apache Hadoop, you can not convert your Apache Hadoop knowledge into higher salary.

What are the topics covered in this course?

We cover a wide range of topics in this course. We have questions on Apache Hadoop, Hadoop architecture, Hadoop deep concepts, Hadoop tricky questions etc.

How will this course help me?

By attending this course, you do not have to spend time searching the Internet for Apache Hadoop interview questions. We have already compiled the list of most popular and latest Apache Hadoop Interview questions. 

Are there answers in this course?

Yes, in this course each question is followed by an answer. So you can save time in interview preparation.

What is the best way of viewing this course?

You have to just watch the course from beginning to end. Once you go through all the videos, try to answer the questions in your own words. Also mark the questions that you could not answer by yourself. Then, in second pass go through only the difficult questions. After going through this course 2-3 times, you will be well prepared to face a technical interview in Apache Hadoop field.

What is the level of questions in this course?

This course contains questions that are good for a Fresher to an Architect level. The difficulty level of question varies in the course from a Fresher to an Experienced professional.

What happens if Apache Hadoop concepts change in future?

From time to time, we keep adding more questions to this course. Our aim is to keep you always updated with the latest interview questions on Apache Hadoop.

What are the sample questions covered in this course?

Sample questions covered in this course are as follows:

1. What are the four Vs of Big Data?
2. What is the difference between Structured and Unstructured Big Data?
3. What are the main components of a Hadoop Application?
4. What is the core concept behind Apache Hadoop framework?
5. What is Hadoop Streaming?
6. What is the difference between NameNode, Backup Node and Checkpoint NameNode in HDFS?
7. What is the optimum hardware configuration to run Apache Hadoop?
8. What do you know about Block and Block scanner in HDFS?
9. What are the default port numbers on which Name Node, Job Tracker and Task Tracker run in Hadoop?
10. How will you disable a Block Scanner on HDFS DataNode?
11. How will you get the distance between two nodes in Apache Hadoop?
12. Why do we use commodity hardware in Hadoop?
13. How does inter cluster data copying works in Hadoop?
14. How can we update a file at an arbitrary location in HDFS?
15. What is Replication factor in HDFS, and how can we set it?
16. What is the difference between NAS and DAS in Hadoop cluster?
17. What are the two messages that NameNode receives from DataNode in Hadoop?
18. How does indexing work in Hadoop?
19. What data is stored in a HDFS NameNode?
20. What would happen if NameNode crashes in a HDFS cluster?
21. What are the main functions of Secondary NameNode?
22. What happens if HDFS file is set with replication factor of 1 and DataNode crashes?
23. What is the meaning of Rack Awareness in Hadoop?
24. If we set Replication factor 3 for a file, does it mean any computation will also take place 3 times?
25. How will you check if a file exists in HDFS?
26. Why do we use fsck command in HDFS?
27. What will happen when NameNode is down and a user submits a new job?
28. What are the core methods of a Reducer in Hadoop?
29. What are the primary phases of a Reducer in Hadoop?
30. What is the use of Context object in Hadoop?
31. How does partitioning work in Hadoop?
32. What is a Combiner in Hadoop?
33. What is the default replication factor in HDFS?
34. How much storage is allocated by HDFS for storing a file of 25 MB size?
35. Why does HDFS store data in Block structure?
36. How will you create a custom Partitioner in a Hadoop job?
37. What are the differences between RDBMS and HBase data model?
38. What is a Checkpoint node in HDFS?
39. What is a Backup Node in HDFS?
40. What is the meaning of term Data Locality in Hadoop?
41. What is the difference between Data science, Big Data and Hadoop?
42. What is a Balancer in HDFS?
43. What are the important points a NameNode considers before selecting the DataNode for placing a data block?
44. What is Safemode in HDFS?
45. How will you replace HDFS data volume before shutting down a DataNode?
46. What are the important configuration files in Hadoop?
47. How will you monitor memory used in a Hadoop cluster?
48. Why do we need Serialization in Hadoop map reduce methods?
49. What is the use of Distributed Cache in Hadoop?
50. How will you synchronize the changes made to a file in Distributed Cache in Hadoop?