Big Data is everywhere now and it is related to AI and IoT.
Businesses are embracing Big Data as it is important in almost every part right from predicting the business to streamlining it.
So most of the people learn and be the part of it. Everyone would do the courses, but cracking the interview is the great challenge.
Here are the few interview questions which would help the fresher in cracking the interview
What do you know exactly by Big Data?
Big Data is defined as a collection of large and complex unstructured and structured data sets from where insights are derived from Data Analysis using open-source tools like Hadoop
What is the V’s of Big Data?
Based on the five V’s the data is segregated
Volume – Amount of data in bytes
Variety – Includes formats like videos, audio sources, textual data or any other source of data.
Velocity – Growth of Data everywhere including conversations in forums, blogs, social media posts, etc.
Veracity – Degree of the efficient data available
Value – Originating insights from collected data to achieve business milestones and new heights
How Big Data is useful for Business?
Big data helps business in finding the fraud and reducing the fraud detection, Improved customer service, better operational efficiency, recalculating entire risk portfolios in minutes, prior identification of risk to the product/services, detecting illegal performance before it affects your business,business can utilize the external intelligence while decision making and generating coupons at the point of sale based on the customer’s buying practices
How Hadoop and Big Data are interrelated?
Big data is the collection of large complex data sets and analyzing it. This is what Hadoop does.
Apache Hadoop is an open-source framework used for storing, processing, and interpreting complex unstructured data sets for obtaining insights and predictable analysis for businesses.
The prior main components of Hadoop are-
- MapReduce – A programming model which processes massive datasets in parallel
- HDFS– A Java-based distributed file system used for data storage
- YARN – A framework that handles resources and requests from assigned applications
What is a distributed cache and what are its benefits?
Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files when needed.
Benefits of using distributed cache are:
- It assigns simple, read-only text/data files and/or complex types like jars, archives and others. Those archives are then un-archived at the slave node.
- Distributed cache tracks the alteration timestamps of cache files, which indicates that the files should not be modified until a job is executing currently.
How can you debug Hadoop Code?
Debugging the Haddop code requires MapReduce jobs.
Firstly Run: “ps –ef | grep –I ResourceManager” and check for the log directory, find job-id from the list and check for any error message.
Based on the RM logs, identify the worker node that was involved in the execution of the task.
Log in to that node and run – “ps –ef | grep –iNodeManager”
Examine the Node Manager log. The majority of errors come from user level logs for each map-reduce job.
What is the difference between Big Data and Data Science?
Big data is the collection of raw and huge data whereas Data Analytics is précised and segregated form of Data which is ready to use.
To tell in a précised way Big Data is like a library if books whereas Data Science is a particular section of books wherein user know what to read.
How many companies use Hadoop?
Most of the top companies as Yahoo search engine uses Hadoop, Facebook – Developed Hive for analysis, Amazon, Netflix, Adobe, eBay, Spotify, Twitter, Adobe.
How do you define unstructured and Structured Data?
structured data is involved of precisely defined data types where the data can be easily searchable; while unstructured data is the data that is usually not as easily searchable like audio, video, and social media posting.
Explain some important features of Hadoop?
Hadoop is an open source, reliable, scalable and fault tolerant and user-friendly.
Explain three running modes of Hadoop?
Hadoop runs in three modes
This is the default mode of Hadoop for both input and output operations. This mode is mainly used for debugging and doesn’t support HDFS use.
Pseudo-Distributed Mode (Single-Node Cluster)
In this mode, a user can configure all the three files. In this case, both the Master and Slave node is the same as all daemons are running on one node.
Fully Distributed Mode (Multiple Cluster Node)
This mode is the production phase of Hadoop where data is used and distributed across several nodes on a Hadoop cluster.
Explain the core methods of a Reducer?
There are three core methods of a Reducer. They are
Setup() – Configure parameters like distributed cache, heap size and input data.
Reduce ()- Reduce task as per the concerned task.
Cleanup()- Clear all the temporary files
13. What are the core components of Hadoop?
The core components of Hadoop are HDFS, Hadoop Map Reduce, YARN
HDFS is the basic storage of Hadoop.
MapReduce is the Hadoop layer that is responsible for the processing of the unstructured and structured page.
YARN is the processing framework of Hadoop.
What’s the way to transfer data from Hive to HDFS?
There is a query to transfer data from Hive to HDFS
hive> insert overwrite directory ‘/’ select * from emp;
This query can be written for the data which needs to be imported from Hive to HDFS. One can find the result stored in part files of the particular HDFS path.
What are the real-time applications of Hadoop?
As Hadoop is an open source software platform for computing complex volume of data.
Some of the real-time applications of Hadoop are
- Fraud detection and Prevention
- Managing public sector fields as Cybersecurity, banking, intelligence, defence and scientific research.
- Managing the Content on social media channels.
- Content management, streaming processing and getting access for the unstructured data like medical data, educational data and clinical data etc.,
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