There are thousands of jobs for Big Data Developers and Engineers in India. These positions pay anywhere between 5 and 13 lakhs depending on the experience and background of the individual. If you’re looking for a job in this exciting and fast-growing field, here are the top 10 big data interview questions (with answers) to help you land the job that you desire.
Big Data Interview Questions & Answers
What Is Big Data?
Arguably, the most basic question you can get at a big data interview. If you fail to answer this, you most definitely can say goodbye to the job opportunity.
Big data refers to all data which don’t necessarily relate to each other as they are but can be useful for acquiring business intelligence. Big data has five features – volume, variety, velocity, veracity and value. These make up the five Vs of big data.
Big data is voluminous. According to estimates, by 2025, the volume of big data will be around 1600 trillion gigabytes. This includes data in a variety of formats – images, audio, video, text, etc. By 2020, every human will create around 1.5 MB of per second. Big data is free-flowing at a high velocity. It is already proving to be useful for a variety of purposes due to its veracity or accuracy. And finally, big data has been proclaimed as the new oil. Safe to say, big data has high value in the modern digital, information rich economy.
What Is Hadoop?
Hadoop is an open-source big data framework. It is useful in data management. The big data technology allows you to store, process and analyze complex data sets effectively. It helps gain insights from data and use it for business purposes. Hadoop has three parts to it. First, MapReduce is great for writing programs or applications that can manage multiple data sets without trouble. Second, HDFS is distributed data storage system. And third, YARN allows you to manage requests involving various applications and resources.
What are Hadoop’s features?
First and foremorst, Hadoop is open-source. The codes for Hadoop are easily available and modifiable for different tasks due to this reason.
Second, Hadoop is scalable. It can be extended above and beyond the commodity hardware (explained later in this blog).
Third, Hadoop easily recovers data by dividing them in different clusters. In case of failures, Hadoop recovers tasks automatically.
Fourth, Hadoop is easy to use due to its simple interface. Even freshers can easily manage distributed computing processes using this framework.
Lastly, Hadoop computes data close to where it is stored instead of moving data for computation. This is an especially useful feature when computing large data sets.
What is difference between HDFS & YARN?
Hadoop Distributed File System or HDFS is useful in storing data for distributed computing. It contains two types of nodes – NameNode and DataNode. The NameNode is the main node, which contains information on all data in the whole system. The DataNode stores the data for processing.
YARN, on the other hand, is useful for processing data. It manages resources and executes big data processes. YARN comprises of a ResourceManager and a NodeManager. The ResourceManager handles requests and assigns processes to nodes. The NodeManager executes the processes on the correct DataNode.
What is an EdgeNode?
An EdgeNode is an interface that allows Hadoop to communicate with an outside network. Through it, client applications and Hadoop’s administration tools transfer data to the Hadoop cluster. EdgeNodes require enterprise-class storage facilities as they are highly efficient in managing multiple Hadoop clusters. Data management tools like Oozie, Pig and Flume work with EdgeNodes in Hadoop.
What is Commodity Hardware?
A system that meets the minimum requirements to effectively run Apache Hadoop and other related programs is called ‘commodity hardware’.
What is FSCK?
FSCK is the command to check the health of the Hadoop system. It stands for File System Check. On execution, File System Check provides a summary report which lists the errors but does not rectify them, mind you. You can use the FSCK to detect flaws in the whole system or just a few files in it.
What is JPS?
JPS is a command to inspect whether Hadoop daemons and processes like DataNode, ResourceManager, etc are running effectively.
What is MapReduce? How does it work?
MapReduce is a parallel-programming model in the Hadoop file system. It is best for applications that must compute large data sets across computers. MapReduce executes operations in two phases. In the Map phase, input is split into map tasks that work in tandem. In the Reduce phase, split data is brought back to provide overall output.
How are big data solutions developed?
Big data solutions are devised using a three-step process. First, data is ingested. Which means, it is gathered from a variety of sources like a CRM or log files like a social media feed. Data can be gathered, either in batches or in real-time by live streaming.
After ingestion, data is naturally stored. Data can be stored either in HDFS or HBase, which is the NoSQL database. HDFS is best if you want to grant sequential access to data. For non-sequential or random access, HBase is better.
The final stage in the development of a big data solution is data processing. Spark, MapReduce, Pig are programs that are handy in this phase.
Wish You Success!
These are our top 10 big data interview questions. We hope this blog helped you prepare for your interview. If you’d like more information on big data, data analytics and other related fields, make sure you subscribe to our blog. As always, we wish you all the best and great success in your career. Happy learning!
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