Big Data brings in big opportunities as it throws up various roles like Data Analyst, Data Engineer, Data Scientist and many more. In this article, we shall go through the top Big Data interview questions that you should be prepared to help you grab these opportunities quickly. Big Data is a vast subject, and it is possible that you might not know the answers to some questions that the interviewer asks you.
Many interviewees try to get around this situation by guessing the answers, which is not the impression that you want to give the interviewer. If you aren’t aware of the topic, be honest and inform the interviewer that you are not completely aware of that topic and will try to gain more knowledge on that subject as quickly as you can.
Here are the top Big Data interview questions you should be prepared with before attending a big data interview.
This is one of the important big data interview questions. The term Big Data refers to the entire ecosystem of data that runs through the business day in and day out, along with the tools to capture and harness value from this data. Due to the nature of the data that runs through a business, you need specialized tools to capture, store, analyse and interpret data. These tools are specially designed to handle the volume, variety and velocity of the data that exists in any business.
This is another one of the most asked Big data interview questions.
5v’s of Big Data are:
Volume- Since Big Data aims to capture almost all data that runs through the business, the volume of data is enormous, and this volume of data is ever increasing.
Variety- Variety refers to the characteristic of Big Data that mandates to capture of any type of data, be it structured, unstructured or semi-structured. Unstructured data like images, videos, audio files, and semi-structured data like log files are captured.
Velocity-Velocity is the pace at which new data is running through the business. In other words, the rate at which new data needs to be captured. Different types of data pass through the business at different velocities. Big Data system should be able to adapt to those speeds and capture this data accordingly.
Veracity- Veracity refers to the trustworthiness of the data. Even at speeds and volumes that Big Data allows data to be captured, the system should check for the trustworthiness of the data being captured. The processes used to capture the data should be designed well enough to maintain the integrity of the data captured.
Value- Value is the most important v of a Big Data setup. Without value, there is no point in the whole exercise. A business should be able to see value in capturing, analysing, and interpreting all this business data at the end of the day.
Big Data refers to the concept of data and tools that surround the business. At the same time, Hadoop is a framework that helps you set up the infrastructure to capture, analyse and interpret big data.
With constant analysis and monitoring of data and tools that help identify relationships in the data, it becomes easier for the business to understand changing business scenarios, react quickly to events that impact the business, generate new streams of revenue depending on consumer patterns, implement effective processes that cut wastage and improve efficiency.
Firstly, you should be able to see the entire big data setup as a coming together of 3 important phases, namely data ingestion, data storage, and data processing.
Data ingestion deals with connecting to various sources of data that runs through the business and capture all varieties of data at any speed that it is coming in. Thus, data can be ingested through batch jobs or real-time streaming.
Data storage refers to the efficient storage of this variety of data in huge volumes and data coming in at great speeds into the system. Typically, a distributed storage system like HDFS is a great solution to the storage of such enormous data.
Data Processing refers to the analysis of data for value generation from the data that is captured.
This is a high-level view of the Big Data implementation, and this needs to be broken down at every stage.
There are two main components of HDFS,
NameNode- The NameNode is a machine that stores metadata about the data stored across the cluster. NameNode knows where each block of data is stored in the HDFS cluster.
DataNode-DataNode is machines that store the data blocks assigned by NameNodes. DataNodes are based on commodity hardware.
ResourceManager- ResourceManager receives requests for processing and allocates the requests to respective NodeMaangers.
NodeManager- NodeManager is the process that executes requests on a DataNode.
FSCK refers to File System Check. It is an HDFS command used to check file system inconsistencies like missing blocks on a file.
NAS runs on individual machines, while HDFS runs a cluster of commodity hardware-based machines. Data thus stored on HDFS is split across multiple nodes or machines across the cluster. Data in the case of NAS resides on a single machine.
The command for formatting a NameNode is
These are some of the top big data interview questions. Big Data is a vast subject, needs guidance and hand-holding through the learning phase. It is best advised to join a course that takes you through Big Data in the right direction and to a suitable place. There are many courses that cover concepts of BigData thoroughly at Jigsaw academy that you can refer to and clear your concepts of Big Data. At the same time, you also get some hands-on experience with Big Data.
Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.