Big Data Analytics empower organizations and they are always looking out for skilled professionals to extract meaningful insights from their data efficiently. There is a sudden and stable surge for big data professionals all around the world and the companies are paying hefty packages for certified and skilled individuals. To help such professionals, we have given here some of the popular and frequently asked Big Data Interview Questions and Answers for acing the technical rounds easily.
- Define Big Data
Big data is the collection of complicated unstructured or semi-structured datasets that can deliver actionable insights. There are five Vs of Big Data to define it’s useful such as Volume which talks about the amount of data, Variety that talks about the various formats of data, Velocity which talks about the ever-increasing speed at which the data is growing, Veracity which talks about the degree of accuracy of available data, and Value that talks about how businesses can generate revenue from data.
- How does Big Data help in increasing business revenue?
Big Data Analytics is becoming very essential for businesses as it helps to give the knowledge to differentiate from others to increase the revenue. The predictive analysis provides customized suggestions and recommendations to businesses to improve their sales. Big data analytics enables businesses to launch their new products depending on the customer’s needs and preferences. Big data analytics make more revenue for companies and they can get a significant increase of 5 to 20% in revenue than traditional data analytics models. Some of the popular companies that improve their revenue are LinkedIn, Facebook, Walmart, Bank of America, and Twitter.
- What are the steps to be followed to deploy big data analytics in a company?
There are three major steps to be followed when deploying big data analytics in a company as Data Ingestion, Data Storage, and Data Processing.
Data Ingestion is the first step to deploy big data solutions and this step involves extracting data from various sources like Salesforce, ERP (Enterprise Resource Planning) systems like SAP, RDBMS like MySQL, Log Files, Documents, Social Media Feeds, etc. These data are to be ingested through batch jobs or real-time streaming and the extracted data have to be stored in HDFS.
Data Storage is the next step of deploying big data that involves storing the extracted data in HDFS or NoSQL databases like HBase. The HDFS storage is working well for sequential data while HBase is used for storing random read or write access.
Data Processing is the final step of deploying a big data solution and this step involves the processing of data through one of the frameworks like Spark, MapReduce, Pig, etc.
- What are the components of HDFS?
There are two main components in HDFS (Hadoop Distributed File System) such as NameNode and DataNode.
- NameNode is the master node for processing metadata information for data blocks within the HDFS.
- DataNode is also known as Slave Node that store data for processing and use by the NameNode.
NameNode executes two of the following roles to serve the client requests
- CheckpointNode that runs on a different host from the NameNode
- BackupNode is a read-only NameNode that consists of file system metadata information excluding the block locations.
- What are the components of YARN?
There are two main components in YARN (Yet Another Resource Negotiator) as Follows
- ResourceManger that receives processing requests and allocates to respective NodeManager according to the processing needs.
- NodeManager that executes tasks on each single Data Node.
- How is Hadoop related to Big Data Analytics?
Hadoop is an open-source framework for storing, processing, and analyzing complicated unstructured data sets for extracting insights and intelligence. Hadoop runs on commodity hardware and it is a cost-effective solution for businesses.
- What is the difference between the regular file system and Hadoop Distributed File System?
Regular File System is maintaining data in a single system. If the machine that has data crashes, the data recovery becomes challenging which leads to fault tolerance and seek time will be more and takes more time to process the data.
In HDFS, Data will be distributed and maintained on multiple systems. If any DataNode crashes, data can be still be recovered from other nodes in the cluster. Time taken for reading data is comparatively more as there is no local data read to the disc and it requires coordination of data from multiple systems.
- How can you say HDFS is fault-tolerant?
HDFS is fault-tolerant as it replicates data on various DataNodes. By default, a block of data is replicated on three DataNodes and the data blocks are stored in different DataNodes. If one node crashes, the data can be retrieved from other DataNodes.
- Define FSCK.
FSCK stands for File System Check and it is used by HDFS. This is the command used to check inconsistencies and problems in the file. For instance, if there are any missing blocks for a file, HDFS receives notification through the fsck command.
- What is the difference between NAS (Network-attached Storage) and HDFS?
HDFS runs on a cluster of machines and NAS runs on individual machines. The data redundancy is the general issue in HDFS but the replication protocol is different in NAS. Hence, the chances of data redundancy are much less in NAS. Data is stored as data blocks in local drives in HDFS while they are stored in dedicated hardware in NAS.
- What is the use of the JPS command in HDFS?
The JPS command is used for testing the process of all the Hadoop Daemons. It tests daemons like NameNode, DataNode, NodeManager, ResourceManager, and so on.
- List the various commands for starting up shutting down Hadoop Daemons
To start all the daemons:
To shut down all the daemons
- Why do we require Hadoop for Big Data Analytics?
Hadoop helps in exploring and analyzing large and unstructured datasets. It offers storage, processing, and data analyzing capabilities that help in data analytics. There are various features in Hadoop such as follows
- Open-source platform that allows code to be rewritten or edited as per the use and analytics needs.
- Scalability that supports the addition of hardware resources to the new nodes.
- Data Recovery follows replication that allows recovery of data in case of failures.
- Data Locality moves the computation to the data and fastens the whole process.
- What are the Port Numbers for NameNode, Task Tracker, and Job Tracker?
Port Number 50070 for NameNode, Port Number 50060 for Task Tracker, and Port Number 50030 for Job Tracker.
- Define Indexing in HDFS
HDFS indexes data blocks according to their sizes and the end of data blocks points to the address of where the next data block or data chunk gets stored. The DataNodes are stored in the blocks of data while NameNode is stored in the data blocks.
- Define EdgeNode in Hadoop
Edge Nodes are the gateway of nodes that act as an interface between the Hadoop cluster and external network. These nodes run client applications and cluster management tools are used as staging areas as well. Enterprise-class storage capacities are needed for Edge Nodes and a single node suffices for multiple Hadoop Clusters.
- Name some of the data management tools used in Edge Nodes in HDFS
The most common data management tools used in Hadoop with Edge Nodes are Oozie, Pig, Flume, and Ambari.
- Define the core methods of Reducer
There are three major core methods of reducer in HDFS as follows
- setup() is the method used for configuring various parameters like heap size, distributed cache, and input data.
- reduce() is the method used to called once per key with the concerned reduce task
- cleanup() is the method to clear the temporary files and is called only at the end of the reducer task.
- Which is recommended hardware configuration for Hadoop jobs?
Dual Processors or Core machines with a configuration of 4 or 8 GB RAM and ECC memory is useful for running Hadoop jobs and operations. The hardware configurations vary depending on the project-specification workflows and process flows and they require customization accordingly.
- Explain Rack Awareness in Hadoop
Rack Awareness is an algorithm implemented to the NameNode to decide how blocks and their replies are placed. Depending on the network traffic, rack definitions will be minimized between DataNodes within the same rack. For instance, if we consider the replication factor as 3, two copies will be stored on one rack while the third copy in a separate rack.
- What are the differences between Hadoop and RDBMS?
RDBMS schemas are based on ‘Schema on Write’ while Hadoop’s are based on ‘Schema on reading’, Data types of the Hadoop is structured, semi-structured, and unstructured while RDBMS have structured data, Speed of Hadoop is fast in writing and RDBMS is fast in reading, Cost of Hadoop is open-source and free while RDBMS is paid and licensed software and Applications of Hadoop is data recovery, storage, processing of unstructured data while RDBMS is used in OLTP and complex ACID transactions.
- What are the common input formats in Hadoop?
The common input formats in Hadoop are as follows
- Text Input Format is defined in Hadoop and it is the default format
- Sequence File Input Format to read files in a sequence
- Key-Value Input Format is used for plain text files.
- What are the various modes that Hadoop runs on?
Apache Hadoop runs in the following three modes
- Standalone or Local Mode: Hadoop runs in a local mode as a non-distributed, single node. It uses the local file system to perform input and output operations. It does not support the use of HDFS but it is used for debugging. No custom configuration is required for configuring files in Standalone mode.
- Pseudo-Distributed Mode: Hadoop runs on a single node just like a standalone mode in this pseudo-distributed mode. Each daemon runs in a separate Java process as all the daemons run a single node, and there is the same node for both master and slave nodes.
- Fully-Distributed Mode: All the daemons run on separate individual nodes to form a multi-node cluster in this fully-distributed mode. There are different nodes for master and slave nodes in this mode.
- What are the components of Hadoop?
The major components of Hadoop are HDFS, MapReduce, and YARN.
HDFS is the basic storage system of Hadoop and the large data files running on a cluster of commodity hardware that is stored in HDFS. It reliably stores data even hardware fails.
Hadoop MapReduce is the layer that is responsible for data processing. It writes an application to process unstructured and structured data that are stored in HDFS. The Map is the first phase of processing complex logic code and the Reduce is the second phase of processing the lightweight operations.
YARN is the processing framework used for resource management and provides multiple data processing engines such as data science, real-time streaming, and batch processing.
- What are the various configuration files in Hadoop?
The various configuration files in Hadoop are
- core-site.xml to configure files that contain core configuration settings like I/O settings, common files for MapReduce, and HDFS. It uses hostname a port.
- mapred-site.xml to configure files that specify a framework name for MapReduce by setting mapreduce.framework.name.
- hdfs-site.xml to configure files that contain HDFS daemons configuration settings. It specifies default block permission and replication checking on HDFS.
- yarn-site.xml to configure file that specifies configuration settings for ResourceManager and NodeManager.
The world of big data is growing continuously and the number of opportunities is arising for certified and skilled big data professionals. We hope these Big Data Interview Questions and Answers help you in the interviews. Join Softlogic to learn the best Big Data Course in Chennai with Hands-on Exposure. We offer industry-recognized certificates by completing the course along with placement assistance in our Big Data Training Institute in Chennai.