The world of Hadoop is constantly evolving, and you must stay up-to-date on the latest trends. The market is continually growing for Big Data and Hadoop experts. There are many positions available, so it is essential that you know the answers to these frequently asked questions before your interview.
This Hadoop interview questions blog will cover the top 20 frequently-asked Hadoop Interview questions that can help you ace an upcoming job interview.
Let’s give you an insight into how demand for Big Data specialists and those with expertise in distributed storage systems like HDFS is constantly increasing as more companies turn towards these technologies to power their data-driven future initiatives.
The need for Big Data Hadoop certification is on the rise. The growth in the demand for Hadoop experts has led to higher salaries.
The Big Data Revolution
The evolution of data analytics has been a long process, with organisations initially only paying attention to operational information.
The need for analytics in an organisation’s operations has been a long-standing issue. Many companies only recently began their journey into revealing how much value it indeed provides when put together as one cohesive unit rather than being separated into different departments.
The time when big giants like Yahoo, Facebook and Google started adopting Hadoop technology was an exciting one for those interested in Big Data related fields. Today, it’s not uncommon to see every other organisation and businesses adopt these technologies as they move towards analytics-driven business models.
Big Data has come to the forefront of technology and innovation. Learning Hadoop and Spark can help your career skyrocket with proper knowledge and upskilling. As a fresher or seasoned professional, these will help in boosting your career no matter where life takes us.
That said, this definitive list of top 20 Hadoop interview questions can act as a resource for your following data analysis or Big Data job interviews so that potential employers can see how well prepared you are.
Big Data is termed as large amounts of data that exceed the processing capacity and require unique mechanisms. It is structured or unstructured, depending on what you are looking for in your information retrieval efforts.
Big Data is a new term created to describe three main characteristics. These include the volume, variety, and velocity of information collected by sensors all over our planet at an incredible speed, which ties into AI technology to analyse better what we see every day.
Big Data Characteristics are-
Volume: Can refer to anything that has been measured by taking up space within units such as gigabytes or PetaBytes, for instance.
Velocity: Refers precisely how fast this data changes rate each second, which has led to much attention due mainly to Social Media platforms where users post constantly uploaded information about themselves onto these sites.
Veracity: Refers to the degree of uncertainty in data. It’s either precise and certain (neatly organised), imprecise with some uncertain aspects like an audio recording that may not capture all sounds made by someone speaking clearly into a microphone at one moment, but might do when combined with other sources such as audios from different people who’ve recorded themselves talking concurrently then analysed together later on for comparison purposes after transcription has taken place if the need arises).
Variety: Data coming from various sources is called “variety”. It covers everything ranging from videos & CSV files. It can be either unstructured, semi-structured, or structured.
Value: An essential characteristic of Big data because it provides such a critical element for success. It offers information about how to access and deliver quality analytics, which gives organisations valuable insights into their business objectives and fair market value on used technology within your organisation.
1. What is Hadoop? List its core components.
Hadoop is a framework (open-source) for storing large data sets and running applications across clusters of commodity hardware. It offers extensive storage with the ability to process endless parallel tasks, all on top of its simple processing architecture, which reduces operational complexity while providing an extremely robust platform in today’s most demanding environments.
Hadoop Core components are Storage Unit HDFS (NameNode and DataNode) and Processing Framework YARN (ResourceManager and NodeManager).
1. What are HDFS and YARN and their respective components?
HDFS (Hadoop Distributed File System) is Hadoop’s primary data storage unit, which stores various blocks in a distributed environment. HDFS follows master/slave topology and has many different sub-systems that work together seamlessly to provide you with an efficient way to store all your information.
NameNode: It’s the most crucial component in HDFS. It’s responsible for maintaining information about blocks of data stored and manages all DataNodes that store Slaves (the slaves). It keeps track of metadata to make sure everything runs smoothly across multiple nodes.
DataNode: It’s the slave node. It is responsible for storing data in the HDFS.
YARN is Yet Another Resource Negotiator. Ir is a key component of Hadoop and is responsible for managing resources, such as computing power or network bandwidth. It also schedules tasks on different cluster nodes to help optimise usage across all applications operating in the system.
The Resource Manager: It is a software system that manages resources in the cluster. It runs on one master daemon and controls allocation for all of its servers while also being responsible for telling each Data Node when it needs more memory or other requirements from outside sources like databases etc.,
The Node Manager: It talks with application masters that maintain job lifecycle info and what tasks need executing at any given time based on these priorities (either set manually through human intervention).
Application Master: It is where you’ll find all of your applications and their specific requirements. It’s also responsible for maintaining the user job lifecycle and other resources needed throughout execution processes, such as Node Manager or Task Scheduler jobs. Along with Node Manager, Application Master controls when tasks should run on nodes based on these internal data tables for each type (e .g., memory versus disk).
1. What are the differences between Hadoop 1.0 and Hadoop 2.0?
While answering this question, focus mainly on two points- Passive NameNode and YARN architecture.
In Hadoop 1.0, the NameNode was a single point of failure because it had all data and metadata for the cluster stored on one machine, which could fail if that server became unavailable.
In Hadoop 2, we have Active Nodes and Passive Node instances where both types are used to provide an extra layer between HDFS storage backend (block store) operations and MapReduce jobs running in adjacent nodes within each rack-load balancing pool. This way, should any problem arise with either type, there’ll still be another copy available from another place for increased high availability capabilities when needed most.
In the latest version of Hadoop, YARN has been improved to provide a central resource manager. It allows users to run multiple applications on top of it without conflicting with one another while still using up resources in Yarn at any given time – though not all types will be able to use this feature due to some restrictions imposed by MapReduce apps on how they interact with each other via MRv2 framework running under them.
The ability for these different tools or programs through input/output channels is to avoid conflicts within your system new update makes things much more manageable.
1. What are the differences between HDFS and Network Attached Storage (NAS)?
First, explain NAS and HDFS, and then compare their features.
A NAS is a device that allows for the storage and retrieval of data. This could be used by anyone in any field, from home office workers to professionals on an enterprise level. Network-attached storage can either come as software or hardware.
Whereas, Hadoop Distributed File System (HDFS) focuses specifically around large files with high availability requirements using commodity servers like CPUs/RAMs etc., instead of specialised equipment usually found at big companies who deal primarily with these tasks.
The HDFS Data Blocks are distributed across all the machines in a cluster. While, NAS data is stored on dedicated hardware for each disk drive that contains this type of media – magnetic or optical disks.
Folders within a Hadoop Distributed File System (HDFS) contain files and other metadata, such as addressing information about their location on one specific machine’s storage volume(s). The name given reflects its nature: “Distributed” means multiple copies spread out among many servers; these serve up chunks from large unlimited-size ‘DataBlock’, which can be anywhere between 1 KB and 64 TB per block size.
HDFS is designed for a MapReduce-based workflow, where the job of computing happens on data. NAS does not work with this paradigm since it stores your files and other data separately from their computations, making them less efficient when using Hadoop in combination with multiple servers.
The high cost of NAS storage devices is one reason why many companies are turning to HDFS for their commodity needs.
The more affordable and accessible hardware used in this case, the better it will be able to serve as an engine driving data analytics while simultaneously cutting down total operating costs.
1. What are the site-specific Configuration Files in Hadoop?
1. What is Hadoop MapReduce? List its features.
MapReduce is an efficient programming model for accessing and processing data stored on Hadoop servers. It automatically splits large datasets into small pieces that can be processed in parallel, saving time when doing analysis or computations with this information.
MapReduce is a programming model that enables data local processing. MapReduces’ automatic parallelisation and distribution, built-in fault tolerance and redundancy are available for safe execution of your program without any disruption in case if anything goes wrong with it due to the use of distributed system capabilities like networking or unreliable servers; this makes Mapper (transaction) simplify coding tasks much faster than other languages do so as well equalises runtime complexity between processes which helps manage all these inter-process communications effectively too.
1. What is Apache Spark? List its components?
Apache Spark is a framework used for real-time data analytics in a distributed computing environment. It provides faster analytics than Hadoop MapReduce, mainly due to its capability of processing large volumes with low latency and high performance across many machines simultaneously without the need to build separate nodes or rack servers as part of your application design; this also makes it easier on IT organisations because they don’t have support hardware costs associated when using other general-purpose platforms like Hive which requires expensive GPU cards just so you can run some query against your database.
Apache Spark Components:-
1) Spark Core Engine – Consists of all core functionality for processing large datasets and running queries against them.
2) MLlib – Contains Stanford University’s Machine Learning laboratory implementations such as regression models, classification trees, etc.
3) GraphX – Provides graph connectivity tools like shortest pathfinding or clustering based on community detection between nodes.
4) Spark SQL – A new program from Toptal that makes it easier and faster for users to query their data in Hadoop. The system was designed on top of Shark, which replaced the execution engine within Hive but still uses similar algorithms like related queries via projection files called Shark Directed Acyclic Graphs (SDGs).
5) Spark Streaming – A popular filter for streaming data. This particular software iteration has been created to allow it to stream gigabytes per second with ease, which makes these libraries helpful tools in our quest towards Big Data processing power and speed
6) Spark R – R programming language is widely used by Data scientists to the point that it’s a staple of their toolkit. R has been proven time and again, with its ability to run complex algorithms as well as being simple enough for beginners.
1. List the three modes that Hadoop can run.
Standalone (local) mode: When in this mode, Hadoop runs as a single Java process with all components. It accesses files locally and not remotely like when it is installed on top of the explicit DataNode or NameNode servers which require special configurations to run properly. One instance can run at any given time; if multiple are detected, then only one will take effect until another has been terminated for some reason (ejected by Control-C). In local mode, there’s no interaction between nodes, so each node does everything independently but also synchronously—it happens pretty quickly because they wait upon each other.
Pseudo-distributed mode: In this mode, a single node can house the entire Hadoop system. The master service and slave services are both executed on this one machine in order to have efficient use of resources without sacrificing scalability or availability for other tasks running at the same time. A distributed deployment where all components run locally (including masters) is called “pseudo”. A “true” implementation would require more hardware than what we’re able to purchase today – so instead, it’s just pretending.
Fully distributed mode: A Hadoop deployment in which the master and slave services are run on separate nodes, making it a fully decentralised system.
1. Explain Apache Hive, Apache Pig, Apache ZooKeeper, and Apache Oozie. List the benefits of using each.
Apache Hive is defined as a data warehouse system that abstracts the complexity of Hadoop MapReduce. It’s used to analyse structured and semi-structured information developed by Facebook, making it one of the most popular tools in this industry.
Benefits: The significant advantage of using Apache Hive is that it takes very little time to write queries, unlike MapReduce code. It’s also easier and more intuitive than writing HQL statements since you’re not dealing with complicated precedence rules like those found within SQL-92; instead, Hive provides a simple declarative language that allows for joins across different file formats; efficiently too! In addition, there have been multiple studies proving how user friendly this system manages to be without sacrificing any performance benefits whatsoever. So whether we’re talking about business or just personal use cases here (I would argue), everyone can find something worth their attention.
Apache Pig can execute its Hadoop jobs in MapReduce, Apache Tez or Spark. It also has high-level features that simplify program development for data analysing tasks like joins and group by operations among others.
Benefits: Apache Pig has many benefits, such as being an easy-to-use programming language that will further reduce the deployment time of your MapReduce jobs by using Hadoop’s cluster. It’s also great for programmers and software developers who want to create their own programs on top of pig Latin.
The Apache ZooKeeper project aims to provide highly reliable distributed coordination for cloud applications. It is a part of the Apache Software Foundation, which also hosts projects like Hadoop and Tomcat, among others.
Benefits- Apache ZooKeeper has many benefits, including distributed coordination, synchronisation and co-operation between server processes. It also offers ordered messages to keep track of data atoms that are serialised according to specific rules before being sent across the network for reliability purposes if anything were to happen at either end. Atomicity ensures transactions succeed entirely or fail entirely but never partial, so you can rest assured your application will go seamlessly from the start until finish without any hiccups whatsoever; thanks again, Apache ZooKeeper.
Apache Oozie is a workflow scheduling system to manage Hadoop jobs. A collection of action nodes and control flow in an acyclic graph, or directed graph for that matter, define how the flows are executed at each step along their respective paths with some formality included too.
Benefits: Oozie is the industry-leading Apache project for managing Hadoop jobs. It’s scalable and reliable to monitor your job in any cluster, it supports various types of MapReduce tasks like Hive or Pig as well as other languages on top of HDFS such as SQL, which makes processing very efficient while keeping data secure with encryption at all times (even when running locally). With Oozie, you have complete control over how grid programming paradigms work inside a certain workflow because its extensible architecture means that new programs can be added without changing anything else outside them – making sure everything still works together properly even if there are slight changes made within individual components.
1. What are the types of Znode?
Persistence Znode is an important factor in the functionality and longevity of a properly functioning Znodes system. It can even be considered one of its most defining features, as Persistent Znodes will remain active regardless of their parent client disconnects them from that point on (unless otherwise specified).
Ephemeral Znode is active until the client is alive. When a client gets disconnected from ZooKeeper, these ephemeral nodes automatically delete themselves to keep up with in-cluster stability and performance without sacrificing availability for your application’s clients. It is crucial for Leader election because it serves as a temporary home to nodes that are deleted. When an empty space becomes available, the next closest candidate eagerly takes its place and easily makes all future leader elections possible.
Sequential znodes are persistent and follow the path they were created in. If a new sequential node is made, then ZooKeeper sets up its name by assigning it a ten-digit sequence number from 0-999, which will be followed thereafter on all future operations for this particular instance of Sequential Znode 1 (with regards to our example).
1. What are the Hadoop HDFS commands?
1. What are the Apache Sqoop features?
1. Explain DistCp in Hadoop.
DistCp is a powerful tool that can copy large files between clusters. The distributed algorithm used in this process, MapReduce, for accurately distributing copies and handling error recovery and reporting with its built-in functionality to expand lists of directories into input data needed by individual task runners – all while maintaining high performance.
1. List the different types of Hadoop schedulers.
– Fair, FIFO, and Capacity
1. How to skip the bad records in Hadoop?
Hadoop provides an option where a certain set of bad input records can be skipped when processing map inputs. Applications control this feature through the SkipBadRecords class, which is useful for applications that sometimes crash on particular types of shapes in your data – usually, because there are bugs within their Map function.
1. List the difference between active and passive NameNodes.
Active NameNode is a worker in the cluster. It works and runs all day to keep things running smoothly. Passive Name Node has similar data as its active counterpart, but when Active node fails, it takes over for that one until something can be done about fixing or replacing them both.
1. Explain how to keep an HDFS cluster balanced.
A data cluster may become unbalanced due to the natural fluctuations in a system. To give balance and stability, use the Balancer tool, which tries its best to balance block distribution and maintain an even processing speed across all nodes within it.
1. List the components of Apache HBase.
Region Server: The Hadoop cluster is powered by a Region Server process that handles read, write, update and delete requests from clients.
HMaster: The region server runs on each individual node of the Hadoop’s ecosystem inside this system and monitors its performance with help from a master known simply as “HMaster”.
ZooKeeper: ZHBase uses ZooKeeper to keep track of the servers in its distributed environment.
1. List the difference between RDBMS vs Hadoop.
RDBMS: unable to store and process a large amount of data.
Hadoop: Works for all data types or sizes. It can easily handle any size compared to traditional databases limited by their storage capabilities, while processing power is often an issue too; however, one thing you need with Hadoop (or most things) is more space.
RDBMS: cannot achieve a high Throughput.
Hadoop: can achieve high Throughput.
RDBMS: Schema of the data is known in RDBMS, and it always depends on structure, though not all types are capable.
Hadoop stores any kind like unstructured or semi-structured ones with a bit of flexibility for transformation later down the line if needed, so that means more potential applications.
RDBMS: Supports Online Transactional Processing (OLTP).
Hadoop: Supports Online Analytical Processing (OLAP).
RDBMS: The read time in RDBMS is fast because of the schema validation that occurs beforehand.
Hadoop: Write with speed on HDFS can be accelerated with no need for verifying stored data.
Schema on reading Vs Write
RDBMS: It follows the schema on write policy.
Hadoop: It follows the schema on reading policy.
RDBMS: It is licensed software.
Hadoop: It is a free and open-source framework.
The Hadoop Interview Questions for 2021 are constantly changing and evolving. The aforementioned list of the Top 20 questions will help you prepare for your following interview. They will help you better understand how this powerful tool can improve efficiency, storage capacity, security, and more. If you want to reach closer to your goal of cracking the interview, prepare well and in advance.