Go through these top Hadoop interview questions to get a competitive edge in the expanding Big Data industry, where big and small local and international businesses are looking for qualified Big Data and Hadoop expertise. With the help of this comprehensive collection of the most often asked Hadoop interview questions, you will learn how questions could be asked from Hadoop Cluster, HDFS, MapReduce, Pig, Hive, and HBase.
The use of big data has increased significantly during the last ten years. One of the most commonly used frameworks for storing, processing, and analysing Big Data is Hadoop. Hadoop is widely used in the Big Data space to address significant difficulties. Consequently, there is a constant need for people to operate in this industry. But how do you land a job in the Hadoop industry? We can certainly respond to it!
We will discuss possible Big Data Hadoop Interview questions in this blog post. The entire Hadoop ecosystem—HDFS, MapReduce, YARN, Hive, Pig, HBase, and Sqoop—will be examined as we examine Big Data Hadoop Interview Questions and Answers.
We have covered various Big Data Hadoop-related interview questions, ranging from basic to advanced.
1. What principle does the Hadoop framework operate on?
2. What operating modes does Hadoop support?
5. What operating modes does Hadoop support?
7. What daemons execute on Primary nodes?
8. Describe the various complex data types available in Pig.
9. What does logistic regression entail?
10. Mention some of the machine learning algorithms
Cloudera, MAPR, Amazon EMR, Microsoft Azure, IBM InfoSphere, and Hortonworks are the several vendor-specific Hadoop distributions (Cloudera).
The following two fundamental parts of the Hadoop Framework are used:
1) Hadoop Distributed File System, or HDFS, is a java-based file system for scalable and dependable storage of massive datasets. HDFS uses a Master-Slave architecture and stores data in the form of blocks.
2) Hadoop MapReduce - The Hadoop framework's Java-based programming paradigm offers scalability across numerous Hadoop clusters. Hadoop jobs carry out two distinct functions. MapReduce divides the burden into several concurrently running tasks. The map job divides the data sets into keyvalue pairs or tuples. After that, the reduction process takes the map job's output and joins the data tuples to create a smaller collection. The map job is always completed before the reduced job.
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The Hadoop distribution includes a generic application programming interface for building Map and Reduce tasks in any preferred programming language, such as Python, Perl, Ruby, etc. Hadoop Streaming is the term used to describe this. Any type of shell script. For executable can be used as the Mapper or Reducer in a job, and users can create and run those jobs. Spark is the newest Hadoop streaming tool.
Dual-core or twin-processor computers with 4GB or 8GB of ECC memory and dual processors are the ideal setup for running Hadoop workloads. Despite not being low-end, ECC memory offers Hadoop several advantages. Because most Hadoop users have encountered various checksum issues while using non-ECC memory, ECC memory is advised for operating Hadoop. However, depending on the needs of the process, the hardware arrangement can also alter.
The following are the most popular input formats listed by Hadoop:
It is said that Hadoop is very fault tolerant. Hadoop accomplishes this by replicating data. A Hadoop cluster has several nodes that copy data. The number of copies of the data spread among the nodes in a Hadoop cluster is indicated by the replication factor linked to the data. For instance, if the replication factor is 3, the data will be spread among three Hadoop cluster nodes, with one copy of each node's data. In this way, if one of the nodes fails, the data won't be lost because it may be recovered from another node with copies or replicas of it.
The term "big data" refers to the vast quantities of structured, unstructured, or semi-structured data that have enormous mining potential but are too big to be handled by conventional database systems. Big data's tremendous velocity, volume, and variety necessitate cost-effective and cutting-edge information processing techniques to yield actionable business insights. The nature of the data determines whether it is classified as big data, not just its amount.
Through the DistCP, HDFS offers a distributed data copying capability from source to destination. Inter-cluster data copying is used when this data copying occurs within the Hadoop cluster. The original and the goal must use the same or compatible version of Hadoop to use DistCP.
Files are written by a single writer in an append-only format in HDFS, meaning that changes to a file are always made at the end of the file. HDFS does not permit alterations at random offsets in the file or multiple writers.
In HDFS, the block size affects the indexing process. The final piece of data that points to the location of the following part of the data chunk is kept by HDFS.
Every component reliant on this HDFS namespace will be either stalled or crashed if your NN goes down and you don't have a backup (in a HA setup).So, When the NameNode is unavailable, the Hadoop task fails.
After receiving the Hadoop task, NameNode searches for the client's requested data and returns the block information. JobTracker handles Hadoop job resource allocation to guarantee prompt completion.
The mapper communicates with other Hadoop systems with the aid of Context Object. It is possible to refresh counts, report progress, and offer any application-level status updates using context objects.
Hadoop is an open-source software framework for the distributed archiving and processing of enormous amounts of data. Open source refers to something that is openly accessible and whose source code can be modified to suit our needs. Thanks to Apache Hadoop, applications can run on a system with thousands of commodity hardware nodes. Its distributed file system allows for quick data transfers between nodes. Additionally, it enables the system to function even if a node fails.
Local (Standalone) Manner - Hadoop runs by default as a solitary Java process on a node in a non-distributed mode. Pseudo-Distributed Mode: Pseudo-distributed way of Hadoop operates similarly to the Standalone mode on a single node. Fully Distributed Mode - In this mode, a multi-node cluster is formed by different nodes on which each daemon runs. It permits independent nodes for Enslaver and Slave as a result.
The vendor-specific distributions of Hadoop are Cloudera, MAPR, Amazon EMR, Microsoft Azure, IBM InfoSphere, and Hortonworks (Cloudera).
By receiving the modifications and fsimage file from the NameNode and merging them locally, the Checkpoint Node regularly creates checkpoints for the namespace. The revised image is then added back to the NameNode that is currently active. The most recent checkpoint is kept track of by Checkpoint Node in a directory similar to NameNode's directory.
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Sqoop is a tool that moves data from relational database management systems (RDBMS) to Hadoop HDFS. Sqoop may export data from HDFS files to RDBMSs and import data from RDMSs like MySQL or Oracle into HDFS.
The name node makes its placement decisions based on the rack definitions through a process known as "rack awareness."
The Combiner is a "mini-reduce" procedure that only uses data produced by a mapper to function. All information released by the Mapper instances on a particular node will be fed into the Combiner as input. Instead of the output from the Mappers, the work from the Combiner is then sent to the Reducers.
It provides the daemon's status for the Hadoop cluster. It gives the output specifying the state of the Jobtracker, Task tracker, Datanode, and Secondarynamenode.
Where daemons (services) are installed or how to check their status is specified in /etc /init.d. It has nothing to do with Hadoop and is quite LINUX-specific.
The mapper can communicate with the rest of the Hadoop system thanks to the Context Object. It has APIs that enable it to emit output and configuration information for the job.
A "Hash" Partitioner is the standard Hadoop partitioner.
The primary node can redundantly run another instance of the identical task on another node if it looks like one node is processing a job more slowly than the others. The job that completes first will then be approved, while the second is terminated. The term "speculative execution" refers to this technique.
A Java-based, multidimensional, distributed, scalable, and NoSQL database, HBase is free and open source. Hadoop gains capabilities are similar to Google's BigTable thanks to HBase, which runs on top of HDFS. It is made to offer a fault-tolerant method of storing massive amounts of sparse data sets. By enabling quicker Read/Write Access on enormous data volumes, HBase enables excellent throughput and low latency.
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Each Region Server has a Write Ahead Log (WAL) file within the distributed system. New data that has yet to be committed or persisted to permanent storage is kept in the WAL. If attempts to recover the data sets are unsuccessful, it is used.
Apache Spark, a framework for real-time data analytics in a distributed computing environment, is the answer to this query. It performs computations in memory to quicken the processing of data.
It processes massive amounts of data 100x quicker than MapReduce by utilising in-memory computations and other improvements.
Since there is no restriction on joining files, partitions, or tables, Sort Merge Bucket (SMB) join is primarily utilised in the hive. Each mapper reads a bucket from the first table and the equivalent bucket from the second table as part of the SMB join in Hive, and then a merge sort join is carried out. Large tables are best suited for SMB join usage. The join columns are used to bucket and sort the columns in SMB join. The SMB join should use the same amount of buckets for all tables.
YARN, often known as Hadoop 2.0 or MapReduce 2 join, is a more potent and effective solution that supports MapReduce but is not a replacement for Hadoop.
No. Due to its dependence on the entire HDFS, Namenode can never be considered commodity hardware. It is HDFS's only point of failure. There must be a high availability machine for Namenode.
The directory structure used by Apache and Cloudera is identical. Cd to /usr/lib/Hadoop to launch Hadoop
The Hadoop distribution is called Cloudera. Cloudera, used for data processing, is a part of Apache. It is a user that is automatically created on the VM.
Use the /etc/init.d/hadoopnamenode status command to see if Namenode is operational or not.
start-all.sh - Starts all Hadoop daemons, the namenode, datanodes, the jobtracker and tasktrackers. Deprecated; use start-dfs.sh then start-mapred.sh. stop-all.sh - Stops all Hadoop daemons.
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Yes, we can likewise accomplish that once we are comfortable with the Hadoop environment.
A medium- to large-sized Hadoop cluster often has a two- or three-level design constructed using rack-mounted computers. A 1 Gigabyte Ethernet (GbE) switch is used to link each server rack to the other.
hive> insert overwrite directory '/' select * from emp;
You can create your query to import the data from Hive to HDFS. The component files in the designated HDFS location will contain the output you receive.
Remote users can define and run saved jobs made with sqoop jobs depicted in the metastore utilising a shared metadata repository called sqoop. Connecting to the megastore should be configured in school-site.xml.
The critical components of Kafka are:
The definition of Kafka given by Wikipedia is "an open-source message broker project developed by the Apache Software Foundation written in Scala, where transaction logs primarily influence the design."
A distributed publish–subscribe messaging system is essentially what it is.
To maintain offsets of messages consumed for a particular topic and partition by a special Consumer Group, Kafka employs Zookeeper.
Linux is the primary operating system that is supported. Hadoop may, however, be installed on Windows with the help of some additional applications.
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On a standalone system, instal the secondary namenode. It is necessary to deploy the secondary namenode on a different workstation. It won't affect the principal namenode operations. The primary namenode's secondary namenode must meet the exact memory requirements.
Since the edit log will continue to expand, cluster performance will deteriorate with time.
The edit log will expand significantly and slow the system down if the secondary namenode is not operating. Additionally, because the namenode needs to integrate the edit log and the most recent filesystem checkpoint image, the system will spend a considerable time in safe mode.
A class that extends the MapFile class is called BloomMapFile. It uses dynamic bloom filters to quickly assess the keys' membership in the HBase table format.
Each element in the data bag is transformed using Apache Pig's FOREACH operation, which causes the corresponding action to be taken to produce new data items. FOREACH data bagname GENERATE exp1, exp2 is the syntax.
The answer is that the case does not always matter in pig Latin. Using an example,
Apache Flume is a system for effectively gathering, aggregating, and transporting substantial volumes of log data from numerous sources to a centralised data source. It is distributed, dependable, and always accessible. Read this Flume use case to learn how Mozilla gathers and analyses the logs using Flume and Hive.
Flume is a framework for feeding data into Hadoop. Agents are populating one's IT infrastructure, such as web servers, application servers, and mobile devices, to gather data and incorporate it into Hadoop.
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It was created by Cloudera to aggregate and move enormous amounts of data. The primary use is asynchronously persisting log files from various sources in the Hadoop cluster. To obtain data from social media websites, Hadoop developers most frequently use this as well.
Purely functional data structures from the Scalaz library are an addition to the basic Scala library. It comes with a pre-defined collection of fundamental type classes, including Monad and Functor.
Since R lacks this functionality, Python has a robust Pandas package that lets analysts employ highlevel data analysis tools and data structures. Python will therefore be better appropriate for text analytics.
A statistical method or model analyses a dataset and forecasts a binary result. The result must be binary, meaning it can only be yes, no, or zero.
Hive's hive.fetch.task.conversion attribute reduces the overhead of the MapReduce function and, as a result, skips the MapReduce function when executing queries like SELECT, FILTER, LIMIT, etc.
To transform complex data types into the necessary table formats, Hive's Explode tool is employed. In essence, UDTF emits each element in an array into several rows.
A collection of the most recent machine learning algorithms revealed by Mahout is provided below :
start-all.sh - Starts all Hadoop daemons, the namenode, datanodes, the jobtracker and tasktrackers. Deprecated; use start-dfs.sh then start-mapred.sh. stop-all.sh - Stops all Hadoop daemons
Yes, we can likewise accomplish that once we are comfortable with the Hadoop environment. Typically, A medium- to large-sized Hadoop cluster often has a two- or three-level design constructed using rack-mounted computers. A 1 Gigabyte Ethernet (GbE) switch is used to link each server rack to the other.
You must, first and foremost, refresh your skill set with the necessary technology and equipment. The crucial competencies you should concentrate on for your ample data interview preparation are listed below:
The area of data science is fundamentally dependent on statistics. Massive data sets are frequently processed using statistics. Thus, having a rudimentary understanding of concepts like the P value, confidence intervals, the null hypothesis, and others is helpful. R and other statistical software are frequently used in experimentation and decision-making. In addition, machine learning is now one of the critical competencies in the extensive data ecosystem.
In the interview, knowing random forests, nearest neighbours, and ensemble approaches can be helpful. These methods can be applied well in Python, and exposure to them can help you gain data science competence.
Knowing the fundamentals of programming and statistical programming languages like R and Python and database languages like SQL is helpful. You will be at an advantage if you have worked with Java and other programming languages. In addition, if you don't
know about dynamic programming, you should strive to learn it. You should also be familiar with data structures, algorithms, and fundamental programming skills. The aspirants can benefit from the runtime and use cases of data structures such as arrays, stacks, lists, and trees.
Data scientists benefit significantly from tools for data visualisation like ggplot. Understanding how to use these tools can help you organise and comprehend how data is used in practical applications. Additionally, you should be able to clear up data. Data wrangling aids in detecting corrupt data and the methods for erasing or fixing it. Therefore, having an understanding of data manipulation and data visualisation tools will be helpful for you as you prepare for an extensive data interview.
There are vast data sets available for study from companies like Facebook, Amazon, Google, and eBay. The same is true for online stores and social media platforms. Therefore, hiring managers are looking for engineers with professional certificates or expertise working with large data companies.
Two categories of crucial configuration files control how Hadoop is configured:
The five properties of volume, value, diversity, velocity, and truthfulness are frequently used to describe big data, a compilation of information from numerous sources.
To examine the HDFS's condition, use the fsck Hadoop command. A damaged file is sent to the lost+found directory. It eliminates the HDFS files that are corrupted. The files being checked are printed.
The Hadoop Ecosystem is a platform or collection of tools offering services to address significant data issues. Hadoop comprises four main components: HDFS, MapReduce, YARN, and Hadoop Common. It consists of Apache projects and several paid tools and services.
MapReduce makes concurrent processing easier by dividing petabytes of data into smaller chunks and processing them in parallel on Hadoop commodity servers. In the end, it collects all the information from several servers and gives the application a consolidated output.
The followings are the restrictions and drawbacks of Hadoop 1. x:
Utilising all of the storage and processing power of cluster servers and running distributed processes on enormous volumes of data are made simpler by Hadoop. On top of Hadoop's building blocks, different services and applications can be developed.
Files are divided into blocks by HDFS, and each block is kept on a DataNode. The cluster is linked to several data nodes. Replicas of these data blocks are subsequently dispersed throughout the cluster by the NameNode. Additionally, it gives the user or program directions on where to find the desired information.
Since they can handle various structured and unstructured data types, Hadoop systems offer customers greater flexibility for data collection, processing, analysis, and management than relational databases and data warehouses.
We are sure that this post has aided you in preparing for the key Hadoop interview questions. You now have a general concept of the types of Hadoop interview questions to expect and how to prepare responses.
Furthermore, The Big Data certification program will teach you more about Hadoop Clusters, installation, and other topics that will cater to rendering your skills!
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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