Top 25 Hadoop Interview Questions Prepared by Experts

1) Compare Hadoop & Spark

                     

Criteria                                           Hadoop                                                   Spark

Dedicated storage                           HDFS                                                     None

Speed of processing                        average                                                excellent

Libraries                                        Separate tools available                        Spark Core, SQL, Streaming, MLlib, GraphX

2)    What are real-time industry applications of Hadoop?

Hadoop, well known as Apache Hadoop, is an open-source software platform for scalable and distributed computing of large volumes of data. It provides rapid, high performance and cost-effective analysis of structured and unstructured data generated on digital platforms and within the enterprise. It is used in almost all departments and sectors today.Some of the instances where Hadoop is used:

• Managing traffic on streets.
• Streaming processing.
• Content Management and Archiving Emails.
• Processing Rat Brain Neuronal Signals using a Hadoop Computing Cluster.
• Fraud detection and Prevention.
• Advertisements Targeting Platforms are using Hadoop to capture and analyze click stream, transaction, video and social media data.
• Managing content, posts, images and videos on social media platforms.
• Analyzing customer data in real-time for improving business performance.
• Public sector fields such as intelligence, defense, cyber security and scientific research.
• Financial agencies are using Big Data Hadoop to reduce risk, analyze fraud patterns, identify rogue traders, more precisely target their marketing campaigns based on customer segmentation, and improve customer satisfaction.
• Getting access to unstructured data like output from medical devices, doctor’s notes, lab results, imaging reports, medical correspondence, clinical data, and financial data.

3)    How is Hadoop different from other parallel computing systems?

Hadoop is a distributed file system, which lets you store and handle massive amount of data on a cloud of machines, handling data redundancy. The primary benefit is that since data is stored in several nodes, it is better to process it in distributed manner. Each node can process the data stored on it instead of spending time in moving it over the network.

On the contrary, in Relational database computing system, you can query data in real-time, but it is not efficient to store data in tables, records and columns when the data is huge.

Hadoop also provides a scheme to build a Column Database with Hadoop HBase, for runtime queries on rows.

4)    What all modes Hadoop can be run in?

Hadoop can run in three modes:

• Standalone Mode: Default mode of Hadoop, it uses local file stystem for input and output operations. This mode is mainly used for debugging purpose, and it does not support the use of HDFS. Further, in this mode, there is no custom configuration required for mapred-site.xml, core-site.xml, hdfs-site.xml files. Much faster when compared to other modes.
• Pseudo-Distributed Mode (Single Node Cluster): In this case, you need configuration for all the three files mentioned above. In this case, all daemons are running on one node and thus, both Master and Slave node are the same.
• Fully Distributed Mode (Multiple Cluster Node): This is the production phase of Hadoop (what Hadoop is known for) where data is used and distributed across several nodes on a Hadoop cluster. Sepa rate nodes are allotted as Master and Slave.

5)    Explain the major difference between HDFS block and InputSplit.

In simple terms, block is the physical representation of data while split is the logical representation of data present in the block. Split acts a s an intermediary between block and mapper.
Suppose we have two blocks:
Block 1: myTectra
Block 2: my Tect
Now, considering the map, it will read first block from my till ll, but does not know how to process the second block at the same time. Here comes Split into play, which will form a logical group of Block1 and Block 2 as a single block.

It then forms key-value pair using inputformat and records reader and sends map for further processing With inputsplit, if you have limited resources, you can increase the split size to limit the number of maps. For instance, if there are 10 blocks of 640MB (64MB each) and there are limited resources, you can assign ‘split size’ as 128MB. This will form a logical group of 128MB, with only 5 maps executing at a time.

However, if the ‘split size’ property is set to false, whole file will form one inputsplit and is processed by single map, consuming more time when the file is bigger.

6)    What is distributed cache and what are its benefits?

Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files when needed. Once a file is cached for a specific job, hadoop will make it available on each data node both in system and in memory, where map and reduce tasks are executing.Later, you can easily access and read the cache file and populate any collection (like array, hashmap) in your code.

Benefits of using distributed cache are:

• It distributes simple, read only text/data files and/or complex types like jars, archives and others. These archives are then un-archived at the slave node.
• Distributed cache tracks the modification timestamps of cache files, which notifies that the files should not be modified until a job is executing currently.

7)    Explain the difference between NameNode, Checkpoint NameNode and BackupNode.

• NameNode is the core of HDFS that manages the metadata – the information of what file maps to what block locations and what blocks are stored on what datanode. In simple terms, it’s the data about the data being stored. NameNode supports a directory tree-like structure consisting of all the files present in HDFS on a Hadoop cluster. It uses following files for namespace:
  fsimage file- It keeps track of the latest checkpoint of the namespace.
  edits file-It is a log of changes that have been made to the namespace since checkpoint.
• Checkpoint NameNode has the same directory structure as NameNode, and creates checkpoints for namespace at regular intervals by downloading the fsimage and edits file and margining them within the local directory. The new image after merging is then uploaded to NameNode.
  There is a similar node like Checkpoint, commonly known as Secondary Node, but it does not support the ‘upload to NameNode’ functionality.
• Backup Node provides similar functionality as Checkpoint, enforcing synchronization with NameNode. It maintains an up-to-date in-memory copy of file system namespace and doesn’t require getting hold of changes after regular intervals. The backup node needs to save the current state in-memory to an image file to create a new checkpoint.

8)   What are the most common Input Formats in Hadoop?

There are three most common input formats in Hadoop:

• Text Input Format: Default input format in Hadoop.
• Key Value Input Format: used for plain text files where the files are broken into lines
• Sequence File Input Format: used for reading files in sequence

9)    Define DataNode and how does NameNode tackle DataNode failures?

DataNode stores data in HDFS; it is a node where actual data resides in the file system. Each datanode sends a heartbeat message to notify that it is alive. If the namenode does noit receive a message from datanode for 10 minutes, it considers it to be dead or out of place, and starts replication of blocks that were hosted on that data node such that they are hosted on some other data node.A BlockReport contains list of all blocks on a DataNode. Now, the system starts to replicate what were stored in dead DataNode.

The NameNode manages the replication of data blocksfrom one DataNode to other. In this process, the replication data transfers directly between DataNode such that the data never passes the NameNode.

10)    What are the core methods of a Reducer?

The three core methods of a Reducer are: setup(): this method is used for configuring various parameters like input data size, distributed cache. public void setup (context) reduce(): heart of the reducer always called once per key with the associated reduced task public void reduce(Key, Value, context) cleanup(): this method is called to clean temporary files, only once at the end of the task public void cleanup (context)

11)    What is SequenceFile in Hadoop?

Extensively used in MapReduce I/O formats, SequenceFile is a flat file containing binary key/value pairs. The map outputs are stored as SequenceFile internally. It provides Reader, Writer and Sorter classes. The three SequenceFile formats are: Uncompressed key/value records. Record compressed key/value records – only ‘values’ are compressed here. Block compressed key/value records – both keys and values are collected in ‘blocks’ separately and compressed. The size of the ‘block’ is configurable.

12)    What is Job Tracker role in Hadoop?

Job Tracker’s primary function is resource management (managing the task trackers), tracking resource availability and task life cycle management (tracking the taks progress and fault tolerance). It is a process that runs on a separate node, not on a DataNode often. Job Tracker communicates with the NameNode to identify data location. Finds the best Task Tracker Nodes to execute tasks on given nodes. Monitors individual Task Trackers and submits the overall job back to the client. It tracks the execution of MapReduce workloads local to the slave node.

13)    What is the use of RecordReader in Hadoop?

Since Hadoop splits data into various blocks, RecordReader is used to read the slit data into single record. For instance, if our input data is split like: Row1: Welcome to Row2: Intellipaat It will be read as “Welcome to Intellipaat” using RecordReader.

14)   What is Speculative Execution in Hadoop?

One limitation of Hadoop is that by distributing the tasks on several nodes, there are chances that few slow nodes limit the rest of the program. Tehre are various reasons for the tasks to be slow, which are sometimes not easy to detect. Instead of identifying and fixing the slow-running tasks, Hadoop tries to detect when the task runs slower than expected and then launches other equivalent task as backup. This backup mechanism in Hadoop is Speculative Execution. It creates a duplicate task on another disk. The same input can be processed multiple times in parallel. When most tasks in a job comes to completion, the speculative execution mechanism schedules duplicate copies of remaining tasks (which are slower) across the nodes that are free currently. When these tasks finish, it is intimated to the JobTracker. If other copies are executing speculatively, Hadoop notifies the TaskTrackers to quit those tasks and reject their output. Speculative execution is by default true in Hadoop. To disable, set mapred.map.tasks.speculative.execution and mapred.reduce.tasks.speculative.execution JobConf options to false.

15)    What happens if you try to run a Hadoop job with an output directory that is already present?

It will throw an exception saying that the output file directory already exists.

To run the MapReduce job, you need to ensure that the output directory does not exist before in the HDFS.

To delete the directory before running the job, you can use shell:Hadoop fs –rmr /path/to/your/output/Or via the Java API: FileSystem.getlocal(conf).delete(outputDir, true);

16)    How can you debug Hadoop code?

First, check the list of MapReduce jobs currently running. Next, we need to see that there are no orphaned jobs running; if yes, you need to determine the location of RM logs.

1. Run: “ps –ef | grep –I ResourceManager”
   and look for log directory in the displayed result. Find out the job-id from the displayed list and check if there is any error message associated with that job.
2. On the basis of RM logs, identify the worker node that was involved in execution of the task.
3. Now, login to that node and run – “ps –ef | grep –iNodeManager”
4. Examine the Node Manager log. The majority of errors come from user level logs for each map-reduce job.

17)   How to configure Replication Factor in HDFS?

hdfs-site.xml is used to configure HDFS. Changing the dfs.replication property in hdfs-site.xml will change the default replication for all files placed in HDFS.
You can also modify the replication factor on a per-file basis using the

Hadoop FS Shell:[training@localhost ~]$ hadoopfs –setrep –w 3 /my/fileConversely,

you can also change the replication factor of all the files under a directory.

[training@localhost ~]$ hadoopfs –setrep –w 3 -R /my/dir

Go through Hadoop Training to learn about Replication Factor In HDFS now!

18)    How to compress mapper output but not the reducer output?

To achieve this compression, you should set:

conf.set("mapreduce.map.output.compress", true)

conf.set("mapreduce.output.fileoutputformat.compress", false)

19)    What is the difference between Map Side join and Reduce Side Join?

Map side Join at map side is performed data reaches the map. You need a strict structure for defining map side join. On the other hand, Reduce side Join (Repartitioned Join) is simpler than map side join since the input datasets need not be structured. However, it is less efficient as it will have to go through sort and shuffle phases, coming with network overheads.

20)    How can you transfer data from Hive to HDFS?

By writing the query:

hive> insert overwrite directory '/' select * from emp;

You can write your query for the data you want to import from Hive to HDFS. The output you receive will be stored in part files in the specified HDFS path.

21)   What companies use Hadoop, any idea?

 Yahoo! (the biggest contributor to the creation of Hadoop) – Yahoo search engine uses Hadoop, Facebook – Developed Hive for analysis,Amazon,Netflix,Adobe,eBay,Spotify,Twitter,Adobe.

22)    In Hadoop what is InputSplit?

It splits input files into chunks and assign each split to a mapper for processing.

23)    Mention Hadoop core components?

Hadoop core components include,

• HDFS
• MapReduce

24)    What is NameNode in Hadoop?

NameNode in Hadoop is where Hadoop stores all the file location information in HDFS. It is the master node on which job tracker runs and consists of metadata.

25)    Mention what are the data components used by Hadoop?

Data components used by Hadoop are

• Pig
• Hive

Top 25 Data Science Interview Questions and Answers

1) How would you create a taxonomy to identify key customer trends in unstructured data?

The best way to approach this question is to mention that it is good to check with the business owner and understand their objectives before categorizing the data. Having done this, it is always good to follow an iterative approach by pulling new data samples and improving the model accordingly by validating it for accuracy by soliciting feedback from the stakeholders of the business. This helps ensure that your model is producing actionable results and improving over the time.

2) Python or R – Which one would you prefer for text analytics?

The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high performance data analysis tools.

3) Which technique is used to predict categorical responses?

Classification technique is used widely in mining for classifying data sets.

4) What is logistic regression? Or State an example when you have used logistic regression recently.

Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

5) What are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

6) Why data cleaning plays a vital role in analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because - as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

7) Differentiate between univariate, bivariate and multivariate analysis.

These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.

If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.

Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

8) What do you understand by the term Normal Distribution?

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell shaped curve. The random variables are distributed in the form of an symmetrical bell shaped curve.

 9)         What is Linear Regression?

Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.

10)       What is Interpolation and Extrapolation?

Estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

11)       What is power analysis?

An experimental design technique for determining the effect of a given sample size.

12)       What is Collaborative filtering?

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

13)       What is the difference between Cluster and Systematic Sampling?

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection, or cluster of elements. Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list,it is progressed from the top again. The best example for systematic sampling is equal probability method.

14)       Are expected value and mean value different?

They are not different but the terms are used in different contexts. Mean is generally referred when talking about a probability distribution or sample population whereas expected value is generally referred in a random variable context.

For Sampling Data

Mean value is the only value that comes from the sampling data.

Expected Value is the mean of all the means i.e. the value that is built from multiple samples. Expected value is the population mean.

For Distributions

Mean value and Expected value are same irrespective of the distribution, under the condition that the distribution is in the same population.

15)       What does P-value signify about the statistical data?

P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

•           P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.

•           P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.

•           P-value=0.05is the marginal value indicating it is possible to go either way.

16)  Do gradient descent methods always converge to same point?

No, they do not because in some cases it reaches a local minima or a local optima point. You don’t reach the global optima point. It depends on the data and starting conditions.

17)       A test has a true positive rate of 100% and false positive rate of 5%. There is a population with a 1/1000 rate of having the condition the test identifies. Considering a positive test, what is the probability of having that condition?

Let’s suppose you are being tested for a disease, if you have the illness the test will end up saying you have the illness. However, if you don’t have the illness- 5% of the times the test will end up saying you have the illness and 95% of the times the test will give accurate result that you don’t have the illness. Thus there is a 5% error in case you do not have the illness.

Out of 1000 people, 1 person who has the disease will get true positive result.

Out of the remaining 999 people, 5% will also get true positive result.

Close to 50 people will get a true positive result for the disease.

This means that out of 1000 people, 51 people will be tested positive for the disease even though only one person has the illness. There is only a 2% probability of you having the disease even if your reports say that you have the disease.

18)       What is the difference between Supervised Learning an Unsupervised Learning?

If an algorithm learns something from the training data so that the knowledge can be applied to the test data, then it is referred to as Supervised Learning. Classification is an example for Supervised Learning. If the algorithm does not learn anything beforehand because there is no response variable or any training data, then it is referred to as unsupervised learning. Clustering is an example for unsupervised learning.

19) What is the goal of A/B Testing?

It is a statistical hypothesis testing for randomized experiment with two variables A and B. The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of an interest. An example for this could be identifying the click through rate for a banner ad.

20)       What is an Eigenvalue and Eigenvector?

Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching. Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

21)       How can outlier values be treated?

Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for large number of outliers the values can be substituted with either the 99th or the 1st percentile values. All extreme values are not outlier values.The most common ways to treat outlier values –

1) To change the value and bring in within a range

2) To just remove the value.

22)       How can you assess a good logistic model?

There are various methods to assess the results of a logistic regression analysis-

•           Using Classification Matrix to look at the true negatives and false positives.

•           Concordance that helps identify the ability of the logistic model to differentiate between the event happening and not happening.

•           Lift helps assess the logistic model by comparing it with random selection.

23)       What are various steps involved in an analytics project?

•           Understand the business problem

•           Explore the data and become familiar with it.

•           Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.

•           After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.

•           Validate the model using a new data set.

•           Start implementing the model and track the result to analyse the performance of the model over the period of time.

24) How can you iterate over a list and also retrieve element indices at the same time?

This can be done using the enumerate function which takes every element in a sequence just like in a list and adds its location just before it.

25)       During analysis, how do you treat missing values?

The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights. If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored.There are various factors to be considered when answering this question-

• Understand the problem statement, understand the data and then give the answer.Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.
• If it is a categorical variable, the default value is assigned. The missing value is assigned a default value.
• If you have a distribution of data coming, for normal distribution give the mean value.
• Should we even treat missing values is another important point to consider? If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.