[SCENARIO-BASED ] Mahout Interview Questions and Answers

Last updated on 17th Nov 2021, Blog, Interview Questions

If you are preparing for Mahout Interview, then you are at the right place. Today, we will cover some mostly asked Mahout Interview Questions, which will boost your confidence. The Mahout course learning Tools for use on analyzing Big-data, how to setup Apache mahout cluster, History of Mahout, etc. Therefore, Mahout professionals need to encounter interview questions on Mahout for different enterprise Mahout job roles. The following discussion offers an overview of different categories of interview questions related to Mahout to help aspiring enterprise Mahout Professionals.

1. What is Apache Mahout?

Ans:

Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop® and using the MapReduce paradigm. Machine learning is a discipline of artificial intelligence focused on enabling machines to learn without being explicitly programmed, and it is commonly used to improve future performance based on previous outcomes.

Once big data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in those big data sets. The Apache Mahout project aims to make it faster and easier to turn big data into big information.

2. What does Apache Mahout do?

Ans:

Mahout supports four main data science use cases:

Collaborative filtering: mines user behavior and makes product recommendations (e.g. Amazon recommendations).

Clustering: takes items in a particular class (such as web pages or newspaper articles) and organizes them into naturally occurring groups, such that items belonging to the same group are similar to each other.

Classification: learns from existing categorizations and then assigns unclassified items to the best category.

Frequent item-set mining: analyzes items in a group (e.g. items in a shopping cart or terms in a query session) and then identify which items typically appear together

3. What is the History of Apache Mahout? When did it start?

Ans:

The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to:

Build and support a community of users and contributors such that the code outlives any particular contributor’s involvement or any particular company or university’s funding.

Focus on real-world, practical use cases as opposed to bleeding-edge research or unproven techniques.

4.What are the features of Apache Mahout?

Ans:

Although relatively young in open source terms, Mahout already has a large amount of functionality, especially in relation to clustering and CF. Mahout’s primary features are:

• Taste CF. Taste is an open source project for CF started by Sean Owen on SourceForge and donated to Mahout in 2008.
• Several Mapreduce enabled clustering implementations, including k-Means, fuzzy k-Means, Canopy, Dirichlet, and Mean-Shift.
• Distributed Naive Bayes and Complementary Naive Bayes classification implementations.
• Distributed fitness function capabilities for evolutionary programming.
• Matrix and vector libraries.
• Examples of all of the above algorithms.

5. What is the Roadmap for Apache Mahout version 1.0?

Ans:

Scala	Spark & h2o
In addition to Java, Mahout users will be able to write jobs using the Scala programming language. Scala makes programming math-intensive applications much easier as compared to Java, so developers will be much more effective.	Mahout 0.9 and below relied on MapReduce as an execution engine. With Mahout 1.0, users can choose to run jobs either on Spark or h2o, resulting in a significant performance increase.

6.How is it different from doing machine learning in R or SAS?

Ans:

Unless you are highly proficient in Java, the coding itself is a big overhead. There’s no way around it, if you don’t know it already you are going to need to learn Java and it’s not a language that flows! For R users who are used to seeing their thoughts realized immediately, the endless declaration and initialization of objects is going to seem like a drag. For that reason, I would recommend sticking with R for any kind of data exploration or prototyping and switching to Mahout as you get closer to production.

7. Mention some machine learning algorithms exposed by Mahout?

Ans:

Below is a current list of machine learning algorithms exposed by Mahout:

• Collaborative FilteringM
• Item-based Collaborative Filtering
• Matrix Factorization with Alternating Least Squares
• Matrix Factorization with Alternating Least Squares on Implicit Feedback
• Classification
• Naive Bayes
• Complementary Naive Bayes
• Random Forest
• Clustering
• Canopy Clustering
• k-Means Clustering
• Fuzzy k-Means
• Streaming k-Means
• Spectral Clustering
• Dimensionality Reduction
• Lanczos Algorithm
• Stochastic SVD
• Principal Component Analysis
• Topic Models
• Latent Dirichlet Allocation
• Miscellaneous
• Frequent Pattern Matching
• RowSimilarityJob
• ConcatMatrices
• Collocations

8. Which type of the data can be imported to HDFS with the help of flume?

Ans:

Flume only ingests unstructured data or semi-structured data into HDFS. While Sqoop can import as well as export structured data from RDBMS or Enterprise data warehouses to HDFS or vice versa.

9. Mention some use cases of Apache Mahout?

Ans:

• Commercial Use
• ADOBE CAMPuses Mahout’s clustering algorithms to increase video consumption by better user targeting.
• Accenture uses Mahout as a typical example for their Hadoop Deployment Comparison Study
• AOLuse Mahout for shopping recommendations. See slide deck
• BOOZ ALLEN HAMILTONuses Mahout’s clustering algorithms. See slide deck
• Buzzlogicuses Mahout’s clustering algorithms to improve ad targeting
• tv uses modified Mahout algorithms for content recommendations
• DATAMINE LABuses Mahout’s recommendation and clustering algorithms to improve our clients’ ad targeting.
• DRUPALusers Mahout provides open source content recommendation solutions.
• Evolv uses Mahout for its Workforce Predictive Analytics platform.
• FOURSQUAREuses Mahout for its recommendation engine.

10. Define an Apache Mahout architecture overview?

Ans:

11. Define Academic use of the Mahout?

Ans:

Academic Use

• Codeproject uses Mahout’s clustering and classification algorithms on top of HBase.
• The course Large Scale Data Analysis and Data Mining At TU Berlin uses Mahout to teach students about the parallelization of data mining problems with Hadoop and Mapreduce
• Mahout is used at Carnegie Mellon University, as a comparable platform to GraphLab
• The ROBUST project, co-funded by the European Commission, employs Mahout in the large-scale analysis of online community data.
• Mahout is used for research and data processing at Nagoya Institute of Technology, in the context of a large-scale citizen participation platform project, funded by the Ministry of Interior of Japan.
• Several types of research within Digital Enterprise Research Institute NUI Galway use Mahout for e.g. topic mining and modeling of large corpora. Mahout is used in the NoTube EU project.

12. How can we scale Apache Mahout in Cloud?

Ans:

Mahout to scale effectively isn’t as straightforward as simply adding more nodes to a Hadoop cluster. Factors such as algorithm choice, number of nodes, feature selection, and sparseness of data — as well as the usual suspects of memory, bandwidth, and processor speed — all play a role in determining how effectively Mahout can scale. To motivate the discussion, I’ll work through an example of running some of Mahout’s algorithms on a publicly available data set of mail archives from the Apache Software Foundation (ASF) using Amazon’s EC2 computing infrastructure and Hadoop, where appropriate.

13. Is “talent crunch” a real problem in Big Data? What has been your personal experience around it?

Ans:

Yes. The talent-crunch is a real problem. But finding really good people is always hard. People over-rate specific qualifications. Some of the best programmers and data scientists I have known did not have specific training as programmers or data scientists. Jacques Nadeau leads the MapR effort to contribute to Apache Drill, for instance, and he has a degree in philosophy, not computing. One of the better data scientists I know has a degree in literature. These are widely curious people who are voracious learners. Combine that with a good sense of mathematical reasoning and a person can go quite far.

14. Which kind of main data science use cases do you support?

Ans:

Mahout supports four main data science use cases: Collaborative filtering: mines user behavior and makes product recommendations (e.g. Amazon recommendations)

15. What is the difference between Apache Mahout and Apache Spark’s MLlib?

Ans:

Mahout	Apache Spark
it is Hadoop MapReduce and in the case of MLib.	To be more specific – from the difference in per job overhead If Your ML algorithm mapped to the single MR job – the main difference will be only startup overhead, which is dozens of seconds for Hadoop MR, and let say 1 second for Spark.

16. What motivated you to work on Apache Mahout? How do you compare Mahout with Spark and H2O?

Ans:

Well, some good friends asked me to answer some questions. From there it was a downhill slope. First, a few questions to be answered. Then some code to be reviewed. Then a few implementations. Suddenly I was a committer and was strongly committed to the project.

With respect to Spark and H2O, it is difficult to make direct comparisons. Mahout was many years ahead of these other systems and thus had to commit early on to much more primitive forms of scalable computing in order to succeed. That commitment has lately changed and the new generation of Mahout code supports both Spark and H2O as computational back-ends for modern work.

17. What are the algorithms used by Mahout?

Ans:

Mahout uses the Naive Bayes classifier algorithm.

18. How does Apache Mahout work?

Ans:

Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop® and using the MapReduce paradigm. … Once big data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in those big data sets.

19. What is Mahout?

Ans:

Apache Mahout is a powerful, scalable machine-learning library that runs on top of Hadoop MapReduce. It is an open source project that is primarily used for creating scalable machine learning algorithms. Maintained by. License Type.

20. What is my recommendation?

Ans:

21.What will the Apache driver do?

Ans:

Mahout supports four main information science use cases:

Collaborative filtering – mines user behaviour and makes product recommendations (e.g. Amazon recommendations)

Clustering – takes things in a very explicit category (such as sites or newspaper articles) and organizes them into present teams, specified things happiness to a similar cluster square measure just like one another

Classification – learns from existing categorizations so assigns unclassified things to the most effective class

22. What is the History of Apache Mahout? Once did it start?

Ans:

The driver project was started by many folks concerned within the Apache Lucene (open supply search) community with a vigorous interest in machine learning and a want for strong, well-documented, scalable implementations of common machine-learning algorithms for bunch and categorization. The community was ab initio driven by nanogram et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) however has since evolved to hide abundant broader machine-learning approaches

23. What square measures the options of Apache Mahout?

Ans:

Although comparatively young in open supply terms, driver already encompasses a great deal of practicality, particularly in relevance bunch and CF. Mahout’s primary options are:

Taste CF. style is an open supply project for CF started by Sean Owen on SourceForge and given to drivers in 2008.

Several Mapreduce enabled bunch implementations, as well as k-Means, fuzzy k-Means, Canopy, Dirichlet, and Mean-Shift.

Distributed Naive Thomas {bayes|mathematician} and Complementary Naive Bayes classification implementations.

24. What is commodity hardware?

Ans:

Commodity Hardware refers to inexpensive systems that do not have high availability or high quality. Commodity Hardware consists of RAM because there are specific services that need to be executed on RAM. Hadoop can be run on any commodity hardware and does not require any super computer s or high end hardware configuration to execute jobs.

25. Explain about the process of inter cluster data copying?

Ans:

HDFS provides a distributed data copying facility through the DistCP from source to destination. If this data copying is within the hadoop cluster then it is referred to as inter cluster data copying. DistCP requires both source and destination to have a compatible or same version of hadoop.

26. What is mahout used for?

Ans:

Apache Mahout is a powerful, scalable machine-learning library that runs on top of Hadoop MapReduce. It is an open source project that is primarily used for creating scalable machine learning algorithms.

27. Who uses Mahout?

Ans:

The companies using Apache Mahout are most often found in the United States and in the Computer Software industry. Apache Mahout is most often used by companies with 50-200 employees and 1M-10M dollars in revenue.

28. What is the process to change the files at arbitrary locations in HDFS?

Ans:

HDFS does not support modifications at arbitrary offsets in the file or multiple writers but files are written by a single writer in append only format i.e. writes to a file in HDFS are always made at the end of the file.

29.What is Apache web server?

Ans:

Apache web server HTTP is a most popular, powerful and Open Source to host websites on the web server by serving web files on the networks. It works on HTTP as in Hypertext Transfer protocol, which provides a standard for servers and client side web browsers to communicate. It supports SSL, CGI files, Virtual hosting and many other features.

30. What is Apache Mahout and machine learning?

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31. What is the role of Mahout in the Hadoop ecosystem?

Ans:

Mahout is open source framework for creating scalable machine learning algorithm and data mining library. Once data is stored in Hadoop HDFS, mahout provides the data science tools to automatically find meaningful patterns in those big data sets.

32.How many algorithms does Mahout support for clustering?

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Mahout supports two main algorithms for clustering namely: Canopy clustering. K-means clustering.

33. What is Row Key?

Ans:

Every row in an HBase table has a unique identifier known as RowKey. It is used for grouping cells logically and it ensures that all cells that have the same RowKeys are co-located on the same server. RowKey is internally regarded as a byte array.

34.What is the Apache Spark framework?

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Apache Spark is an open-source, distributed processing system used for big data workloads. Apache Spark has become one of the most popular big data distributed processing framework with 365,000 meetup members in 2017.

35. Is Spark and PySpark different?

Ans:

Py Spark	Is Spark
PySpark is the collaboration of Apache Spark and Python.	Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language

36. What is spark tool?

Ans:

Spark tools are the major software features of the spark framework that are used for efficient and scalable data processing for big data analytics. The Spark framework being open-sourced through Apache license. MLlib Spark tool is used for machine learning implementation on the distributed dataset.

37. What do you mean by Apache Web Server?

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Apache web server is the HTTP web server that is open source, and it is used for hosting the website.

38. How to check the Apache version?

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You can use the command httpd -v.

39. Does apache run on which users and how to check the location of the config file?

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Apache runs on the nobody user, and the config file’s location is /etc/httpd/conf/httpd.conf.

40. What is a general architecture for mahout?

Ans:

41. What is the port of HTTP and https of Apache?

Ans:

The port of HTTP is 80, and https is 443 in Apache.

42. How will you install the Apache server on Linux Machine?

Ans:

This is the common Apache Interview Question asked in an interview. We can give the following command for Centos and Debian, respectively:

Centos: yum install httpd

Debian: apt-get install apache2.

43.Where are the configuration directories of the Apache webserver?

Ans:

You can use the following command:

cd /etc/HTTP and type ls -l

44.Can we install two apache web servers on one Single machine?

Ans:

The answer is Yes, we can install the two apache web servers on one machine, but we have to define two different ports on that.

45. Compare Mahout & MLlib

Ans:

Mahout	MLlib
Hadoop & MapReduce	heightened

46. What DocumentRoot refers to in Apache?

Ans:

It means the web file location, which is stored on the server. For Eg: /var/www.

47.What do you mean by the Alias Directive?

Ans:

The alias directive is responsible for mapping resources in the file system.

48. What do you mean by Directory Index?

Ans:

It is the first file that the apache server looks for when any request comes from a domain.

49.What do you mean by the log files of the Apache web server?

Ans:

The Apache log records events that were handled by the Apache web server including requests from other computers, responses sent by Apache, and actions internal to the Apache server.

50. Define an quick guide for mahout?

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51. Which tasks are performed by mahout package?

Ans:

Mahout offers the coder a ready-to-use framework for doing data mining tasks on large volumes of data. Mahout lets applications to analyze large sets of data effectively and in quick time. Includes several MapReduce enabled clustering implementations such as k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift.

52. What is replacing Hadoop?

Ans:

• Apache Spark. Apache Spark is an open-source cluster-computing framework.
• Apache Storm.
• Ceph.
• DataTorrent RTS.
• Disco.
• Google BigQuery.
• High-Performance Computing Cluster (HPCC)

53. Which of the following platforms does Apache Hadoop run on?

Ans:

Hadoop has support for cross-platform operating systems.

54. What kind of job is a mahout?

Ans:

A mahout is an elephant rider, trainer, or keeper. Usually, a mahout starts as a boy in the family profession when he is assigned an elephant early in its life. They remain bonded to each other throughout their lives’.

55. What is the distinction between Apache driver and Apache Spark’s MLlib?

Ans:

Apache driver	Apache Spark
Clustering – takes things in a very explicit category (such as sites or newspaper articles) and organizes them into present teams, specified things happiness to a similar cluster square measure just like one another	If Your cc algorithmic rule mapped to solely|the one} mister job – main distinction is only startup overhead, that is dozens of seconds for Hadoop mister, and let say one second for Spark. Therefore, just in case of model coaching it’s not that necessary.

56. Whenever a client submits a hadoop job, who receives it?

Ans:

NameNode receives the Hadoop job which then looks for the data requested by the client and provides the block information. JobTracker takes care of resource allocation of the hadoop job to ensure timely completion.

57. Explain about the different catalog tables in HBase?

The two important catalog tables in HBase, are ROOT and META. ROOT table tracks where the META table is and META table stores all the regions in the system.

58. Which of the following recommendations is used in Mahout?

Ans:

Mendeley uses Mahout to power Mendeley Suggest, a research article recommendation service. Myrrix is a recommender system product built on Mahout.

59. Which task is performed by mahout package?

Ans:

Mahout offers the coder a ready-to-use framework for doing data mining tasks on large volumes of data. Mahout lets applications analyze large sets of data effectively and in a quick time. Includes several MapReduce enabled clustering implementations such as k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift.

60. What is Map R on Apache Mahout?

Ans:

61. What Hadoop is used for?

Ans:

Apache Hadoop is an open source framework that is used to efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly.

62. Which is better: Spark or Hadoop?

Ans:

Spark has been found to run 100 times faster in-memory, and 10 times faster on disk. It’s also been used to sort 100 TB of data 3 times faster than Hadoop MapReduce on one-tenth of the machines. Spark has particularly been found to be faster on machine learning applications, such as Naive Bayes and k-means.

63. What mahout does Mcq?

Ans:

The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. 3.

64. What is Apache Mahout explain its features and applications?

Ans:

Apache Mahout is a highly scalable machine learning library that enables developers to use optimized algorithms. Mahout implements popular machine learning techniques such as recommendation, classification, and clustering. Therefore, it is prudent to have a brief section on machine learning before we move further.

65. Difference between graphlab and mahout

Ans:

Mahout	Graphlab
Mahout is a framework for machine learning and part of the Apache Foundation.	Graphlab project takes a quite different approach to parallel collaborative filtering (more broadly, machine learning), and is primarily used by academic institutions.
Mahout has inherent Fault-tolerance	Graphlab does not have inherent Fault tolerance
Mahout looks like a more polished product, especially as it relies on Hadoop for scalability and distribution.	Graphlab excells since it is built ground up for iterative algorithms such as those used in collaborative filtering.

66. Is Mahout a part of Hadoop?

Ans:

Mahout’s architecture sits atop the Hadoop platform. Hadoop unburdens the programmer by separating the task of programming MapReduce jobs from the complex bookkeeping needed to manage parallelism across distributed file systems.

67. How many times faster is MLlib vs Apache Mahout?

Ans:

Spark with MLlib proved to be nine times faster than Apache Mahout in a Hadoop disk-based environment

68. Algorithms Supported in Apache Mahout

Ans:

Apache Mahout implements sequential and parallel machine learning algorithms, which can run on MapReduce, Spark, H2O, and Flink*. The current version of Mahout (0.10.0) focuses on recommendation, clustering, and classification tasks.

69. How Installing Apache Mahout

Ans:

Mahout requires Java 7 or above to be installed, and also needs a Hadoop, Spark, H2O, or Flink platform for distributed processing (though it can be run in standalone mode for prototyping). Mahout can be downloaded from http://mahout.apache.org/general/downloads.html and can either be built from source or downloaded in a distribution archive. Download and untar the distribution, then set the environment variable MAHOUT_HOME to be the directory where the distribution is located

70. What is apache spark Apache Mahout?

Ans:

71. What is Running Mahout from Java or Scala

Ans:

In the last example we used Mahout from the command line. It’s also possible to integrate Mahout with your Java applications. Mahout is available via Maven using the group id org.apache.mahout. Add the following dependency to your pom.xml to get started.

• org.apache.mahout.classifier.naivebayes (for a Naive Bayes Classifier)
• org.apache.mahout.classifier.df (for a Decision Forest)
• org.apache.mahout.classifier.sgd (for logistic regression)
• When using sbt (e.g. for a Scala application), add this to your library dependencies:
• libraryDependencies ++= Seq(
• [other libraries]
• “org.apache.mahout” % “mahout-core” % “0.10.0”
• )

72. Define samsara ?

Ans:

Apache Mahout-Samsara refers to a Scala domain specific language (DSL) that allows users to use R-Like syntax as opposed to traditional Scala-like syntax. This allows users to express algorithms concisely and clearly.

73. What is Backend Agnostic?

Ans:

Apache Mahout’s code abstracts the domain specific language from the engine where the code is run. While active development is done with the Apache Spark engine, users are free to implement any engine they choose- H2O and Apache Flink have been implemented in the past and examples exist in the code base.

74. Define the Applications of Mahout?

Ans:

Companies such as Adobe, Facebook, LinkedIn, Foursquare, Twitter, and Yahoo use Mahout internally.

Foursquare helps you in finding out places, food, and entertainment available in a particular area. It uses the recommender engine of Mahout.

Twitter uses Mahout for user interest modelling.

Yahoo! uses Mahout for pattern mining.

75. What is the difference between MapReduce and Hadoop

Ans:

The Apache Hadoop is an ecosystem which provides an environment which is reliable, scalable and ready for distributed computing. MapReduce is a submodule of this project which is a programming model and is used to process huge datasets which sits on HDFS (Hadoop distributed file system).

76. Which of the following does Hadoop produce?

Ans:

Distributed file system”

77. Apache Mahout Developers?

Ans:

Apache Mahout is developed by a community. The project is managed by a group called the “Project Management Committee” (PMC). The current PMC is Andrew Musselman, Andrew Palumbo, Drew Farris, Isabel Drost-Fromm, Jake Mannix, Pat Ferrel, Paritosh Ranjan, Trevor Grant, Robin Anil, Sebastian Schelter, Stevo Slavić.

78. Using Apache Mahout With Apache Spark for Recommendations

Ans:

In the following example, we will use Spark as part of a system to generate recommendations for different restaurants. The recommendations for a potential diner are constructed from this formula:

recommendations_for_user = [V’V] * historical_visits_from_user

Here, V is the matrix of restaurant visits for all users and V’ is the transpose of that matrix. [V’V] can be replaced with a co-occurrence matrix calculated with a log-likelihood ratio, which determines the strength of the similarity of rows and columns (and thus be able to pick out restaurants that other similar users have liked).

79. Example of Multi-Class Classification Using Amazon Elastic MapReduce

Ans:

We can use Mahout to recognize handwritten digits (a multi-class classification problem). In our example, the features (input variables) are pixels of an image, and the target value (output) will be a numeric digit—0, 1, 2, 3, 4, 5, 6, 7, 8, or 9.

Creating a Cluster Using Amazon Elastic MapReduce (EMR) While Amazon Elastic MapReduce is not free to use, it will allow you to set up a Hadoop cluster with minimal effort and expense. To begin, create an Amazon Web Services account and follow these steps:

• Create a Key Pair
• Go to the Amazon AWS Console and log in.
• Select the “EC2” tab.
• Select “Key Pairs” in the left sidebar.

80. What is journal d big data for mahout?

Ans:



81. What is the importance and the role of Apache Hive in Hadoop?

Ans:

Hive allows users to read, write, and manage petabytes of data using SQL. Hive is built on top of Apache Hadoop, which is an open-source framework used to efficiently store and process large datasets. As a result, Hive is closely integrated with Hadoop, and is designed to work quickly on petabytes of data.

82. How does Apache Hive work in Hadoop functionality?

Ans:

How Does Apache Hive Work? In short, Apache Hive translates the input program written in the HiveQL (SQL-like) language to one or more Java MapReduce, Tez, or Spark jobs. Apache Hive then organizes the data into tables for the Hadoop Distributed File System HDFS) and runs the jobs on a cluster to produce an answer.

83. What kind of name is Mahout?

Ans:

A mahout is an elephant rider, trainer, or keeper.

84. What is better than Hadoop?

Ans:

Spark has been found to run 100 times faster in-memory, and 10 times faster on disk. It’s also been used to sort 100 TB of data 3 times faster than Hadoop MapReduce on one-tenth of the machines. Spark has particularly been found to be faster on machine learning applications, such as Naive Bayes and k-means.

85. Why is Spark better than Hadoop?

Ans:

Spark is faster because it uses random access memory (RAM) instead of reading and writing intermediate data to disks. Hadoop stores data on multiple sources and processes it in batches via MapReduce. Cost: Hadoop runs at a lower cost since it relies on any disk storage type for data processing.

86. What is Apache in big data?

Ans:

Apache Hadoop is an open source, Java-based software platform that manages data processing and storage for big data applications. Hadoop works by distributing large data sets and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel.

87. Can Databricks run on Hadoop?

Ans:

Just as Data Engineering Integration users use Hadoop to access data on Hive, they can use Databricks to access data on Delta Lake. Hadoop customers who use NoSQL with HBase on Hadoop can migrate to Azure Cosmos DB, or DynamoDB on AWS, and use Data Engineering Integration connectors to process the data.

88. What is the Hadoop ecosystem?

Ans:

Hadoop Ecosystem is a platform or a suite which provides various services to solve the big data problems. It includes Apache projects and various commercial tools and solutions. There are four major elements of Hadoop i.e. HDFS, MapReduce, YARN, and Hadoop Common. … HDFS: Hadoop Distributed File System

89. In which languages can you code in Hadoop?

Ans:

Hadoop framework is written in Java language, but it is entirely possible for Hadoop programs to be coded in Python or C++ language.

90. What is Hadoop and Mahout in data mining?

Ans:



91. What are Mahout libraries?

Ans:

Apache Mahout is a powerful, scalable machine-learning library that runs on top of Hadoop MapReduce. It is an open source project that is primarily used for creating scalable machine learning algorithms. Maintained by. License Type.

92. How do Mahouts control elephants?

Ans:

Elephants can cause considerable harm or damage if they are spooked or get angry. Mahouts mount elephants by holding on to its ears with his hands and climbing up the trunk with his feet. They sit behind the animals neck. Mahouts take naps on top of mounts, usually taking off their sarongs and using them as a sheet.

93. What is MAP reduce technique?

Ans:

MapReduce is a programming model or pattern within the Hadoop framework that is used to access big data stored in the Hadoop File System (HDFS). … MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers.

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94. What is flume in big data?

Ans:

Flume. Apache Flume. Apache Flume is an open-source, powerful, reliable and flexible system used to collect, aggregate and move large amounts of unstructured data from multiple data sources into HDFS/Hbase (for example) in a distributed fashion via it’s strong coupling with the Hadoop cluster.