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Course Content Covered in Hadoop Training Course 100%

Hbase positioning Hbase Introduction   What it is, what it is not, its history and common use-cases Hbase Client – Shell, exercise Hbase Architecture     Building Components Storage, B+ tree, Log Structured Merge Trees Region Lifecycle Read/Write Path Hbase Schema Design     Introduction to hbase schema Column Family, Rows, Cells, Cell timestamp Deletes Exercise - build a schema, load data, query data Hbase Java API – Exercises   Connection CRUD API      Scan API Filters Counters Hbase MapReduce Hbase Bulk load Hbase Operations, cluster management     Performance Tuning Advanced Features Exercise Recap and Q&A MapReduce for Developers Introduction   Traditional Systems / Why Big Data / Why Hadoop Hadoop Basic Concepts/Fundamentals Hadoop in the Enterprise   Where Hadoop Fits in the Enterprise Review Use Cases Architecture   Hadoop Architecture &

https://www.pdf-archive.com/2017/06/07/course-content-covered-in-hadoop-training-course/

07/06/2017 www.pdf-archive.com

MapReduce Training 97%

➢ Hadoop Ecosystem ➢ Hadoop Installation ➢ How To Develop Mapreduce Application Visit For More Info ::

https://www.pdf-archive.com/2018/04/12/mapreduce-training/

12/04/2018 www.pdf-archive.com

Hadoop vs. Apache Spark Three Facts You Ought To Know 90%

It’s a large-scale data processing engine which will most possibly replace Hadoop’s MapReduce.

https://www.pdf-archive.com/2017/02/08/hadoop-vs-apache-spark-three-facts-you-ought-to-know/

08/02/2017 www.pdf-archive.com

ESN - Big data Hadoop 87%

MapReduce: ...   Not as fast as MapReduce.

https://www.pdf-archive.com/2016/08/09/esn-big-data-hadoop/

09/08/2016 www.pdf-archive.com

IJEAS0405015 86%

The primary focus of the paper is to prepare web based analysis system which will depict trends based on the users browsing mode using Hadoop MapReduce.

https://www.pdf-archive.com/2017/09/10/ijeas0405015/

10/09/2017 www.pdf-archive.com

hadoop 84%

› How it is related to MapReduce split size #204, 2nd Floor, Annapurna Block, Aditya Enclave, Ameerpet, Hyderabad.

https://www.pdf-archive.com/2016/07/05/hadoop/

05/07/2016 www.pdf-archive.com

RedactedResume 76%

Spark MapReduce    November 2014 Created a script that takes in an input location and a maximum number of steps and simulates a cat and mouse chase.

https://www.pdf-archive.com/2016/01/06/redactedresume/

06/01/2016 www.pdf-archive.com

Apache Hadoop 2 76%

Hadoop stores data in Hadoop Distributed File System the processing of these data is done using MapReduce.

https://www.pdf-archive.com/2017/07/15/apache-hadoop-2/

15/07/2017 www.pdf-archive.com

IJETR2137 74%

With its unlimited scale and on-demand access to compute and storage capacity, cloud computing is the perfect match for big data processing.[12] Hadoop is an open-source Cloud computing environment that implements the Google MapReduce framework in Java.

https://www.pdf-archive.com/2017/09/09/ijetr2137/

09/09/2017 www.pdf-archive.com

x13401792 73%

Data Mining Data mining is the act of searching for patterns within a dataset, MapReduce will be used to draw patterns from different periods of history to find if there has been certain characteristics throughout history that has caused police officers to die in certain states.

https://www.pdf-archive.com/2017/02/16/x13401792/

16/02/2017 www.pdf-archive.com

PhD in Big data Cloud computing Hadoop, HIVE ,Techniques 72%

Top​ ​five​ ​breakthrough​ ​technologies​ ​on​ ​PhD​ ​in​ ​Big data/Cloud​ ​computing:​ ​Hadoop,​ ​HIVE​ ​&​ ​Mapreduce techniques The​ ​exponential​ ​surge​ ​in​ ​digitalization​ ​has​ ​influenced​ ​our​ ​lives​ ​to​ ​a​ ​great​ ​extent compared​ ​to​ ​a​ ​decade​ ​ago.​ ​It​ ​has​ ​generated​ ​a​ ​huge​ ​amount​ ​of​ ​Data.​ ​‘Big​ ​Data’.

https://www.pdf-archive.com/2017/11/29/phd-in-big-data-cloud-computing-hadoop-hive-techniques/

29/11/2017 www.pdf-archive.com

Resume - Prasanna Pai Updated 72%

Spring, Hibernate, AWS, Ansible, Docker, Apache SOLR, ElasticSearch, REST APIs, RabbitMQ, React.js, Node.js, Angular.js, Apache Spark, D3.js, Apache Pig, Apache Hive, MLlib, HBase, Cassandra, J2EE, Git, Jenkins, JUnit 4, TestNG, Selenium WebDriver, Redis, MongoDB, Apache Hadoop, MapReduce, PostgreSQL, SPLUNK, Oracle 10g/11g, SQL Developer, Apache Tomcat, Linux OS, Pandas, NumPy, Scikit-learn, Android Studio, MySQL, HTML5 WORK EXPERIENCE Software Engineer at Clean Power Finance (Spruce Finance) December 2016 – February 2017 SpruceFlow API/Platform - Spring, Hibernate, Java, PHP, React.js, Ansible, Docker, Redis, MongoDB, AWS, Git  Core team member in SpruceFlow platform team.

https://www.pdf-archive.com/2017/02/20/resume-prasanna-pai-updated/

20/02/2017 www.pdf-archive.com

SettingupHDFSclusteron3nodes 67%

<name>yarn.nodemanager.auxservices.mapreduce.shuffle.class</name>

https://www.pdf-archive.com/2017/06/10/settinguphdfsclusteron3nodes/

10/06/2017 www.pdf-archive.com

Coursera mmds 2014 60%

Mining Massive Datasets Topics include MapReduce, Web-link analysis, Data-streams, Locality-sensitive hashing, Computational advertising, Clustering, Recommender systems, Analysis of large graphs, Decision trees, Dimensionality reduction, Support-vector machines, and Frequent-itemset analysis.

https://www.pdf-archive.com/2015/03/29/coursera-mmds-2014/

29/03/2015 www.pdf-archive.com

Hadoop Online Training 60%

Hive Components www.MonsterCourses.com +1(772) 777-1557 info@monstercourses.com • • • • • www.MonsterCourses.com Complex Types Abilities of HIVE Query Language Configuring Hive Partitions and Buckets Hive Data Models Hbase (database technique and loading the data) • • • • • • Problems in the real world Traditional RDBMS fallacies The advent of HBase HBase Architecture Hands-on creation, updation of HBase table on shell Multiple ways of loading data into HBase (Shell, Jvm-Client, MapReduce, Avro, Thrift, REST Api) Zookeeper (Highly available Management) • • • • • • • • What Is A ZooKeeper?

https://www.pdf-archive.com/2015/06/04/hadoop-online-training/

04/06/2015 www.pdf-archive.com

Hadoop Training In Hyderabad ppt 60%

http://www.orienit.com Learning Objectives  Candidates will acquire huge subject knowledge after the       course completion Acquiring knowledge in Big Data and Hadoop ecosystem Working with Hadoop Distributed File System (HDFS) Knowledge in MapReduce Programs and implementation with HBase Skills in writing Hive and Pig Scripts Acquires knowledge in Machine learning and predictive modelling Real Time Industry Experts http://www.orienit.com

https://www.pdf-archive.com/2016/07/27/hadoop-training-in-hyderabad-ppt/

27/07/2016 www.pdf-archive.com

learnapachesparkonline-161026124841 60%

In fact, beyond the buzz, Apache Spark has seen extraordinary implementation and has been noticeable out as the successor to Hadoop MapReduce.

https://www.pdf-archive.com/2016/11/03/learnapachesparkonline-161026124841/

03/11/2016 www.pdf-archive.com

Development Environment Of Hadoop MapReduce 60%

Development Environment Of Hadoop MapReduce :

https://www.pdf-archive.com/2017/04/04/development-environment-of-hadoop-mapreduce/

04/04/2017 www.pdf-archive.com

CV 60%

C++, BigData, MapReduce, Machine Learning 01/15 - 10/16 CTO &

https://www.pdf-archive.com/2018/02/25/cv/

25/02/2018 www.pdf-archive.com

Resume 57%

  Travon M. Speller  EDUCATION  Howard University, ​Washington, DC — ​B.S. in Systems and  Computer Science - May 2017  -  GPA:  3.26/4 (Major)    EXPERIENCE  Howard University, ​Washington, DC — ​Undergraduate T.A.  August 2016 - Present  Teaching assistant, Computer Science, responsible for providing  supplemental material to a class of 30+ students four hours weekly. Also  assist Dr. Keeling with weekly labs/lectures.  WatchMeCode, ​Washington, DC​ ​— ​Instructor  (Voluntary)  August 2016 - Present  Mentor local high school students interested in computer science. Teach  how to code using JavaScript and Python and basic computer science  concepts.   Howard University, ​Washington, DC​ ​— ​Bootcamper (Tutor)  August 2016 - Present  Assist students with computer science concepts and algorithms for six  hours weekly. Reiterate concepts students learned in class and provide  SKILLS  Programming languages:  3+​ years of experience with  Java, C/C++, Python, Bash,  HTML, and CSS  2+​ years of experience with  C#, MIPS, JavaScript, and  AngularJS  1​ year of experience with  LISP, Prolog, and SNOBOL4    Technologies/Environments:   4+ ​years of experience with  Microsoft Office, Unix, and  Git  3+​ years of experience with  Visual Studio, Eclipse, and  Virtualbox  2+​ years of experience with  Xamarin Studio, Lingo, and  Tableau  interview prep.  1​ year of experience with  Hadoop and Mapreduce  Google, ​Mountain View, California — ​Edge Participant  AWARDS  January 2014 - August 2014  One of fifteen students selected from across the country in this  pre-professional career and skill development program. Edge reinforced CS  fundamentals, beginner C/C++, and Python skills. Received mentorship  from Google engineers. Participated in two two-week-long campus visits.  Collaborated with participants to build and present web-apps.  PROJECTS  Quail ​— #​HUHacks hackathon project  Quail randomly makes topics to chat about based on recent activities and  pulls random users in a group chat.  The chats are automatically deleted  after 24 hours.  I worked on the UI and used C# to make it multi platform.  goDJ ​— Senior​ Project  Senior project created to make it easier for DJ’s and party promoters to  connect and was made with Node.Js and HTML.  My job was to set up a  MySQL database and link it to the webapp.      533 Gresham Place NW  Washington, DC 20001   (757) 676-2961  travonspeller@gmail.com  www.travonspeller.com    National Society of  Collegiate Scholars  Member – 2014 - Current    HU Legacy Scholarship -  2013 – Current    Access Scholarship - 2013  - Current    1​st​ Place at #HUHacks  Hackathon at Howard  University - 2016    1​st​ Place at CodeCon Hosted  by Bloomberg at Howard  University - 2015    Top 20 in Microsoft Codess  Hunt - 2015    Club Optimist Leadership  Award – 2013 

https://www.pdf-archive.com/2017/09/23/resume/

23/09/2017 www.pdf-archive.com

anoynmous-resume 57%

Predix-Enabled Toy Train ● ● ● ● Worked with four other MSU Computer Science seniors to designed a Bachmann N-scale toy train-set to demonstrate the power of GE’s Predix Used microcontrollers and sensors to locate and control all trains on the train-set Implemented a website to visualize and interact with the data collected Produced a video about the delivered product Voice Analyzer ● ● ● Applied machine learning and data techniques to classify gender, age, and dialect of a human sound recording Collected and processed around 15,000 sound clips of human voice by using python (librosa, numpy, pandas, matplotlib) Dealt with missing and vague data and used sampling techniques to get better data distribution LANGUAGES Python, C/C++, Matlab, Java, Javascript, SQl, NoSQL, Hive, Pig, HTML, CSS TOOLS Linux/Unix, Hadoop (MapReduce), Spark, Frameworks (Django, Cakephp, Angularjs, React/Redux), jQuery, Twitter Bootstrap, AWS, Docker, Vim, Version Control (git, svn) CLUBS MSU Data Science, MSU ACM

https://www.pdf-archive.com/2017/09/26/anoynmous-resume/

26/09/2017 www.pdf-archive.com

Resume 57%

RECREATION | WEB DEVELOPER SKILLS Autumn 2015 • Used HTML, CSS, JavaScript, and Adobe Dreamweaver for the Geoplateau project (educates about local parks and DIY conservation projects) • Part of the Lexus Eco Challenge 2016 SOFTWARE TECHVENTURE KIDS | INSTRUCTOR Proficient Java • SQL Server • Ruby LATEX• SML • Racket Intermediate R • Azure • Python Git • MapReduce • HTML Familiar Hadoop • Spark • JDBC JS • NoSQL • Shell Willing to learn any necessary technologies and languages Autumn 2013 - Summer 2016 • Taught K-12 students basic programming and computer science principles in after-school clubs and summer camp programs • Volunteered for the first year RESEARCH TASKAR CENTER FOR ACCESSIBLE TECHNOLOGY | RESEARCH ASSISTANT Autumn 2016 - Winter 2017 • Worked with Dr.

https://www.pdf-archive.com/2017/09/03/resume/

03/09/2017 www.pdf-archive.com

Resume 57%

RECREATION | WEB DEVELOPER SKILLS Autumn 2015 • Used HTML, CSS, JavaScript, and Adobe Dreamweaver for the Geoplateau project (educates about local parks and DIY conservation projects) • Part of the Lexus Eco Challenge 2016 SOFTWARE TECHVENTURE KIDS | INSTRUCTOR Proficient Java • SQL Server • Ruby LATEX• SML • Racket Intermediate R • Azure • Python Git • CSS • HTML Familiar C • C++ • MapReduce Hadoop • Spark • AWS JSON • JDBC • Shell Willing to learn any necessary technologies and languages Autumn 2013 - Summer 2016 • Taught K-12 students basic programming and computer science principles in after-school clubs and summer camp programs • Volunteered for the first year RESEARCH TASKAR CENTER FOR ACCESSIBLE TECHNOLOGY | RESEARCH ASSISTANT Autumn 2016 - Winter 2017 • Worked with Dr.

https://www.pdf-archive.com/2017/07/22/resume/

22/07/2017 www.pdf-archive.com

Resume 57%

RECREATION | WEB DEVELOPER SKILLS Autumn 2015 • Used HTML, CSS, JavaScript, and Adobe Dreamweaver for the Geoplateau project (educates about local parks and DIY conservation projects) • Part of the Lexus Eco Challenge 2016 SOFTWARE TECHVENTURE KIDS | INSTRUCTOR Proficient Java • SQL Server • Ruby LATEX• SML • Racket Intermediate R • Azure • Python Git • CSS • HTML Familiar C • C++ • MapReduce Hadoop • Spark • AWS JSON • JDBC • Shell Willing to learn any necessary technologies and languages Autumn 2013 - Summer 2016 • Taught K-12 students basic programming and computer science principles in after-school clubs and summer camp programs • Volunteered for the first year RESEARCH TASKAR CENTER FOR ACCESSIBLE TECHNOLOGY | RESEARCH ASSISTANT Autumn 2016 - Winter 2017 • Worked with Dr.

https://www.pdf-archive.com/2017/08/23/resume/

23/08/2017 www.pdf-archive.com