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    HADOOP 2022 - Hadoop, MapReduce for Big Data problems | Simpliv

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    Website https://www.simpliv.com/hadoop/learn-by-example-hadoop-mapreduce-for-big-data-problems | Want to Edit it Edit Freely

    Category Hadoop

    Deadline: December 30, 2022 | Date: December 30, 2022

    Venue/Country: Online Courses, U.S.A

    Updated: 2018-05-07 16:16:46 (GMT+9)

    Call For Papers - CFP

    Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.

    This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.

    Let’s parse that.

    Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.

    Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You'll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered - including advanced topics like Total Sort and Secondary Sort.

    The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to "think parallel".

    What's Covered: Lot's of cool stuff ..

    Using MapReduce to:

    Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.

    Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.

    Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.

    Build your Hadoop cluster:

    Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes

    Set up a hadoop cluster using Linux VMs.

    Set up a cloud Hadoop cluster on AWS with Cloudera Manager.

    Understand HDFS, MapReduce and YARN and their interaction

    Customize your MapReduce Jobs:

    Chain multiple MR jobs together

    Write your own Customized Partitioner

    Total Sort : Globally sort a large amount of data by sampling input files

    Secondary sorting

    Unit tests with MR Unit

    Integrate with Python using the Hadoop Streaming API

    .. and of course all the basics:

    MapReduce : Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort

    HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster.

    Using discussion forums

    Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(

    We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.

    The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.

    We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.

    It is a hard trade-off.

    Thank you for your patience and understanding!

    Who is the target audience?

    Yep! Analysts who want to leverage the power of HDFS where traditional databases don't cut it anymore

    Yep! Engineers who want to develop complex distributed computing applications to process lot's of data

    Yep! Data Scientists who want to add MapReduce to their bag of tricks for processing data

    Basic knowledge

    You'll need an IDE where you can write Java code or open the source code that's shared. IntelliJ and Eclipse are both great options.

    You'll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc

    A bit of exposure to Linux/Unix shells would be helpful, but it won't be a blocker

    What you will learn

    Develop advanced MapReduce applications to process BigData

    Master the art of "thinking parallel" - how to break up a task into Map/Reduce transformations

    Self-sufficiently set up their own mini-Hadoop cluster whether it's a single node, a physical cluster or in the cloud.

    Use Hadoop + MapReduce to solve a wide variety of problems : from NLP to Inverted Indices to Recommendations

    Understand HDFS, MapReduce and YARN and how they interact with each other

    Understand the basics of performance tuning and managing your own cluster

    Gmail: supportatsimpliv.com

    Phone no: 5108496155

    Click to Continue Reading: https://www.simpliv.com/search

    Registration Link: https://www.simpliv.com/hadoop/learn-by-example-hadoop-mapreduce-for-big-data-problems

    Simpliv Youtube Course & Tutorial : https://www.youtube.com/channel/UCZZevQcSlAK689KbsrMvEog?view_as=subscriber

    Facebook Page: https://www.facebook.com/simplivllc

    Linkedin: https://www.linkedin.com/company/simpliv

    Twitter: https://twitter.com/simplivllc


    Keywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
    Disclaimer: ourGlocal is an open academical resource system, which anyone can edit or update. Usually, journal information updated by us, journal managers or others. So the information is old or wrong now. Specially, impact factor is changing every year. Even it was correct when updated, it may have been changed now. So please go to Thomson Reuters to confirm latest value about Journal impact factor.