Table of Contents
Best MapReduce Courses 2021
Best MapReduce Tutorials 2021
Taming Big Data with MapReduce and Hadoop – Hands On!
Analyzing “big data” is a sought-after and very valuable skill – and this course will quickly teach you two fundamental technologies of big data: MapReduce and Hadoop. Have you ever wondered how Google manages to constantly crawl the entire Internet? You will learn these same techniques, using your own Windows system at home.
Learn and master the art of framing data analysis problems as MapReduce problems through more than 10 practical examples, then scale them to run on cloud computing services in this course. You will learn from a former engineer and senior manager at Amazon and IMDb.
Learn the concepts of MapReduce
Quickly run MapReduce jobs using Python and MRJob
Translate complex analysis problems into multi-step MapReduce tasks
Upgrade to Larger Data Sets Using Amazon’s Elastic MapReduce Service
Understand how Hadoop distributes MapReduce on compute clusters
Discover other Hadoop technologies, such as Hive, Pig and Spark
By the end of this course, you’ll be running code that analyzes gigabytes of information – in the cloud – in minutes.
We’re going to have fun along the way. You’ll warm up with some easy examples of using MapReduce to analyze movie rating data and book text. Once you have the basics under your belt, we’ll move on to more complex and interesting tasks. We’ll use a million movie ratings to find movies that look alike, and you might even discover new movies that you might like in the process! We’ll analyze a social graph of superheroes, and find out who is the most “popular” superhero – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You will find the answer.
This course is very practical; you will spend most of your time following up with the instructor as we write, analyze, and run real code together – both on your own system and in the cloud using Amazon’s Elastic MapReduce service. Over 5 hours of video content is included, with over 10 real life examples of increasing complexity that you can create, run and study on your own. Explore them at your own pace, on your own schedule. The course ends with an overview of other Hadoop-based technologies including Hive, Pig, and the very hot Spark framework – with a working example in Spark.
You will learn:
Understand how MapReduce can be used to analyze sets of big data
Write your own MapReduce jobs using Python and MRJob
Run MapReduce Jobs on Hadoop Clusters Using Amazon Elastic MapReduce
Chain map: reduce tasks together to analyze more complex problems
Analyze social media data with MapReduce
Analyze movie rating data using MapReduce and generate movie recommendations with it.
Understand other Hadoop based technologies including Hive, Pig, and Spark
Understand what Hadoop is for and how it works
Learn By Example: Hadoop, MapReduce for Big Data problems
This course is both broad and deep. It covers the individual components of Hadoop in great detail and also gives you a next-level picture of how they interact with each other.
Hands-on training involving Hadoop, MapReduce: This course will get you started with Hadoop early on. You will learn how to set up your own cluster using both VMs and the cloud. All major MapReduce features are covered, including advanced topics like total sort and secondary sort.
The Art of Parallel Thinking: MapReduce has completely changed the way people think about processing big data. Decomposing any problem into parallelizable units is an art. The examples in this course will teach you to “think in parallel”.
What is covered:
Recommend friends on a social networking site: Generate the 10 best friend recommendations using a collaborative filtering algorithm.
Create a reverse index for search engines: Use MapReduce to parallelize the colossal task of creating a reverse index for a search engine.
Generate bigrams from text: generate bigrams and calculate their frequency distribution in a text corpus.
Create your Hadoop cluster:
Install Hadoop in stand-alone, pseudo-distributed and fully distributed modes
Configure a hadoop cluster using Linux virtual machines.
Configure a Hadoop cloud cluster on AWS with Cloudera Manager.
Understand HDFS, MapReduce and YARN and their interaction
Customize your MapReduce tasks:
Chain multiple MR tasks together
Write your own custom partitioner
Total sort: globally sort a large amount of data by sampling the input files
Secondary sorting
Unit tests with MR Unit
Integrate with Python using the Hadoop Streaming API
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, Configure HDFS and YARN to optimize the performance of your cluster.
You will learn:
Develop advanced MapReduce applications to process BigData
Master the art of “parallel thinking” – how to divide a task into Map / Collapse transformations
Autonomously configure their own Hadoop mini-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 reverse clues 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
Hadoop MAPREDUCE in Depth | A Real-Time course on Mapreduce
The Mapreduce framework is the closest to Hadoop in terms of big data processing. It is considered an atomic processing unit in Hadoop and that is why it will never be obsolete.
Knowing only the basics of MapReduce (Mapper, Reducer, etc.) is not at all sufficient to work in a Hadoop Mapreduce business project in real time. These basics are just the tip of the iceberg in Mapreduce programming. Realtime Mapreduce is much more than that. In Live Big Data projects, we need to override many of the default implementations of the Mapreduce framework to make them work as needed.
This course is an answer to the question “What concepts of Hadoop Mapreduce are used in Live Big Data projects and how to implement them in a program?” To answer this, each Mapreduce concept of the course is explained practically via a Mapreduce program.
Each lecture in this course is explained in 2 steps.
Step 1: Explanation of a Hadoop component | Step 2: Practices – How to implement this component in a MapReduce program.
General inclusions and advantages of this course:
Complete Hadoop Mapreduce explained from scratch to real-time implementation.
Each Hadoop concept is supported by a Mapreduce HANDS-ON code.
Advanced Mapreduce concepts that are not even available on the Internet.
For non-Java help, all of Mapreduce’s Java codes are explained line by line so that even a non-technical person can understand.
Mapreduce codes and data sets used in conferences are attached for your convenience.
Includes a “Case Studies” section that are typically asked in Hadoop interviews.
You will learn:
Each concept that falls under the Hadoop Mapreduce framework, from SCRATCH to the LIVE PROJECT implementation.
Learn how to write Mapreduce codes in a real-time work environment.
Understand how each component of Hadoop Mapreduce works with HANDS-ON Practicals.
Replace the default implementation of Java classes in Mapreduce and code it according to our requirements.
ADVANCE level of Mapreduce concepts which are not even available on the Internet.
Real-time Mapreduce case studies requested in Hadoop interviews with its own Mapreduce code running on the cluster.