A hands-on workout in Hadoop, MapReduce and the art of thinking “parallel”.
We have given a detailed review of this course below for our readers but just wanted to highlight some points:
- 4,320+ students enrolled
- 358 Review available
- 13.5 Hours on-demand videos
- 1 Articles
- 112 Supplemental Resource
- Access on Mobile and TV
- Certificate of Completion
- Lifetime Access
- 30 days Money back guarantee
Most attractive feature is 30 days Money Back Guarantee means there is no risk. If you didn’t like this online course, you can refund your money back within next 30 days.
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
Online Course Description:
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”.
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.