This course helps you learn in-depth details of Hadoop and MapReduce along with a hands-on experience of the same. You’ll learn how to set up your own Hadoop cluster and Virtual Linux Instance. All the major features of MapReduce are covered – including advanced topics like Custom DataTypes, Input/Output Formats, and Collaborative Filtering.
This course is a continuation of the Part -1 of Hadoop Course named “Hadoop, MapReduce for Big Data Problems”
By the end of both the parts, you may expect to get an in-depth understanding of Hadoop, MapReduce, and Big Data Problems.
- Develop advanced MapReduce applications to process BigData.
- Master the art of “thinking parallel” – how to break up a task into MapReduce transformations.
- Use Hadoop + MapReduce to solve a wide variety of problems: from NLP to Inverted Indexes.
- Understand HDFS, MapReduce, and YARN and how they interact with each other.
- Understand the basics of performance tuning and managing Hadoop cluster.
Hadoop skills are in demand – this is an undeniable fact! Hence, there is an urgent need for IT professionals to keep themselves in trend with Hadoop and Big Data technologies.
Dice has quoted, “Technology professionals should be volunteering for Big Data projects, which makes them more valuable to their current employer and more marketable to other employers.”
Alice Hill, managing director of Dice, tells Data Informed, that the postings for Hadoop jobs has gone up by 64%, compared to last year. And that Hadoop is the leader in the Big Data category of job postings. According to Dice, Hadoop pros made an average of $108,669 in 2013, which is slightly above the $106,542 average for Big Data jobs.
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