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Writing a Multistep MapReduce Job Using the mrjob Python Library is a video sample excerpt from, Data Just Right LiveLessons Video Training -- 7 Hours of Video Instruction

Overview

Data Just Right LiveLessons provides a practical introduction to solving common data challenges, such as managing massive datasets, visualizing data, building data pipelines and dashboards, and choosing tools for statistical analysis. You will learn how to use many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery.

Data Just Right LiveLessons shows how to address each of today's key Big Data use cases in a cost-effective way by combining technologies in hybrid solutions. You'll find expert approaches to managing massive datasets, visualizing data, building data pipelines and dashboards, choosing tools for statistical analysis, and more. These videos demonstrate techniques using many of today's leading data analysis tools, including Hadoop, Hive, Shark, R, Apache Pig, Mahout, and Google BigQuery.

Data Engineer and former Googler Michael Manoochehri provides viewers with an introduction to implementing practical solutions for common data problems. The course does not assume any previous experience in large scale data analytics technology, and includes detailed, practical examples.

Skill Level

Beginner
What You Will Learn

Mastering the four guiding principles of Big Data success--and avoiding common pitfalls
Emphasizing collaboration and avoiding problems with siloed data
Hosting and sharing multi-terabyte datasets efficiently and economically
"Building for infinity" to support rapid growth
Developing a NoSQL Web app with Redis to collect crowd-sourced data
Running distributed queries over massive datasets with Hadoop and Hive
Building a data dashboard with Google BigQuery
Exploring large datasets with advanced visualization
Implementing efficient pipelines for transforming immense amounts of data
Automating complex processing with Apache Pig and the Cascading Java library
Applying machine learning to classify, recommend, and predict incoming information
Using R to perform statistical analysis on massive datasets
Building highly efficient analytics workflows with Python and Pandas
Establishing sensible purchasing strategies: when to build, buy, or outsource
Previewing emerging trends and convergences in scalable data technologies and the evolving role of the "Data Scientist"
Who Should Take This Course

Professionals who need practical solutions to common data challenges that they can implement with limited resources and time.

Course Requirements

Basic familiarity with SQL
Some experience with a high-level programming language such as Java, JavaScript, Python, R
Experience working in a command line environment
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