NYC Open Data: Streaming Python on Hadoop
Our upcoming 12-Week Data Science bootcamp starts on January, 11th, 2016. Apply now to secure a spot in our winter cohort!
In the meantime, come join the NYC Open Data Meetup Group and learn how easily you can use Hadoop for Machine Learning.
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This summer, Sam Kamin (Vice President of Engineering, NYC Data Science Academy) gave a teaser version of NYC Data Science Academy’s five-week Hadoop course. (The current version on offer is a six-week evening program, “Big Data with Hadoop and Spark.”) See below for a class syllabus from the five-week Hadoop course.
First, though, here are the slides from the Meetup:
(Slides can also be accessed here through SlideShare.)
This five-week course is an intensive, hands-on introduction to the Hadoop ecosystem of Big Data technologies. The emphasis in this course is on learning several of the major components of Apache Hadoop – HDFS, MapReduce, Hive, Pig, Streaming – by doing exercises of increasing complexity. Programming will be done in Python. Students are expected to be familiar with using an operating system from the command line; knowledge of Python is helpful; the material in Learn Python the Hard Way is sufficient background knowledge. The course format is mixed lecture/lab. Students will need to bring their own laptops to connect to our server; instructions will be provided ahead of time as to how to install any required software.
Week 1 – Introduction: MapReduce Overview of Big Data and the Hadoop ecosystem the concept of MapReduce
HDFS – Hadoop Distributed File System
MapReduce with Python streaming
Week 2 – More on MapReduce
More on Big Data, the Hadoop ecosystem, and MapReduce.
Mixed case studies and exercises using MR with Python streaming
Week 3 – Hive: A database for Big Data
User-defined functions in the Hive language
User-defined functions in Python (using streaming)
Advanced topic: Hive queries in Python code
Week 4 – Pig: Simplified MapReduce
Pig functions and macros
Week 5 – Project day
The Hadoop ecosystem
Brief intro to Spark
Brief intro to Mahout
Case studies/project ideas