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6-week evening program providing hands-on intro to Hadoop Spark ecosystem of Big Data technologies.
Monday, September 18, 2017 at 07:00 PM    Cost: $2990
NYC Data Science Academy, 500 8th Ave, Ste 905

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This is a 6-week evening program providing a hands-on introduction to the Hadoop & Spark ecosystem of Big Data technologies. The course will cover these key components of Apache Hadoop: HDFS, MapReduce with streaming, Hive, & Spark. Programming will be done in Python. The course will begin with a review of Python concepts needed for our examples. The course format is interactive. Students will need to bring laptops to class. We will do our work on AWS (Amazon Web Services); instructions will be provided ahead of time on how to connect to AWS & obtain an account.

What is Hadoop?

Hadoop is a set of open-source programs running in computer clusters that simplify the handling of large amounts of data. Originally, Hadoop consisted of a distributed file system tuned for large data sets & an implementation of the MapReduce parallelism paradigm, but has expanded in many ways. It now includes database systems, languages for parallelism, libraries for machine learning, its own job scheduler, & much more. Furthermore, MapReduce is no longer the only parallelism framework; Spark is an increasingly popular alternative. In summary, Hadoop is a very popular & rapidly growing set of cluster computing solutions, which is becoming an essential tool for data scientists.


Unit 1 †Introduction: Hadoop, MapReduce, Python

Overview of Big Data & the Hadoop ecosystem
The concept of MapReduce
HDFS †Hadoop Distributed File System
Python for MapReduce

Unit 2 †MapReduce

More Python for MapReduce
Implementing MapReduce with Python streaming

Unit 3 †Hive: A database for Big Data

Hive concepts, Hive query language (HiveQL)
User-defined functions in Python (using streaming)
Accessing Hive from Python

Unit 4 & 5 †Spark

Intro to Spark using PySpark
Basic Spark concepts: RDDs, transformations, actions
PairRDDs & aggregating transformations
Advanced Spark: partitions; shared variables

Unit 6 †Project Week

Case studies/Final projects
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