This document discusses Apache Spark, an open-source cluster computing framework, and provides an overview of key Spark concepts including Resilient Distributed Datasets (RDDs), transformations vs actions, and programming models compared to MapReduce. It recommends learning Spark through code examples and provides a link to develop Spark apps on Windows.

1. Learning Apache Spark
by Examples
SAMUEL YEE

2. Why Apache Spark?

3. MapReduce vs Spark
PIG HIVE MAHOUT
(machine
learning)
MapReduce

4. Programming Models
MAPREDUCE – 50 LINES OF CODE SPARK – 1 LINE OF CODE

5. Resilient Distributed Dataset (RDD)
Immutable (read-only), fault-tolerant, distributed collection of records that can be operated on
in parallel across different nodes.
RDD is created by loading data from storage or as results of other RDDs.
RDD can be processed either in-disk or in-memory and persists finally on disk.
Verbs can be categorized into Transformations and Actions.

6. Transformations vs Actions
Transformations: define new RDDs based on current ones e.g. map, filter, join, union etc.
Actions: return a value (e.g. reduce, count, first etc)

7. Data Transformations and Actions
Input
Data on
Disk
RDD 1
T1
RDD 2
T2
T3
RDD X
RDD 3
Output
Result on
DiskA1

8. Demo
Learn from code samples and examples provided by Spark.
Develop Apache Spark Apps on Windows platform
◦ http://tinyurl.com/nf9bwey