Spark and Scala Training

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Spark and Scala Training

BD-340

The Apache Spark and Scala training teaches: Scala basics; The fundamentals of Spark architecture, installation, configuration, administration and tools; and Advanced Scala topics.

This course designed for Java software engineers who want a deep dive into Scala and Spark. The course scope may be adjusted (32-48 hours) based on clients need, based on its actual implementation and usage.

Prerequisites

Previous knowledge:
Database management and Java Programming

Environment: 
5 virtual machines per student w/ small configuration (1 CPU, 4GB RAM, 500GB disk)

Method: 
Each day will include a theoretic discussion as well as hands on training. Complementary exercises will be given to participants

Course Topics

Day 1+2: Scala Rampup

Introduction
Scala REPL
Functions and Transformations
Objects and Traits
Option Type
Testing
Collections and functional programming
Pattern Matching
Concurrency
Libraries and RESTful APIs

Day 3+4: Spark for Scala

Scala Collections and Spark API foundations
Spark Setup Locally
Big Data cluster
Resilient Distributed Datasets (RDDs)
Key-Value Pairs and Map/Reduce the Spark way
Functions, Transformations, and Actions
RDD Persistence
Testing Scala Spark programs
Loading and cleaning data
Data summaries and reporting
Introduction to Spark SQL
Introduction to algorithms with MLLib and GraphX
Introduction to BlinkDB, Tachyon, and the rest of Berkeley Stack

Optional Day 5+6: Advanced Scala

When to use Mutable State
Type Parameterization, Co and Contra Variance, Upper and Lower Bounds
Abstract Members in Depth
Implicit Conversions and Parameters
Type-classes / Context Bounds
Implementing Types
Extractors
Going Further with Akka
Modular Programming, Cake and Parfait
Object Equality, the Gory Details
Workflows and Monads
Scala Idioms and Best Practices
Domain Specific Languages
Design Patterns for Scala
Tail Recursion, Trampolines

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