SPARK Development Instructor-led Training

Uplatz offers comprehensive training on Spark Development. This is instructor-led online course.

• REAL-TIME PROJECTS & ASSIGNMENTS
• RESUME & INTERVIEW PREPARATION SERVICES
• CERTIFICATE & BADGE OF COURSE COMPLETION

Uplatz Certificate of Completion

Digital certificate - Included

• Course Syllabus - Spark Development - download

COURSE DETAILS & CURRICULUM

Scala Basics
 What is Scala?
 Why Scala for Spark?
 Intro to Scala REPL : Journey from Java to Scala
 Installing Scala IDE
 Basic Operations
 Defining Functions

Scala Essentials
 Control Structures in Scala
 loops – ForEach, While, Do-While
 Collections – Array, ArrayBuffer, Map, Tuples, Lists
 If Statements
 Conditional Operators
 Enumerations

OOP's and FP
 Class and Object Basics
 Scala Constructors
 Nested Classes
 Visibility Rules
 Overriding Methods
 Functional Programming
 Higher Order Functions
 Traits
 Interfaces
 Layered Traits

Prerequisite: BigData and Hadoop Framework

 Introduction to BigData
 Challenges with Bigdata
 Batch Vs. Realtime processing
 Overview- Hadoop Ecosystem
 HDFS
 Review of MapReduce
 Hive
 Sqoop
 Flume

APACHE SPARK

Introduction to Spark
 What is Spark?
 Spark Overview
 Setting up environment
 Using Spark Shell
 Spark Web UI

Spark Basics
 RDD's
 Spark Context
 Spark Ecosystem
 In-Memory data – Spark

Working with RDD's
 Creating, Loading and Saving RDD
 Transformations in RDD
 Actions in RDD
 Key-Value Pair RDD
 MapReduce and Pair RDD operations
 RDD Partitions

Writing and Deploying Spark Applications
 Spark Applications vs. Spark Shell
 Creating Spark Context
 Building a Spark Application
 Running a Spark Application
 Spark and Hadoop Integration-HDFS
 Handling Sequence Files

Spark RDD
 RDD Lineage
 RDD Persistence Overview
 Distributed Persistence

Spark Streaming
 Spark Streaming Architecture
 First Spark Streaming Programming
 Transformations in Spark Streaming

Spark MLlib
1. What is Machine Learning?
2. ML library for Spark
3. Algorithms
 Statistics
 Classification
 Regression
 Clustering
 Collaborative Filtering

Spark SQL
 Overview on Hive
 Spark SQL Architecture
 SQLContext in Spark SQL
 Working with DataFrames
 Example for Spark SQL
 Integrating Hive and Spark SQL
 DataFrames and RDD's
 Knowing JSON and Parquet File Formats
 Loading of data
 Comparing Spark SQL,Impala and Hive-on-Spark

GraphX
 Overview of GraphX
 Data Visualisation in Spark
 Common Spark use-cases

Performance Tuning
 Shared Variables: Broadcast Variables
 Shared Variables: Accumulators
 Common Performance Issues
 Performance tuning tips

Course Deliverables
 Workshop style coaching
 Interactive approach
 Course material
 POC Implementation
 Hands on practice exercises for each topic
 Quiz at the end of each major topic
 Tips and techniques on Cloudera Certification Examination
 Linux concepts and basic commands
 On Demand Services
 Mock interviews for each individual will be conducted on need basis
 SQL basics on need basis
 Resume preparation and guidance
 Interview questions

Passion and determination to achieve your goals!

Data Architect

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