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Spark Development Training

Self-paced Videos, Full course with each topic covered in depth, SAP Certification prep, Course Completion Certificate


Uplatz

Summary

Price
£200 inc VAT
Or £66.67/mo. for 3 months... Read more
Study method
Online, self-paced
Duration
30 Hours
Access to content
Lifetime access
Qualification
No formal qualification
Additional info
  • Certificate of completion available and is included in the price

1 student purchased this course

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Overview

Uplatz offers in-depth and extensive training on Spark Development. This is video-based online course (self-paced training).

Did you know?

  • There are almost 4.4 million jobs internationally in the field of Big Data
  • Data specialists are earning 20-30% more than other IT professionals
  • Research from e-skills UK, sponsored by SAS, suggests demand for big data analysts will double over the next five years.
  • The value of Big Data market in 2015 was $59 billion and it is expected to roughly double to $102 billion by 2019

Course media

Resources

  • Course Syllabus - Spark Development - download

Description

Spark Development Course 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

Who is this course for?

Anyone who wants to make a career in Big Data and Data Engineering

Requirements

Passion and determination to achieve your goals!

Career path

  • Data Engineer
  • Spark Developer
  • Data Scientist

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