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Spark Development Training

30 Hours
Online Instructor-led Training
USD 1399 (USD 2800)
Save 50% Offer ends on 30-Jun-2024
Spark Development Training course and certification
95 Learners

About this Course

Welcome to Uplatz, the biggest IT & SAP training provider in Europe!
Uplatz is well known for providing instructor-led training and video-based courses on SAP, Oracle, Salesforce, AWS, Big Data, Machine Learning, Python, R, SQL, Google & Microsoft Technologies, and Digital Marketing.
SAP and AWS training courses are currently the most sought-after courses globally. 
An SAP consultant on an average earns a package of $80,000 ($100,000) per annum based on the skills and experience.

To learn this course -

1) Pay the course fees directly through secured payment gateway by clicking "Pay Now" and relax. After this Uplatz team will take over and get the course conducted for you.
2) If you are based in UK or India, you can directly pay to our respective bank accounts. To do this, you just need to send an email to info@uplatz.com and the Uplatz team will respond back with the details.

For any questions, queries, or payment related issues, simply contact us at -

Call: +44 7836 212635
WhatsApp: +44 7836 212635
Email: info@uplatz.com

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

Spark Development Training

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 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

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

    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
Career Path

Prerequisite: SCALA for Spark

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