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Master Spark ecosystem and Scala. Enroll now to learn from a Spark and Scala expert.

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Course Duration

24 Hrs.

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2 Project

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Big Data & Hadoop

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  • Croma Campus' Apache Spark training course has been thoroughly reviewed by industry professionals to ensure that it meets industry standards. This article will show you how to use Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, and Spark MLlib.
  • Things you will learn:
    • You'll gain a thorough understanding of key Apache Spark principles.

      Interact with and learn from your instructor as well as your classmates.

      How to build Spark apps using Scala programming.

      how to increase application performance and enable high-speed processing using Spark RDDs

      You'll learn how to customise Spark using Scala in this course.

  • The Apache Spark certification training course will teach you all you need to know. It provides a basic Spark vs. Hadoop comparison.

Apache Spark and Scala Training

About-Us-Course

  • Whether you're a beginner or a seasoned veteran, our Apache Spark training course will help you grasp all of the principles and put them into practise at work. Let's talk about the course goals:
    • Be familiar with Big Data, its components and frameworks, as well as Hadoop Cluster design and modes.

      Know how to programme in Scala, how to implement it, and how to use the core Apache Spark constructs.

      Gain an understanding of Apache Spark concepts and learn how to build Spark apps.

      Understand the Apache Spark framework's concepts and deployment procedures.

      Learn how to use the Spark Internals RDD, as well as the Spark API and Scala functions, to create and modify RDDs.

      Be an expert in SparkSQL, Spark Context, Spark Streaming, MLlib, and GraphX, as well as RDD and other Combiners.

  • The company where you work will also determine your income. Pay for businesses such as Cognizant, Accenture, and Infosys is normally good, but you may make a lot of money working for Amazon, Microsoft, or Yahoo.
    • A Spark developer at the beginning level can expect to earn between Rs 6,00,000 and Rs 10,00,000 per year.

      A skilled developer might make anywhere between Rs 25,00,000 and Rs 40,00,000.

      A Data Engineer with Apache Spark expertise may expect to earn more than Rs 10,00,000 per year on average.

      You will receive a greater wage package after completing your Apache Spark certification course.

      A Data Scientist with Apache Spark skills in Hyderabad, on the other hand, may earn more than Rs 8,00,000 per year on average.

  • If you want to break into the big data industry and succeed, Apache Spark is the way to go, as it offers a wide range of options for big data analysis. It is the most popular Big Data technology because its different approaches are effective against a variety of data difficulties.
    • Because Spark can operate on Hadoop MapReduce, YARN, and HDFS, it outperforms Hadoop.

      Because of its great Hadoop compatibility, companies are looking for a big number of Spark Developers.

      Many companies are turning to Spark as a complementary big data platform since it processes data much quicker than Hadoop.

      As technology progresses and new businesses turn to big data management to fulfil their needs, a plethora of new options emerge.

  • That’s the reason, there’s a huge demand for Apache Spark training courses among students.

  • Today, there are opportunities all around the world, including in India, resulting in a growth in professional opportunities for skilled persons.
    • Companies all across the world are using Spark as their primary big data processing platform.

      You'll have the opportunity to work in a range of fields, including retail, software, media and entertainment, consulting, healthcare, and more.

      To gain a competitive advantage, every industry is employing big data analytics and machine learning techniques.

  • You will be required to undertake a variety of work duties and responsibilities after completing your Apache Spark training course.
    • Ability to define problems, collect data, establish facts, and draw valid conclusions using software code.

      Using Spark, produce ready-to-use data by cleaning, processing, and analysing raw data from different mediation sources.

      To guarantee that joins are executed quickly, refactoring code is utilised.

      Assist with the Spark platform's technical architecture.

      Use partitioning strategies to meet certain use situations.

      Hold deep-dive working sessions to fix Spark platform issues fast.

  • Top Hiring Industries in Apache Spark:
    • Google, Cognizant Technology Solutions, TCS, IBM, Accenture, and other well-known employers.

      In addition, we have a team of experts who can assist you with resume writing and interview preparation.

      You will have the opportunity to participate in interviews and be hired in a variety of industries.

      There are several work opportunities available all over the world.

  • Training Certificate:
    • With our certification, you may be able to work from anywhere.

      In today's tech-driven environment, you'll be more valuable.

      Our certification could help you outperform the competition.

      Make a name for yourself as a sought-after expert.

      Obtain a sizable remuneration package.

Why Should You Learn Apache Spark Training?

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Plenary for Apache Spark and Scala Training

TrackWeek DaysWeekendsFast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training ModeClassroom/OnlineClassroom/OnlineClassroom/Online
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Apache Spark and Scala Training Upcoming Batches

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CURRICULUM & PROJECTS

Apache Spark and Scala Training

  • In this program you will learn:
    • Apache HBASE Ecosystem

      Troubleshooting in Hbase

      Troubleshooting in Hbase

      Troubleshooting in Hbase

      Administration in HBASE

      No SQL Databases Hbase

      Hadoop Overview

      Advanced Concepts in PIG

      Functions in PIG

      Data Models, Operators, and Streaming in PIG

      PIG Architecture

      Introduction

      Kafka Integration with Flume, Talend and Cassandra

      Career Guidance and Roadmap

      Kafka Integration with Hadoop, Storm, and Spark

      Kafka Stream Processing

      Kafka Monitoring & Kafka Connect

      Cluster Architecture and Administering Kafka

      Kafka Internals

      Kafka Consumers

      Kafka Producer

      Overview

      Graph X

      Spark Streaming

      Machine Learning with Spark ML

      Spark SQL, Data Frames, and Data Sets

      Running Spark on a cluster

      Using Resilient Distributed Datasets

      Scala

      Introduction

  • This Spark certification training helps you master the essential skills of the Apache Spark open-source framework and Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark.
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  • Introduction
    • Apache Spark Overview

      What’s new in Apache Spark 3

      Installing IntelliJ and Scala

      Introduction to IntelliJ and Scala

      Hadoop MapReduce Overview

      Big Data Overview

      Apache Hadoop Overview

      Hadoop Distibution File System

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  • Scala
    • Flow control in Scala

      Functions in Scala

      Data Structures in Scala

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  • Using Resilient Distributed Datasets
    • Check Your Results and Implementation Against Min

      Filtering RDD's and the Minimun Temperature by Location Example

      Key/value RDD's and the Average Friends by Age example

      The Resilient Distributed Dataset

      Ratings Histogram Example

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  • Spark SQL, Data Frames, and Data Sets
    • Item-Based Collaborative Filtering in Spark, cache (), and persist ()

      What are Data Sets

      What are Data Frames

      Introduction to Spark SQL

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  • Running Spark on a cluster
    • Partitioning Concepts

      Troubleshooting and managing dependencies

      Introducing Amazing Elastic MapReduce

      Cluster management in Hadoop

      What is a Cluster

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  • Machine Learning with Spark ML
    • Linear Regression with MLLib

      Using MLLib

      Introduction MLLib

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  • Spark Streaming
    • The DStream API for Spark Streaming

      Spark Streaming

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  • Graph X
    • Using Pregel API with Spark API

      Breadth-First-Search with Pregel

      About Pregel

      What is Graph X

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  • Overview
    • Configuring Single Node Single Broker Cluster

      Type of Kafka Clusters

      Kafka Cluster

      Kafka Installation

      Zookeeper

      Where is Kafka Used

      Kafka Components

      Kafka Architecture

      Kafka Concepts

      Kafka Features

      What is Kafka

      Need for Kafka

      Big Data Analytics

      Introduction to Big Data

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  • Kafka Producer
    • Partitions

      Serializing Using Apache Avro

      Serializers

      Configuring Producers

      Sending a Message Synchronously & Asynchronously

      Producing Keyed and Non-Keyed Messages

      Sending a Message to Kafka

      Configuring Single Node Multi Broker Cluster

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  • Kafka Consumers
    • De-serializers

      Rebalance Listeners

      Consuming Records With Specific Offsets

      Commits and Offsets

      Configuring Consumers

      The Poll Loop

      Subscribing to Topics

      Creating a Kafka Consumer

      Consumer Groups and Partition Rebalance

      Standalone Consumer

      Consumers and Consumer Groups

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  • Kafka Internals
    • Performance Tuning in Kafka

      Validating System Reliability

      Using Consumers in a Reliable System

      Reliability

      Broker Configuration

      Using Producers in a Reliable System

      Physical Storage

      Request Processing

      Replication

      The Controller

      Cluster Membership

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  • Cluster Architecture and Administering Kafka
    • Unsafe Operations

      Consuming and Producing

      Partition Management

      Topic Operations

      Consumer Groups

      Dynamic Configuration Changes

      Other Cross-Cluster Mirroring Solutions

      Apache Kafka’s Mirror Maker

      Multi-Cluster Architectures

      Use Cases - Cross-Cluster Mirroring

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  • Kafka Monitoring & Kafka Connect
    • When to Use Kafka Connect

      Kafka Connect Properties

      Kafka Connect

      Kafka Broker Metrics

      Client Monitoring

      Lag Monitoring

      End-to-End Monitoring

      Metric Basics

      Considerations When Building Data Pipelines

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  • Kafka Stream Processing
    • Kafka Streams: Architecture Overview

      Kafka Streams by Example

      Stream Processing

      Stream-Processing Concepts

      Stream-Processing Design Patterns

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  • Kafka Integration with Hadoop, Storm, and Spark
    • Kafka Integration with Spark

      Spark Configuration

      Integration of Kafka with Storm

      Apache Spark Basics

      Configuration of Storm

      Apache Storm Basics

      Kafka Integration with Hadoop

      Hadoop Configuration

      Apache Hadoop Basics

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  • Kafka Integration with Flume, Talend and Cassandra
    • Talend Basics

      Integration of Kafka with Talend

      Integration of Kafka with Cassandra

      Cassandra Basics Such as Key Space and Table Creation

      Integration of Kafka with Flume

      Flume Basics

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  • Career Guidance and Roadmap
    • When to avoid PIG

      When PIG suits the most

      Why you need PIG

      Significance od PIG

      PIG over MapReduce

      History of Apache PIG

      Exploring use cases for PIG

      Introduction to PIG

      Prerequisites for Apache PIG

      Hadoop MapReduce Overview

      Hadoop Distribution File System

      Apache Hadoop Overview

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  • PIG Architecture
    • Utility Commands

      Running Pig Scripts

      PIG Latin Statements

      GRUNT Shell

      PIG Architecture

      Running PIG in Different Modes

      PIG Latin Language

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  • Data Models, Operators, and Streaming in PIG
    • Diagnostic Operators

      Combining and Splitting Operators

      Sorting Operator

      Grouping and Joining Operators-

      Filtering Operators- Pig Streaming with Python

      Filtering Operators

      Relation Operators

      Loading and Stroing Operators

      Type Construction Operators

      Cast Operators

      Comparison Operators

      Arithmetic Operators

      PIG Data Model - Complex Data Type

      PIG Data Model - Scarlar Data Type

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  • Functions in PIG
    • Math Function

      String Functions

      Tuple and Bag Functions

      Eval Functions

      Load and Store Funtions

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  • Advanced Concepts in PIG
    • Parameter Substitutions

      Import Macros

      Pig Macros

      Embedded PIG in JAVA

      Pig Unit Testing

      Intermediate Compression

      File compression in PIG

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  • Hadoop Overview
    • What is a Hadoop Framework

      Type of Hadoop Frameworks

      Hadoop Ecosystem

      HDFS

      Big Data Overview

      Hadoop Overview

      Course overview

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  • No SQL Databases Hbase
    • HBase Shell

      Connecting to HBase

      Data Model

      HBase Overview

      HBase Architecture

      NoSQL Introduction

      NoSQL Databases Hbase

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  • Administration in HBASE
    • Interacting with HBase

      Basic Static Configuration

      Rolling Restarts and Upgrades

      Installation with Cloudera Manager

      Hbase Deployment at scale

      Software Recommendations

      Hardware Recommendations

      Introduction

      Learn and Understand Hbase Fault Tolerance

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  • Troubleshooting in Hbase
    • Learn How To Use the Metrics

      Learn How To Use the Logs

      Learn How To Use the Hbase UI

      Troubleshooting Distributed Clusters

      Introduction

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  • Troubleshooting in Hbase
    • Tuning for Modern Hardware

      Tuning with Failures

      Memory Tuning

      Table Storage Tuning

      Generating With YCSB

      Region Tuning

      Generating Load & Load Test Tool

      Introduction

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  • Apache HBase Operations Continuity
    • Cluster Replication

      Snapshots

      Import Export and copy Paste

      Security Demo

      Security

      Corruption: Other Tools

      Corruption: hbck

      Introduction

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  • Apache HBASE Ecosystem
    • Hue

      HBase With Apache Phoenix’

      Introduction

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+ More Lessons

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?

Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards. Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.
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FAQ's

Pre-course reading will be provided so that you are familiar with the content before the class begins.

Yes. Visit our payment plans website to discover more about the payment choices available at Croma Campus.

You will be able to register for our next training session, as we only offer two to three each year.

Yes, there are a variety of payment options.

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  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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