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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

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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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Track Week Days Weekends Fast Track
Course Duration 40-45 Days 7 Weekends 8 Days
Hours 1 Hrs. Per Day 2 Hrs. Per Day 6+ Hrs. Per Day
Training Mode Classroom/Online Classroom/Online Classroom/Online
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CURRICULUM & PROJECTS

Apache Spark and Scala Training

  • 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.
  • In this program you will learn:
    • Introduction

      Scala

      Using Resilient Distributed Datasets

      Spark SQL, Data Frames, and Data Sets

      Running Spark on a cluster

      Machine Learning with Spark ML

      Spark Streaming

      Graph X

      Overview

      Kafka Producer

      Kafka Consumers

      Kafka Internals

      Cluster Architecture and Administering Kafka

      Kafka Monitoring & Kafka Connect

      Kafka Stream Processing

      Kafka Integration with Hadoop, Storm, and Spark

      Kafka Integration with Flume, Talend and Cassandra

      Career Guidance and Roadmap

      Introduction

      PIG Architecture

      Data Models, Operators, and Streaming in PIG

      Functions in PIG

      Advanced Concepts in PIG

      Hadoop Overview

      No SQL Databases Hbase

      Administration in HBASE

      Troubleshooting in Hbase

      Troubleshooting in Hbase

      Troubleshooting in Hbase

      Apache HBASE Ecosystem

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  • Introduction
    • Big Data Overview

      Apache Hadoop Overview

      Hadoop Distibution File System

      Hadoop MapReduce Overview

      Introduction to IntelliJ and Scala

      Installing IntelliJ and Scala

      Apache Spark Overview

      What’s new in Apache Spark 3

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  • Scala
    • Flow control in Scala

      Functions in Scala

      Data Structures in Scala

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  • Using Resilient Distributed Datasets
    • The Resilient Distributed Dataset

      Ratings Histogram Example

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

      Filtering RDD's and the Minimun Temperature by Location Example

      Check Your Results and Implementation Against Min

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  • Spark SQL, Data Frames, and Data Sets
    • Introduction to Spark SQL

      What are Data Frames

      What are Data Sets

      Item-Based Collaborative Filtering in Spark, cache (), and persist ()

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  • Running Spark on a cluster
    • What is a Cluster

      Cluster management in Hadoop

      Introducing Amazing Elastic MapReduce

      Partitioning Concepts

      Troubleshooting and managing dependencies

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  • Machine Learning with Spark ML
    • Introduction MLLib

      Using MLLib

      Linear Regression with MLLib

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  • Spark Streaming
    • Spark Streaming

      The DStream API for Spark Streaming

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  • Graph X
    • What is Graph X

      About Pregel

      Breadth-First-Search with Pregel

      Using Pregel API with Spark API

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  • Overview
    • Introduction to Big Data

      Big Data Analytics

      Need for Kafka

      What is Kafka

      Kafka Features

      Kafka Concepts

      Kafka Architecture

      Kafka Components

      Zookeeper

      Where is Kafka Used

      Kafka Installation

      Kafka Cluster

      Type of Kafka Clusters

      Configuring Single Node Single Broker Cluster

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  • Kafka Producer
    • Configuring Single Node Multi Broker Cluster

      Sending a Message to Kafka

      Producing Keyed and Non-Keyed Messages

      Sending a Message Synchronously & Asynchronously

      Configuring Producers

      Serializers

      Serializing Using Apache Avro

      Partitions

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  • Kafka Consumers
    • Consumers and Consumer Groups

      Standalone Consumer

      Consumer Groups and Partition Rebalance

      Creating a Kafka Consumer

      Subscribing to Topics

      The Poll Loop

      Configuring Consumers

      Commits and Offsets

      Rebalance Listeners

      Consuming Records With Specific Offsets

      De-serializers

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  • Kafka Internals
    • Cluster Membership

      The Controller

      Replication

      Request Processing

      Physical Storage

      Reliability

      Broker Configuration

      Using Producers in a Reliable System

      Using Consumers in a Reliable System

      Validating System Reliability

      Performance Tuning in Kafka

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  • Cluster Architecture and Administering Kafka
    • Use Cases - Cross-Cluster Mirroring

      Multi-Cluster Architectures

      Apache Kafka’s Mirror Maker

      Other Cross-Cluster Mirroring Solutions

      Topic Operations

      Consumer Groups

      Dynamic Configuration Changes

      Partition Management

      Consuming and Producing

      Unsafe Operations

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  • Kafka Monitoring & Kafka Connect
    • Considerations When Building Data Pipelines

      Metric Basics

      Kafka Broker Metrics

      Client Monitoring

      Lag Monitoring

      End-to-End Monitoring

      Kafka Connect

      When to Use Kafka Connect

      Kafka Connect Properties

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  • Kafka Stream Processing
    • Stream Processing

      Stream-Processing Concepts

      Stream-Processing Design Patterns

      Kafka Streams by Example

      Kafka Streams: Architecture Overview

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

      Hadoop Configuration

      Kafka Integration with Hadoop

      Apache Storm Basics

      Configuration of Storm

      Integration of Kafka with Storm

      Apache Spark Basics

      Spark Configuration

      Kafka Integration with Spark

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

      Integration of Kafka with Flume

      Cassandra Basics Such as Key Space and Table Creation

      Integration of Kafka with Cassandra

      Talend Basics

      Integration of Kafka with Talend

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  • Career Guidance and Roadmap
    • Apache Hadoop Overview

      Hadoop Distribution File System

      Hadoop MapReduce Overview

      Introduction to PIG

      Prerequisites for Apache PIG

      Exploring use cases for PIG

      History of Apache PIG

      Why you need PIG

      Significance od PIG

      PIG over MapReduce

      When PIG suits the most

      When to avoid PIG

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  • PIG Architecture
    • PIG Latin Language

      Running PIG in Different Modes

      PIG Architecture

      GRUNT Shell

      PIG Latin Statements

      Running Pig Scripts

      Utility Commands

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  • Data Models, Operators, and Streaming in PIG
    • PIG Data Model - Scarlar Data Type

      PIG Data Model - Complex Data Type

      Arithmetic Operators

      Comparison Operators

      Cast Operators

      Type Construction Operators

      Relation Operators

      Loading and Stroing Operators

      Filtering Operators

      Filtering Operators- Pig Streaming with Python

      Grouping and Joining Operators-

      Sorting Operator

      Combining and Splitting Operators

      Diagnostic Operators

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  • Functions in PIG
    • Eval Functions

      Load and Store Funtions

      Tuple and Bag Functions

      String Functions

      Math Function

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  • Advanced Concepts in PIG
    • File compression in PIG

      Intermediate Compression

      Pig Unit Testing

      Embedded PIG in JAVA

      Pig Macros

      Import Macros

      Parameter Substitutions

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  • Hadoop Overview
    • Course overview

      Big Data Overview

      Hadoop Overview

      HDFS

      Hadoop Ecosystem

      What is a Hadoop Framework

      Type of Hadoop Frameworks

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  • No SQL Databases Hbase
    • NoSQL Databases Hbase

      NoSQL Introduction

      HBase Overview

      HBase Architecture

      Data Model

      Connecting to HBase

      HBase Shell

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  • Administration in HBASE
    • Introduction

      Learn and Understand Hbase Fault Tolerance

      Hardware Recommendations

      Software Recommendations

      Hbase Deployment at scale

      Installation with Cloudera Manager

      Basic Static Configuration

      Rolling Restarts and Upgrades

      Interacting with HBase

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  • Troubleshooting in Hbase
    • Introduction

      Troubleshooting Distributed Clusters

      Learn How To Use the Hbase UI

      Learn How To Use the Metrics

      Learn How To Use the Logs

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  • Troubleshooting in Hbase
    • Introduction

      Generating Load & Load Test Tool

      Generating With YCSB

      Region Tuning

      Table Storage Tuning

      Memory Tuning

      Tuning with Failures

      Tuning for Modern Hardware

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  • Apache HBase Operations Continuity
    • Introduction

      Corruption: hbck

      Corruption: Other Tools

      Security

      Security Demo

      Snapshots

      Import Export and copy Paste

      Cluster Replication

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  • Apache HBASE Ecosystem
    • Introduction

      Hue

      HBase With Apache Phoenix’

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+ More Lessons

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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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