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Apache Spark and Scala Course Training

Apache Spark and Scala

Unlock the power of big data with hands-on learning in Apache Spark and Scala.

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Apache Spark and Scala Course Training Highlights

Why to learn Apache Spark using Scala

24 Hours of Hands-On Training for Practical Skill-Building

70+ Hours of MCQs and Assignments for Practice

3 Real-Time Projects to Apply Knowledge Effectively

24/7 Expert Support and Guidance for Enhanced Learning

In this era of Artificial intelligence, machine learning, and data science, algorithms that run on Distributed Iterative computation make the task of distributing and computing huge volumes of data easy. Spark is a lightning-fast, in-memory, cluster computing framework that can be used for a variety of purposes. This JVM-based open source framework can be used for processing and analyzing huge volumes of data and at the same time can be used to distribute data over a cluster of machines. It is designed in such a way that it can perform batch and stream processing and hence is known as a cluster computing platform. Scala is the language in which Spark is developed. Scala is a powerful and dynamic programming language that doesn’t compromise on type safety.

Do you know the secret behind Uber’s flawless map functioning? Here’s a hint, the images gathered by the Map Data Collection Team are accessed by the downstream Apache Spark team and are assessed by operators responsible for map edits. A number of file formats are supported by Apache Spark which allows multiple records to be stored in a single file.

According to a recent survey by DataBricks, 71% of Spark users use Scala for programming. Spark with Scala is a perfect combination to stay grounded in the Big Data world. 9 out of 10 companies have this successful combination running in their organizations. Spark has over 1000 contributors across 250+ organizations making it the most popular open source project ever. The Apache Spark Market is expected to grow at a CAGR of 67% between 2019 and 2022 jostling a high demand for trained professionals.

WHY KNOWLEDGEHUT FOR Apache Spark and Scala Course TRAINING

The KnowledgeHut Advantage

Instructor-led Live Classroom

Learn and interact in real-time with industry experts through hands-on sessions.

Curriculum Designed by Experts

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Gain practical skills through theory, case studies, and hands-on coding practice for real-world application.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

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Learn from scratch and advance with step-by-step guidance on tools and techniques.

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Get reviews and feedback on your final projects from professional developers.

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Apache Spark and Scala Course Training

Prerequisites and Eligibility

Although you don't have to meet any prerequisites to take up Apache Spark and Scala certification training, having familiarity with Python/Java or Scala programming will be beneficial. Other than this, you should possess:

  • Basic understanding of SQL, any database, and query language for databases.
  • It is not mandatory, but helpful for you to have working knowledge of Linux or Unix-based systems.
  • Also, it is recommended to have a certification training on Big Data Hadoop Development.
Prerequisites and Eligibility

Apache Spark and Scala Course Syllabus

Curriculum

1. Introduction to Big Data Hadoop and Spark

Learning Objectives:

Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture. You will also get an introduction to Spark and the difference between batch processing and real-time processing.

Topics:

  • What is Big Data?
  • Big Data Customer Scenarios
  • What is Hadoop?
  • Hadoop’s Key Characteristics
  • Hadoop Ecosystem and HDFS
  • Hadoop Core Components
  • Rack Awareness and Block Replication
  • YARN and its Advantage
  • Hadoop Cluster and its Architecture
  • Hadoop: Different Cluster Modes
  • Big Data Analytics with Batch & Real-time Processing
  • Why Spark is needed?
  • What is Spark?
  • How Spark differs from other frameworks?

Hands-on: Scala REPL Detailed Demo.

2. Introduction to Scala

Learning Objectives:

Learn the basics of Scala that are required for programming Spark applications. Also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.

Topics:

  • What is Scala?
  • Why Scala for Spark?
  • Scala in other Frameworks
  • Introduction to Scala REPL
  • Basic Scala Operations
  • Variable Types in Scala
  • Control Structures in Scala
  • Foreach loop, Functions and Procedures
  • Collections in Scala- Array
  • ArrayBuffer, Map, Tuples, Lists, and more

Hands-on: Scala REPL Detailed Demo

3. Object Oriented Scala and Functional Programming Concepts

Learning Objectives:

Learn about object-oriented programming and functional programming techniques in Scala.

Topics

  • Variables in Scala
  • Methods, classes, and objects in Scala
  • Packages and package objects
  • Traits and trait linearization
  • Java Interoperability
  • Introduction to functional programming
  • Functional Scala for the data scientists
  • Why functional programming and Scala are important for learning Spark?
  • Pure functions and higher-order functions
  • Using higher-order functions
  • Error handling in functional Scala
  • Functional programming and data mutability

Hands-on: OOPs Concepts- Functional Programming

4. Collection APIs

Learning Objectives:

Learn about the Scala collection APIs, types and hierarchies. Also, learn about performance characteristics.

Topics

  • Scala collection APIs
  • Types and hierarchies
  • Performance characteristics
  • Java interoperability
  • Using Scala implicits

5. Introduction to Spark

Learning Objectives:

Understand Apache Spark and learn how to develop Spark applications.

Topics:

  • Introduction to data analytics
  • Introduction to big data
  • Distributed computing using Apache Hadoop
  • Introducing Apache Spark
  • Apache Spark installation
  • Spark Applications
  • The back bone of Spark – RDD
  • Loading Data
  • What is Lambda
  • Using the Spark shell
  • Actions and Transformations
  • Associative Property
  • Implant on Data
  • Persistence
  • Caching
  • Loading and Saving data

Hands-on:

  • Building and Running Spark Applications
  • Spark Application Web UI
  • Configuring Spark Properties

6. Operations of RDD

Learning Objectives:

Get an insight of Spark - RDDs and other RDD related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).

Topics

  • Challenges in Existing Computing Methods
  • Probable Solution & How RDD Solves the Problem
  • What is RDD, Its Operations, Transformations & Actions
  • Data Loading and Saving Through RDDs
  • Key-Value Pair RDDs
  • Other Pair RDDs, Two Pair RDDs
  • RDD Lineage
  • RDD Persistence
  • WordCount Program Using RDD Concepts
  • RDD Partitioning & How It Helps Achieve Parallelization
  • Passing Functions to Spark

Hands-on:

  • Loading data in RDD
  • Saving data through RDDs
  • RDD Transformations
  • RDD Actions and Functions
  • RDD Partitions
  • WordCount through RDDs

7. DataFrames and Spark SQL

Learning Objectives:

Learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kinds of SQL operations performed on the data-frames. Also, learn about the Spark and Hive integration.

Topics

  • Need for Spark SQL
  • What is Spark SQL?
  • Spark SQL Architecture
  • SQL Context in Spark SQL
  • User Defined Functions
  • Data Frames & Datasets
  • Interoperating with RDDs
  • JSON and Parquet File Formats
  • Loading Data through Different Sources
  • Spark – Hive Integration

Hands-on:

  • Spark SQL – Creating Data Frames
  • Loading and Transforming Data through Different Sources
  • Spark-Hive Integration

8. Machine learning using MLlib

Learning Objectives:

Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.

Topics

  • Why Machine Learning?
  • What is Machine Learning?
  • Where Machine Learning is Used?
  • Different Types of Machine Learning Techniques
  • Introduction to MLlib
  • Features of MLlib and MLlib Tools
  • Various ML algorithms supported by MLlib
  • Optimization Techniques

9. Using Spark MLlib

Learning Objectives:

Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and so on

Topics

  • Supervised Learning - Linear Regression, Logistic Regression, Decision Tree, Random Forest
  • Unsupervised Learning - K-Means Clustering

Hands-on:

  • Machine Learning MLlib
  • K- Means Clustering
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest

10. Streaming with Kafka and Flume

Learning Objectives:

Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Clusters. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using flume.

Topics

  • Need for Kafka
  • What is Kafka?
  • Core Concepts of Kafka
  • Kafka Architecture
  • Where is Kafka Used?
  • Understanding the Components of Kafka Cluster
  • Configuring Kafka Cluster
  • Kafka Producer and Consumer Java API
  • Need of Apache Flume
  • What is Apache Flume?
  • Basic Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Flume Configuration
  • Integrating Apache Flume and Apache Kafka

Hands-on:

  • Configuring Single Node Single Broker Cluster
  • Configuring Single Node Multi Broker Cluster
  • Producing and consuming messages
  • Flume Commands
  • Setting up Flume Agent

What You'll Learn in the Apache Spark and Scala Course

Learning Objectives
Big Data Introduction

Understand Big Data, its components and the frameworks, Hadoop Cluster architecture and its modes.

Introduction on Scala

Understand Scala programming, its implementation, basic constructs required for Apache Spark.

Spark Introduction

Gain an understanding of the concepts of Apache Spark and learn how to develop Spark applications.

Spark Framework & Methodologies

Master the concepts of the Apache Spark framework and its associated deployment methodologies.

Spark Data Structure

Learn Spark Internals RDD and use of Spark’s API and Scala functions to create and transform RDDs.

Spark Ecosystem

Master the RDD and various Combiners, SparkSQL, Spark Context, Spark Streaming, MLlib, and GraphX.

Who Can Attend the Apache Spark and Scala Course

Who This Course Is For
  • Data Scientists
  • Data Engineers
  • Data Analysts
  • BI Professionals
  • Research professionals
  • Software Architects
  • Software Developers
  • Testing Professionals
  • Anyone who is looking to upgrade Big Data skills
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Apache Spark and Scala Training FAQs

Frequently Asked Questions
Apache Spark & Scala Course

1. What are the prerequisites for learning Apache Spark?

Prerequisites for Spark are.

  1. Basics of Hadoop file system
  2. Understanding of SQL concepts
  3. Basics of any Distributed Database (HBase, Cassandra)

2. Why should I learn Apache Spark?

These are the reasons why you should learn Apache Spark:-

  1. Spark can be integrated well with Hadoop and that’s a great advantage for those who are familiar with the latter.
  2. According to technology forecasts, Spark is the future of worldwide Big Data Processing. The standards of Big Data Analytics are rising immensely with Spark, driven by high-speed data processing and real time results.
  3. Spark is an in-memory data processing framework and is all set to take up all the primary processing for Hadoop workloads in the future. Being way faster and easier to program than MapReduce, Spark is now among the top-level Apache projects.
  4. The number of companies that are using Spark or are planning the same has exploded over the last year. There is a massive surge in the popularity of Spark, the reason being its matured open-source components and an expanding community of users.
  5. There is a huge demand for Spark Professionals and the demand for them is increasing.

3. Who should do the Apache Spark course?

  • Professionals aspiring for a career in the field of real-time big data analytics
  • Analytics professionals
  • Research professionals
  • IT developers and testers
  • Data scientists
  • BI and reporting professionals
  • Students who wish to gain a thorough understanding of Apache Spark

4. What should be the system requirements for me to learn Apache Spark online?

  • You just need 4GB RAM to learn Spark.
  • Windows 7 or higher OS
  • i3 or higher processor

5. What are the course objectives?

You will get in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka.

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