Apache Spark and Scala Certification Training

Big Data is a term used to describe a huge volume of data. Using this Big Data technology, you can manage data at scale. The Big Data field provides the best ways to analyze large data sets that are too complex. Big Data deals with both structured and unstructured data present in the organizations. You even can extract useful information from extremely complex data sets. In this module, you will get to know the deeper insights of Big Data. 

Topics Covered in this module are:

• Big Data Overview
• Big Data Life Cycle
• Data Tools
• Statistical Methods
• Logistic Regression
• Cleansing Data
• Data Visualization

Apache Hadoop is the most popularly used open-source framework. This is mainly designed for processing large data sets across different clusters of computers using simple MapReduce programming models. It has the capability to handle and detect failures at the application layer. Hadoop provides the best ways to change methods of analyzing and processing data. In this module, you will get to know more in detail about the Apache Hadoop along with its key applications. 

Topics Covered in this module are:

• Apache Hadoop Overview
• Hadoop Installation
• Hadoop Modules
• What is HDFS and its key features
• Introduction to Yarn
• MapReduce API

Apache Spark open-source analytics engine is also known as a cluster computing framework. It is mainly designed for processing large-scale data workloads and Machine Learning. It consists of built-in modules used for performing graph processing, data streaming, etc. Apache Spark also provides high-level APIs for programming languages like R programming, Java, Python, Scala, and many more. It also makes use of optimized query execution and in-memory caching for generating fast queries against any size of data. Apache Spark is also named as an open-source cluster computing framework.

Topics Covered in this module are:

• Apache Spark Overview
• History of Apache Spark
• Essential features of Apache Spark
• Key components of Apache Spark
• Uses of Apache Spark
• Installation of Apache Spark
• Advanced Spark Programming

Apache Spark is mainly designed for processing large-scale data workloads and Machine Learning. It consists of built-in modules used for performing graph processing, data streaming, etc. There are many in-built functions in Apache Spark and you will get in detail about each in-built and their use. 

Topics Covered in this module are:

• Cartesian Function
• Union Function
• Filter Function
• Co-Group Function
• Intersection Function
• Count Function
• Map Function
• reducedByKey Function
• Distinct Function

Apache Spark’s open-source analytics engine is also known as a cluster computing framework. Master-slave architecture is followed by Apache Spark. This Architecture completely depends and works based on two abstractions and they are Directed Acyclic Graph (DAG) and Resilient Distributed Dataset (RDD). As it follows master-slave architecture it consists of one master and a number of slaves. In this module, you will get to know in detail about the Spark Architecture and its key components.

Topics Covered in this module are:

• What is Resilient Distributed Dataset (RDD)
• What is Directed Acyclic Graph
• Apache Spark Components 
• Gain insights on Spark SQL, Spark Core, and Spark Streaming.
• The workflow in Apache Spark

Apache Spark Resilient Distributed Dataset (RDD) is considered as a fundamental data structure of Apache Spark. It is defined as a collection of immutable objects and is also a partitioned collection of records. In this module, you will get to know in detail about the working of Spark RDD and the elements used in it. The division of datasets in RDD is also well explained in this section. 

Topics Covered in this module are:

• Introduction to Spark RDD
• What are Parallelized Collections
• Different ways used to create Spark RDD
• RDD persistence
• Use of external datasets
• Data Sharing in MapReduce
• Data Sharing using Spark RDD
• Iterative Operations on Spark RDD

Spark RDD is defined as a collection of immutable objects and is also a partitioned collection of records. Apache Spark Resilient Distributed Dataset (RDD) is considered as a fundamental data structure of Apache Spark. Spark RDD mainly provides two different types of operations. In this module, you will get to know in detail about each RDD operation and their need in unified analytics Engine Spark. 

Topics Covered in this module are:

• What is Spark RDD
• Different RDD operations 
• Transformations 
• What are the different RDD Transformations
• Action Operation
• Different RDD Actions

Apache Spark Resilient Distributed Dataset (RDD) is considered as a fundamental data structure of Apache Spark. In this module, you get through the use of RDD shared variables in performing RDD operations. You will also get to know about the types of variables used in Apache Spark. 

Topics Covered in this module are:

• Spark RDD Overview
• What are RDD shared variables
• What is the use of a Broadcast Variable
• What is the use of an Accumulator
• How to create a numeric accumulator
• How to create a broadcast variable

Apache Spark MLlib is a scalable Machine Learning Library. It mainly contains common utilities and learning algorithms. It provides various algorithms like clustering, classification, collaborative filtering, regression, etc. In this module, you will get to know in detail about Machine Learning and algorithms. You will also learn how to make use of Apache Spark MLlib to train a logistic regression model at scale. 

Topics Covered in this module are:

• Introduction to Machine Learning
• Machine Learning algorithms
• What is Clustering
• What is Logistic Regression
• Classification Model
• Use of Spark Machine Learning Library
• Random Forest
• TensorFlow vs Spark MLlib

Apache Spark is the most popularly used unified analytics Engine mainly designed for processing large-scale data workloads and Machine Learning. Spark is becoming the most popularly used go-to platform for streaming computing applications. There are many use cases in which the Apache Spark is being used for processing large dataset workloads. In this module, you will get to know the real-time applications of Apache Spark.

Topics Covered in this module are:

• Fog Computing
• Machine Learning
• Data Streaming
• Data Enrichment
• Trigger Event Detection
• Complex Session Analysis
• Interactive Analysis

Scala is abbreviated as Scalable Language. Scala programs are converted into bytecodes and run on the Java Virtual Machine (JVM).  Scala programming is used for providing enriched functional programming support. Scalable programming language combines functional and object-oriented programming in single concise. Apache Spark is also developed and coded in Scala programming. Apache Spark provides APIs for the Scala Programming language. In this module, you will get a clear understanding of Scala programming basics and how to code in Scala programming to achieve functional programming support. 

Topics Covered in this module are:

• Introduction to Scalable programming language
• History of Scala
• Basic Hello Program in Scala
• Scala Comments
• Scala Basic Syntax
• Key features of Scala programming

Scala is abbreviated as Scalable Language. Scala programs are converted into bytecodes and run on the Java Virtual Machine (JVM).  Scala programming is used for providing enriched functional programming support. Scalable programming language combines functional and object-oriented programming in single concise. Scala can be installed on any Window-based system or UNIX systems. In order to install Scala in on PC, you must have Java 1.8 or greater versions in your systems. 

Topics Covered in this module are:

• Introduction to Scala
• Verification of Java Installation
• Set up Java Environment
• Installation of Scala
• Different Scala Versions

Scala is the most prominently used Object Oriented Programing language. This Scalable Programming also allows you to create a class and an object in order to help you in developing object-oriented applications. Class is defined as a blueprint or a template. In another way, the class can also be defined as a collection of objects. The object is defined as a real-world entity of a class. An object mainly has a state and behavior. In this module, you will get to know in detail about the concept of classes and objects in Scala programming.

Topics Covered in this module are:

• What is a Class
• What is an Object
• Data Member
• Nested Class
• What is a Member Method
• Constructor
• Examples of class and object
• What is an Anonymous object

Pattern Matching is a key feature in Scala Programming. This works the same as a switch case which is present in other programming languages. Pattern Matching is mainly used to match best-case available in the pattern. The match expression also returns the case value. Scala provides enhanced support in processing the messages for Pattern Matching. The sequence of alternatives is included in Pattern Matching. 

Topics Covered in this module are:

• What is a Pattern Matching
• Pattern Matching Examples
• How to match the best case available in the pattern
• Any class in Scala
• How to implement a Pattern Match in the program
• Use of underscore (_) in the default case

Scala Trait is defined as an interface with a partial implementation that is mainly used to encapsulate field and method definitions. These definitions can be reused by mixing them up into the classes. In Scala Programming, Trait is also defined as a collection of both abstract and non-abstract methods. Scala Traits are mainly used to define object types by defining the signature of the supported methods. The trait definition looks like a class name but differs from the keyword trait. 

Topics Covered in this module are:

• What is Scala Trait
• Scala Trait Mixins
• Scala Trait Examples
• How to implement multiple traits in a class

Access Modifiers in the Scala Programming are mainly used to define the data accessibility and the capability to showcase our code to the outside world. Access modifiers can be applied to various fields like constructors, classes, data members, traits, member methods, etc. According to a given requirement, you can apply an access modifier to your code. In this module, you will get to know in detail about different types of access modifiers. In Scala programming, if you don’t mention access modifier it is considered as no modifier. 

Topics Covered in this module are:

• Scala Access Modifiers
• Different types of Access Modifiers
• Private Access Modifier
• Protected Access Modifier

Scala is the most prominently used Object Oriented Programing language. Class is defined as a blueprint or a template. In another way, the class can also be defined as a collection of objects. The object is defined as a real-world entity of a class.

This Scalable Programming also allows you to create a class and an object in order to help you in developing object-oriented applications. In this module, you will get through different OOPs concepts present in Scalable Programming language and their usage in Scala programs. 

Topics Covered in this module are:

• What is a class
• What is an object
• Singleton Object
• Constructors
• Method Overloading
• Inheritance
• Method Overriding
• This keyword
• Final keyword
• Field Overriding

In Scala Programming, a variable is defined as a name that is mainly used to refer to a memory location. In Scala, you can create both mutable and immutable variables. You will get to know more in detail about variables, variable declaration, and use of variables in different scenarios. In Scala, Data Types are quite similar to Java but there is no concept of primitive data types in Scala. Everything is dealt with objects and starts with a capital letter. In this module, you will get to know in detail about the different data types used in Scala Programming.

Topics Covered in this module are:

• What is a variable
• What is a Data Type
• Mutable Variable
• Immutable Variable
• Different Data Types in Scala

In Scala, there are a set of built-in functions, and these functions can be used to perform some operations in the program. The functional programming approach is supported by Scalable Language. You can also create user-defined functions in Scala. The functions in Scala can be created by using the def keyword. The function can be used to pass an argument and to store function value. The key point to be considered while defining a function is that you need to mention the return type of parameters. In Scala Programming the default return type is Unit.

Topics Covered in this module are:

• What is a function in Scala
• Use of Function
• Higher-Order Functions
• Function Declaration Syntax
• Scala Parameterized Function
• Recursion Function

In Scala Programming, exceptions play a key role in handling abnormal conditions. By using the exception handling mechanism you can also avoid the termination of the program unexpectedly. Scalable Language only contains unchecked exceptions there are no checked exceptions present. In this module, you will get to know in detail about all the available exceptions in Scala Programming along with their usage. 

Topics Covered in this module are:

• What is an Exception
• Concept of Exception Handling
• Scala Custom Exception
• Try Catch Block
• Finally Block
• Use of Throw and Throws Keyword
• Types of Exceptions
• What is an Unchecked Exception

Scala language provides an enriched set of collection library. This Scala Collection library contains traits and classes to collect data. You can make use of collections as per your requirement. All the mutable collections are present in the Scala.collection.mutable package. Whereas, all the immutable abstract classes and collections are present in Scala.collection.immutable package. In this module, you will get to know more in detail about the concept of collections in Scala.

Topics Covered in this module are:

• What is a Collection in Scala
• Mutable Collections Hierarchy
• Immutable Collections Hierarchy
• What is Scala Traversable
• Methods of Traversable Trait
• Scala Iterable

The collection of elements in a sequentially ordered form is called a Tuple. If there is no element available or present then it is called an empty tuple. A tuple can be used to store any kind of data. Using tuple you can return multiple values from a function. You will get to know more in detail about the Scala tuples using examples. You can even create a tuple of different types of elements. 

Topics Covered in this module are:

• What is a Tuple
• How to create a Tuple
• Use of Tuple in Scala Function
• Examples of Scala Tuples

Multithreading is defined as a process of executing multiple threads simultaneously. Using the concept of multithreading in Scala you can perform multiple operations independently. In Scala programming, you can achieve multitasking by using the Multithreading process. By using multithreading you can develop concurrent applications in Scala. You can create a thread either by extending the runnable interface or thread class. 

Topics Covered in this module are:

• What is a Thread
• Thread Life Cycle
• The different Scala Thread States
• What is Multithreading
• Use of Runnable Interface
• What is Multitasking

In Scala Programming, you can deal with files using the predefined methods. You can learn to create, read, write, and open files. Scala.io package is provided by Scala for file handling. In this module, you will get through all the file operations in a detailed manner.

Topics Covered in this module are:

• How to create a file
• How to read a file
• How to handle files in Scala