Apache Spark with Scala Certification Training

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Learners : 1080

Duration :  60 Days

About Course

When used alone or in conjunction with other distributed computing tools, Apache Spark is a data processing framework that can quickly conduct operations on very large data sets and distribute operations across several machines. These two characteristics are essential to the fields of big data and machine learning, which call for the mobilization of enormous computational power to process vast data repositories. With an intuitive API that abstracts away most of the tedious labor of distributed computing and big data processing, Spark also relieves developers of some of the programming responsibilities associated with these activities.

Apache Spark with Scala Training Course Syllabus

INTRODUCTION TO SCALA (Apache Spark)

✔ Introducing Scala
✔ Use of java virtual machine in Scala
✔ What is object-oriented programming language
✔ What is a functional language
✔ Scala Basics terms
✔ Things to note about Scala
✔ Java Vs Scala

Installation and Setup

✔ JDK Installation
✔ Scala Installation
✔ Eclipse Installation and Setup
✔ First Spark / Scala application using Eclipse

Use of Scala

✔ Advantages of Scala
✔ Data Types in Scala
✔ What all are the companies using Scala
✔ Access Modifiers in Scala

✔ Private
✔ Protected
✔ No-Access Modifier

EXECUTING THE SCALA CODE

✔ Hello Word Program
✔ What is class
✔ What is Object
✔ Class Vs Object
✔ Types of variables in Scala (Mutable, Immutable and Final)
✔ Val Vs Var
✔ Operations on Variables

CLASSES CONCEPT IN SCALA

✔ Learning about the classes concept
✔ Understanding the parameters passing
✔ Understanding the overloading
✔ Understanding the overriding
✔ Names Arguments
✔ Class Constructors
✔ Inheritance
✔ Field Override
✔ Method Overriding

SCALA COLLECTIONS

✔ Introduction to Scala collections
✔ Classification of collections
✔ The difference between iterator and iterable in Scala
✔ Example of list sequence in Scala

MUTABLE COLLECTIONS VS IMMUTABLE COLLECTIONS

✔ Two types of collections in Scala
✔ Mutable and immutable collections
✔ Understanding lists and arrays in Scala
✔ The list buffer and the array buffer

Scala Threads

✔ Types of threads creation
✔ multi-tasking in threads
✔ Threads priority

Scala Exception Handling

✔ Introduction to Exceptions
✔ How to define Try / Catch / Finally blocks
✔ Throw Vs Throws

INTRODUCTION TO SPARK (Apache Spark)

✔ Introduction to Spark
✔ The overview of Spark and how it is better than Hadoop
✔ Spark history server and Cloudera distribution
✔ Features of Spark
✔ Components of Spark

SPARK BASICS

✔ Memory management
✔ Executor memory vs driver memory
✔ Working with Spark Shell
✔ The concept of resilient distributed datasets (RDD)
✔ The architecture of Spark
✔ Introduction to Spark Core
✔ Introduction to Spark SQL
✔ Introduction to Spark Streaming
✔ Modes of Apache spark deployment

WORKING WITH RDDS IN SPARK

✔ Spark RDDs
✔ Creating RDDs
✔ RDD partitioning
✔ Features of RDD
✔ Operations and transformations in RDDs
✔ Narrow Transformations (Map, Flat Map, Map Partition, Filter, Sample, Union)
✔ Wide Transformations (Intersection, Distinct, ReduceByKey, GroupByKey, Joins, Cartesian, Repartition, Coalesce, Subtract)

AGGREGATING DATA WITH PAIRED RDDS

✔ Various operations of RDDs
✔ Distributed shared memory vs RDD
✔ Fine and coarse-grained update
✔ Spark Actions (Collect, Count, Take, First, Reduce, CountByValue, Max, Min, Sum, Top, Take Ordered, Take Sample, Foreach)

SPARK SQL & DATAFRAMES

✔ Learning about Spark SQL
✔ The context of SQL in Spark for providing structured data processing
✔ Data Frames in Spark
✔ Creating Data Frames
✔ Purpose of Data Set
✔ Data Frame Vs Data Set
✔ JSON support in Spark SQL
✔ Working with XML data
✔ Parquet files
✔ Creating Hive context
✔ Writing a Data Frame to Hive
✔ Reading JDBC files
✔ Manual inferring of schema
✔ Working with CSV Files

WRITING & DEPLOYING SPARK APPLICATIONS

✔ Comparing Spark applications with Spark Shell
✔ Creating a Spark application using Scala or Java (Word count program)
✔ Deploying a Spark application
✔ Scala built application
✔ Creation of the mutable list, set and set operations, lists, tuples, and concatenating lists
✔ The web user interface of a Spark application

Spark Project

✔ Introduction to live project
✔ code walkthrough
✔ Project explanation

Apache Spark and Scala Materials

✔ All the materials like PPTs and Complete reference books will share it over email.

Apache Spark Resume

✔ Sample resumes will share over email
✔ How to prepare spark resume and sample resume walkthrough

Live Instructor Based Training With Software
Lifetime access and 24×7 support
Certification Oriented content
Hands-On complete Real-time training
Get a certificate on course completion
Flexible Schedules
Live Recorded Videos Access
Study Material Provided

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Live Virtual Training

  • Schedule your sessions at your comfortable timings.
  • Instructor-led training, Real-time projects
  • Certification Guidance.
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  • Complete set of live-online training sessions recorded videos.
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  • Learn As A Full Day Schedule With Discussions, Exercises,
  • Practical Use Cases
  • Design Your Own Syllabus Based
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Apache Spark with Scala Training FAQ'S

What is Apache Spark?

The Hadoop Ecosystem uses Apache Spark, an open-source framework and in-memory computing processing engine, to process data. It uses distributed and parallel processing to handle both batch and real-time data.

Difference between Spark and MapReduce?

MapReduce: MapReduce is I/O intensive read from and writes to disk. It is batch processing. MapReduce is written in Java only. It is not iterative and interactive. MapReduce can process larger sets of data compared to Spark.

Spark: Spark is a lighting-fast in-memory computing process engine, 100 times faster than MapReduce, and 10 times faster than disk. Spark supports languages like Scala, Python, R, and Java. Spark Processes both batch as well as Real-Time data.

Get ahead in your career by learning Apache Spark through VISWA Online Trainings

What are the components/modules of Apache Spark?

Apache Spark comes with SparkCore, Spark SQL, Spark Streaming, Spark MlLib, and GraphX

  • Spark Core
  • Spark SQL
  • Spark Streaming
  • MLib
  • GraphX
What are the different installation modes of Spark?

Spark can be installed in 3 different ways.

  • Standalone mode:
  • Pseudo-distribution mode:
  • Multi cluster mode:
What is Spark Session?

Spark Session is an entry point to the underlying Spark functionality that enables programmatic creation of Spark RDD, DataFrame, and DataSet. It was first introduced in version 2.0 of Spark. The default variable in spark-shell is the SparkSession object spark, which may be constructed programmatically using the SparkSession builder pattern.

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