Apache Spark and Scala Training in Bangalore, India

Master Apache Spark using Scala with advanced techniques & get started on a lucrative Big Data career!

  • 24 hours of instructor-led live online training
  • Master the concepts on Apache Spark framework & development
  • In-depth exercises and real-time projects on Apache Spark
  • Learn about Apache Spark Core, Spark Internals, RDD, Spark SQL, etc
  • Get comprehensive knowledge on Scala Programming language
  • Get Free E-learning Access to 100+ courses

Why to learn Apache Spark using Scala

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.

Benefits of Apache Spark with Scala:

Apache Spark with Scala is used by 9 out of 10 organizations for their big data needs. Let’s take a look at its benefits at the individual and organizational level: 

Individual Benefits:

  • Learn Apache Spark to have increased access to Big Data
  • There’s a huge demand for Spark Developers across organizations
  • With an Apache Spark with Scala certification, you will earn a minimum salary of $100,000.  
  • As Apache Spark is deployed by every industry to extract huge volumes of data, you get an opportunity to be in demand across various industries

Organization Benefits:

  • It supports multiple languages like Java, R, Scala, Python
  • Easier integration with Hadoop as Spark is built on the Hadoop Distributed File System
  • It enables faster  processing of data streams in real-time with accuracy
  • Spark code can be used for batch processing, join stream against historical data, and run ad-hoc queries on stream state

According to Databricks - "The adoption of Apache Spark by businesses large and small is growing at an incredible rate across a wide range of industries, and the demand for developers with certified expertise is quickly following suit". 

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What you will learn

Who should attend the Apache Spark course?

  • 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
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.

KnowledgeHut Experience

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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

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

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

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


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.


  • 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.

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.


  • 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

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


  • 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

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


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

Learning Objectives: Understand Apache Spark and learn how to develop Spark applications.


  • 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               


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

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


  • 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           


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

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.


  • 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       


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

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


  • 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    

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


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


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

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.


  • 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     


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

Learning Objectives: Learn about the different streaming data sources such as Kafka and Flume. Also, learn to create a Spark streaming application.


  • Apache Spark Streaming: Data Sources
  • Streaming Data Source Overview                         
  • Apache Flume and Apache Kafka Data Sources     


Perform Twitter Sentimental Analysis Using Spark Streaming

Learning Objectives: Learn the key concepts of Spark GraphX programming and operations along with different GraphX algorithms and their implementations.


  • A brief introduction to graph theory
  • GraphX             
  • VertexRDD and EdgeRDD                         
  • Graph operators                          
  • Pregel API                       
  • PageRank       


Adobe Analytics

Adobe Analytics processes billions of transactions a day across major web and mobile properties. In recent years they have modernised their batch processing stack by adopting new technologies like Hadoop, MapReduce, Spark etc. In this project we will see how Spark and Scala are useful in refactoring process.

Read More

Interactive Analytics

Apache Spark has many features like, Fog computing, IOT and MLib, GraphX etc. Among the most notable features of Apache Spark is its ability to support interactive analysis. Unlike MapReduce that supports batch processing, Apache Spark processes data faster because of which it can process exploratory queries without sampling.    

Read More

Personalizing news pages for Web visitors in Yahoo

Various Spark projects are running in Yahoo for different applications. For personalizing news pages, Yahoo uses ML algorithms which run on Spark to figure out what individual users are interested in, and also to categorize news stories as they arise to figure out what types of users would be interested in reading them. To do this, Yahoo wrote a Spark ML algorithm 120 lines of Scala.

Read More

Apache Spark Using Scala Training Details

Apache Spark and Scala:

Apache Spark is an open-source parallel processing framework for running large-scale data analytics applications across clustered computers. It can handle both batch and real-time analytics and data processing workloads.

Apache Spark can process data from a variety of data repositories, including the Hadoop Distributed File System (HDFS), NoSQL databases and relational data stores, such as Apache Hive. Spark supports in-memory processing to boost the performance of big data analytics applications, but it can also perform conventional disk-based processing when data sets are too large to fit into the available system memory.

The Spark Core engine uses the resilient distributed data set, or RDD, as its basic data type. The RDD is designed in such a way so as to hide much of the computational complexity from users. It aggregates data and partitions it across a server cluster, where it can then be computed and either moved to a different data store or run through an analytic model. The user doesn't have to define where specific files are sent or what computational resources are used to store or retrieve files.

In addition, Spark can handle more than the batch processing applications that MapReduce is limited to running.

Spark libraries

The Spark Core engine functions partly as an application programming interface (API) layer and underpins a set of related tools for managing and analyzing data. Aside from the Spark Core processing engine, the Apache Spark API environment comes packaged with some libraries of code for use in data analytics applications. These libraries include:

Spark SQL -- One of the most commonly used libraries, Spark SQL enables users to query data stored in disparate applications using the common SQL language.

Spark Streaming -- This library enables users to build applications that analyze and present data in real time.

MLlib -- A library of machine learning code that enables users to apply advanced statistical operations to data in their Spark cluster and to build applications around these analyses.

GraphX -- A built-in library of algorithms for graph-parallel computation.

Apache Spark is a general purpose cluster-computing framework that can be deployed by multiple ways like streaming data, graph processing and Machine learning.

Features of Spark are –

  1. Lighting Fast Processing
  2. Support for Sophisticated Analytics
  3. Real-Time Stream Processing
  4. Ability to Integrate with Hadoop and Existing Hadoop Data
  5. Active and Expanding Community

The different components of Apache Spark are:-

Spark libraries

The Spark Core engine functions partly as an application programming interface (API) layer and underpins a set of related tools for managing and analyzing data. Aside from the Spark Core processing engine, the Apache Spark API environment comes packaged with some libraries of code for use in data analytics applications. These libraries include:

Spark SQL -- One of the most commonly used libraries, Spark SQL enables users to query data stored in disparate applications using the common SQL language.

Spark Streaming -- This library enables users to build applications that analyze and present data in real time.

MLlib -- A library of machine learning code that enables users to apply advanced statistical operations to data in their Spark cluster and to build applications around these analyses.

GraphX -- A built-in library of algorithms for graph-parallel computation.

  • Apache Spark can handle both batch and real-time analytics and data processing workloads.
  • Apache Spark can process data from a variety of data repositories, including the Hadoop Distributed File System (HDFS), NoSQL databases and relational data stores, such as Apache Hive.
  • Spark supports in-memory processing to boost the performance of big data analytics applications
  • Spark is 100 times faster than Map Reduce

Apache Spark supports Java, Scala, Python APIs.

Scala is the language of the future and is the best language to learn for Apache Spark. Apache Spark is completely written in Scala.

Spark supports the Scala APIs as Spark is written completely in Scala. Hence Spark programs written in Scala might have some performance benefits. Since Scala is based on JVM, it is native for Hadoop and so seamlessly work with it. And, in almost all cases, it outperforms python.

Scala is the most upcoming programming language. Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

The main difference between Spark and Scala is that the Apache Spark is a cluster computing framework designed for fast Hadoop computation while the Scala is a general-purpose programming language that supports functional and object-oriented programming.

Apache Spark is implemented in Scala because Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

No. Spark programs can be written using Java and Python too.

Apache Spark in widely considered as the future of Big Data platform. Spark stepped into Big data industry, it has met the enterprises’ expectations in a better way regarding data processing, querying analytics reports in a faster way.

Spark is written in Scala and Scala gives you access to many advanced features of Spark.

Yes, Spark is an open source, cluster-computing framework which supports various programming languages like Scala, Python, Java and R.

Some of the great applications of Apache Spark are:

Spark is a widely-used technology adopted by most of the industries. Some of the prominent applications of Apache Spark are –

Machine Learning – Apache Spark is equipped with a scalable Machine Learning Library called  MLlib that can perform advanced analytics such as clustering, classification, dimensionality reduction, etc. Some of the prominent analytics jobs like predictive analysis, customer segmentation, sentiment analysis, etc., make Spark an intelligent technology.

Fog computing – With the influx of big data concepts, IoT has acquired a prominent space for the invention of more advanced technologies. Based on the theory of connecting digital devices with the help of small sensors this technology deals with a humongous amount of data emanating from numerous mediums. This requires parallel processing which is certainly not possible on cloud computing. Therefore Fog computing which decentralizes the data and storage uses Spark streaming as a solution to this problem.

Event detection – The feature of Spark streaming allows the organization to keep track of rare and unusual behaviors for protecting the system. Institutions like financial institutions, security organizations, and health organizations use triggers to detect the potential risk.

Interactive analysis – Among the most notable features of Apache Spark is its ability to support interactive analysis. Unlike MapReduce that supports batch processing, Apache Spark processes data faster because of which it can process exploratory queries without sampling.

Spark Installation:

Apache Spark 2.3, SBT, Eclipse, Scala, IntelliJ Idea, PySpark(for Spark with Python)

Follow the below steps given below for installing Spark.

Extracting Spark tar file using following command –

$ tar xvf spark-2.4.3-bin-hadoop2.7.3.tgz

Move Spark software files to respective directory using following commands –


# cd /home/Hadoop/Downloads/

# mv spark-2.4.3-bin-hadoop2.7.3 /usr/local/spark

Add the following line to a ~/.bashrc file which will add the location, where the spark software files are located to the PATH variable type.

export PATH = $PATH:/usr/local/spark/bin

Use the following below command for sourcing the ~/.bashrc file.

$ source ~/.bashrc

Verify the Installation of Spark application on your system

The following command will open the Spark shell application version.


Verify the Installation of Spark application on your system

The following command will open the Spark shell application version.


If spark is installed successfully then you will be getting the following output.

Spark assembly has been built with Hive, including Datanucleus jars on classpath

Using Spark’s default log4j profile: org/apache/spark/log4j-defaults.properties

12/04/19 15:25:22 INFO SecurityManager: Changing view acls to: hadoop

12/04/19 15:25:22 INFO SecurityManager: Changing modify acls to: hadoop

12/04/04 15:25:22 INFO SecurityManager: SecurityManager: authentication disabled;

ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop)

12/04/19 15:25:22 INFO HttpServer: Starting HTTP Server

12/04/19 15:25:23 INFO Utils: Successfully started service naming ‘HTTP class server’ on port 43292.

Welcome to the Spark World

Initializing Spark in Scala

import org.apache.spark.SparkConf

import org.apache.spark.SparkContext

import org.apache.spark.SparkContext._

val conf = new SparkConf().setMaster("local").setAppName("My App")

val sc = new SparkContext(conf)

Initializing Spark in Java

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaSparkContext;

SparkConf conf = new SparkConf().setMaster("local").setAppName("My App");

JavaSparkContext sc = new JavaSparkContext(conf);

Use of Apache Spark:

Apache Spark has a few big value propositions:

  • It can run on Hadoop with YARN to work with MASSIVE data sets in a distributed fashion.
  • It not only works mostly (if not all) in-memory, thus reducing the latency associated with Disk I/O, but it can be run with a Streaming Context (micro-batches) that processes data nearly on arrival.
  • It is open source, as is Hadoop, and can run on commodity hardware.
  • Lastly, it has an incredibly extensible API that makes it extremely easy to write code around data fast. You can have a very good analytics application shipped in a matter of days…the development time is super-fast.

Apache Spark is one of the most popular projects in the Hadoop Ecosystem and is, in fact, the most actively developed open source project in Big data. And, it continues to attract more and more people every day.

It is popular not just among Data Scientists but also among Engineers, Developers and everybody else interested in Big Data. It is so popular that a lot of people believe it will grow to replace Map Reduce entirely.

It is popular because of three things, Simplicity, Performance, and Flexibility.

A few things why  Spark is so popular :

One of the major strengths of Spark is its easy integration with the Hadoop ecosystem.

Spark is written in Scala easily embeds in all JVM-based systems. Also, it provides an interactive REPL, Spark-shell, making it easy to test simple  programs

It has API's in Python and Java apart from the native Scala. This makes application development very easy and it makes Spark a great platform for developers.

It comes with a Machine learning library MLlib making it very easy for a lot of people to start with and is ideally suited to ML applications.

With all these features, Spark has become the center of attraction for almost all of the Big Data developers and Data scientists. Though it has only been a few years, Spark has been evolving quickly and promises to be a sure contender for an industry standard in Big Data.

The advantages/benefits of Apache Spark are:-

Integration with Hadoop:

Spark’s framework is built on top of the Hadoop Distributed File System (HDFS). So it’s advantageous for those who are familiar with Hadoop.


Spark also starts with the same concept of being able to run MapReduce jobs except that it first places the data into RDDs (Resilient Distributed Datasets) so that this data is now stored in memory so it’s more quickly accessible i.e. the same MapReduce jobs can run much faster because the data is accessed in memory.

Real-time stream processing

Every year, the real-time data being collected from various sources keeps shooting up exponentially. This is where processing and manipulating real-time data can help us. Spark helps us to analyze real-time data as and when it is collected.

Applications are fraud detection, electronic trading data, log processing in live streams (website logs), etc.

Graph Processing

Apart from Steam Processing, Spark can also be used for graph processing. From advertising to social data analysis, graph processing capture relationships in data between entities, say people and objects which are then are mapped out. This has led to recent advances in machine learning and data mining.


Today companies manage two different systems to handle their data and hence end up building separate applications for it. One for streaming & storing real-time data. The other to manipulate and analyze this data. This means a lot of space and computational time. Spark gives us the flexibility to implement both batch and stream processing of data simultaneously, which allows organizations to simplify deployment, maintenance and application development.

Top Companies Using Spark

  • Microsoft

Including Spark support to Azure HDInsight (its cloud-hosted version of Hadoop).

  • IBM

To manage its SystemML machine learning algorithm construction, IBM uses Spark technology.

  • Amazon

To run Spark apps developed in Scala, Java, and Python, Amazon uses Apache Spark.

  • Yahoo!

Yahoo used to have the origin in Hadoop for analyzing big data. Nowadays, Apache Spark is the next cornerstone.

Apart from them many more names like:

  • Conviva
  • Netflix
  • Pinterest
  • Oracle
  • Hortonworks
  • Cisco
  • Verizon
  • Visa
  • Databricks
  • Amazon
  • Accenture PLC
  • Paxata
  • DataStax, Inc.
  • UC Berkeley AMPLab
  • TripAdvisor
  • Samsung Research America
  • Shopify
  • Premise
  • Quantifind
  • Radius Intelligence
  • OpenTable
  • Hitachi Solutions
  • The Hive
  • IBM Almaden
  • eBay!
  • Bizo and many more

Apache Spark is the go-to tool for Data Science at scale. It is an open source, distributed compute platform which is the first tool in the Data Science toolbox which is built specifically with Data Science in mind.

Spark is different from the myriad other solutions to this problem because it allows Data Scientists to develop simple code to perform distributed computing, and the functionality available in Spark is growing at an incredible rate. Much has been made in the Data Science community around Spark’s ability to train Machine Learning models at scale, and this is a key benefit, but  the real value comes from being able to put an entire analytics pipeline into spark, right from the data ingestion and ETL processes, through the data wrangling and feature engineering processes through to training and execution of models. What’s more, with spark streaming and graphx spark can provide a much more complete analytics solution.

Learn Apache Spark

The collection of KnowledgeHut’s tutorials, guides and courses will help understand Spark as well as master it. These tutorials will help you dive deep into the underlying concepts of Spark, after which our certification training will help you to master the technology with real-world hands-on experience and instructor-led sessions. Feel free to have a look at our blogs to get a basic foundational knowledge of Spark.

If you are a professional who is keen on learning Apache Spark, then the following resources might help you do so:

Apache Spark Tutorials:





Apache Spark Videos:

What is Apache Spark by Mike Olson

What is Apache Spark?

Spark Tutorials for Beginners

Apache Spark Books:

Learning Spark: Lightning-Fast Big Data Analysis

Mastering Apache Spark

Spark in Action

Spark Cookbook

Mastering Apache Spark 2.x

Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis

If you wish to master the skills and features of Apache Spark, you can opt for training sessions to help you. Here is a list of few training institutes which will help you do so:

  • Udemy
  • KnowledgeHut
  • Edx
  • Coursera
  • Lynda

No,  you need not learn Hadoop first to learn Apache Spark.

A while back, the market trend was more towards Hadoop. But with time, there has been a variation in the trend as more and more industries are moving towards Spark as it is faster than Hadoop.

But at the same time, professionals who have the knowledge of Spark and Hadoop are best preferred in the IT industry and are highly paid as well.

Organisations use Apache Spark with ML algorithms. Spark library has a library labelled as MLib, which is a library for ML. This library of Apache Spark contains algorithms for the functions of classification, clustering, regression, dimensionality reduction, collaborative filtering, etc.

Apache Spark provides a powerful API for ML applications, with the goal to make practical ML easier. For the same, it has higher-level pipeline APIs and lower-level optimisation primitives.

With resources and tutorials available, it is easy to learn Apache Spark.

If you are already familiar with Scala, it’ll be easier for you as you already know the basic principles behind Spark and how it works.  

Moreover, if you wish to learn and get certified, you can opt for online training on Spark and Scala provided by KnowledgeHut. The curriculum of the course provided by them covers all the relevant topics which are required by the industry. Feel free to take a look at the course content of Apache Spark that KnowledgeHut provides.

Technical skills and knowledge required to become a Spark Professional are:-

  • Fundamental knowledge of any programming language
  • Basic understanding of any database, SQL and query language for databases
  • Working knowledge of Linux- or Unix-based systems (not mandatory)
  • Certification training as a Big Data Hadoop developer (recommended)

Apache Spark Certification and Training

If you wish to master the skills and features of Apache Spark, you can opt for training sessions to help you. The following is a list of the best institutes for Apache Spark and Scala Training:

  • Udemy
  • KnowledgeHut
  • Edx
  • Coursera
  • Lynda

Among these training providers, KnowledgeHut has gained traction from the industry experts because of the course features that we offer. You can get your doubts cleared by our trainers anytime through one-to-one discussion. The courses provided by us are up-to-date and are designed by our team of experts. Our courses provide reason-based training sessions which will help you gain not just theoretical but practical knowledge as well, making the process of learning more simplified.

Get yourself registered in any of the training institutes that provides Apache Spark and Scala certification. Participate and get certified.  

After successfully completing the Apache Spark and Scala course, you will be accorded with a certification of course completion from KnowledgeHut.

The certification provided by KnowledgeHut has lifetime validity. 

Career scope and Salary

Apache Spark is a Big Data framework which is in high demand. Spark provides streaming as well as batch capabilities, making it one of the biggest revolutionary changes in the environment of Big Data processing. Hence, it is an ideal framework for people and organizations who are looking for speed data analysis. Learning this framework will help you climb up the ladder of your career as nowadays more and more companies are eager to adopt Spark in their system.

According to the Data Science Salary Survey by O’Reilly, there exists a strong link between professionals who utilize Spark and Scala and the change in their salaries. The survey has shown that professionals with Apache Spark skills added $11,000 to the median or average salary, while Scala programming language affected an increase of $4000 to the bottom line of a professional’s salary. Apache Spark developers have been known to earn the highest average salary among other programmers utilizing ten of the most prominent Hadoop development tools. Real-time big data applications are going mainstream faster and enterprises are generating data at an unforeseen and rapid rate and this is the best time for professionals to learn Apache Spark online and help companies progress in complex data analysis.

Many companies have recognized the power of Spark and quickly started working on it. More and more companies are started using Spark. In upcoming days Spark will be most trending technology and there will be huge scope for Spark.

Apache Spark is the most advanced and popular product of Apache Community that provides the provision to work with the streaming data, has various Machine learning library, can work on structured and unstructured data, deal with graph, etc.

Apache Spark is one of the most active projects of Apache and its future scope will be long-lasting.

Spark users have exponentially increased and have progressively considered as future of Big Data Platform

After completing the Apache Spark and Scala course, you will be able to:

  • Understand the fundamentals of Scala programming language along with its features
  • Master the use of Resilient Distributed Datasets (RDD) to create applications in Spark
  • Get a proper understanding of the streaming features of Spark
  • Master the features of Spark ML programming and GraphX programming
  • Know the limitations of MapReduce and how Spark is used to overcome these limitations.
  • Learn and master how Spark can be installed as a standalone cluster
  • Acquire the knowledge of SQL using SparkSQL

According to Indeed.com, the average salary for "apache spark developer" ranges from approximately $97,915 per year for Developer to $133,184 per year for Data Engineer.

The average salary for big data analytics professionals in the non-managerial role is 10 lakhs INR, whilst managers can earn an average of a whopping 18 lakhs. These average salaries are big data skills like Hadoop and spark.

Scala and spark is great in demand in Big Data domain in India..

There is a huge demand for Apache Spark professionals today. With the increasing needs for rapid analysis and processing of Big Data, Spark, the in-memory stack, is being preferred as a faster and simpler alternative to MapReduce, either within a Hadoop framework or outside it. Therefore, Big Data enthusiasts with in-depth knowledge of Spark are hugely rewarded by employers.

Facebook, Twitter, Linkedin, Yahoo, eBay, Alibaba, Cloudspace, Fox Audience Network, Adobe, etc. are some of the companies who are hiring Spark developers regulars. there is also a great demand for spark developers and architects from the Retail industry, Manufacturing, Healthcare, Banking, and Finance industries.

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Apache Spark & Scala Course

Prerequisites for Spark are.

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

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 spark professionals is increasing.

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

You just need 4GB RAM to learn Spark.

Windows 7 or higher OS

i3 or higher processor

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.

Apache Spark is one of the ‘trending’ courses right now. Its myriad advantages including fast data processing, cheaper costs at adoption, and easy compatibility with other platforms have made it among the fastest technologies to be adopted for Big Data analytics. And considering that the demand for Data Analysts is hitting the roof, pursuing a course in Apache Scala and making a career in Data Analytics will be a most lucrative career decision for you. We bring you a well-rounded Apache Spark and Scala online tutorial that will hand hold you through the fundamentals of this technology and its use in Big Data Analytics. Through loads of exercises and hands-on tutorials, we’ll ensure that you are well versed with Spark and Scala.

KnowledgeHut’s training is intended to enable you to turn into an effective Apache Spark developer. After learning this course, you can acquire skills like-

  • Write Scala Programs to build Spark Application
  • Master the concepts of HDFS
  • Understand Hadoop 2.x Architecture
  • Understand Spark and its Ecosystem
  • Implement Spark operations on Spark Shell
  • Implement Spark applications on YARN (Hadoop)
  • Write Spark Applications using Spark RDD concepts
  • Learn data ingestion using Sqoop
  • Perform SQL queries using Spark SQL
  • Implement various machine learning algorithms in Spark MLlib API and Clustering
  • Explain Kafka and its components

The Big data explosion has created huge avenues for data analysis and has made it the most sought after career option. There is a huge demand for developers and engineers who can use tools such as Scala and Spark to derive business insights. This course will prepare you for everything you need to learn about Big Data while gaining practical experience in Scala and Spark.  After completing our course, you will become proficient in Apache Spark Development.

There are no restrictions but participants would benefit if they have basic computer knowledge.

Workshop Experience

All of the training programs conducted by us are interactive in nature and fun to learn as a great amount of time is spent on hands-on practical training, use case discussions, and quizzes. An extensive set of collaborative tools and techniques are used by our trainers which will improve your online training experience.

The Apache Kafka training conducted at KnowledgeHut is customized according to the preferences of the learner. The training is conducted in three ways:

Online Classroom training: You can learn from anywhere through the most preferred virtual live and interactive training   

Self-paced learning: This way of learning will provide you lifetime access to high-quality, self-paced e-learning materials designed by our team of industry experts

Team/Corporate Training: In this type of training, a company can either pick an employee or entire team to take online or classroom training. Flexible pricing options, standard Learning Management System (LMS), and enterprise dashboard are the add-on features of this training. Moreover, you can customize your curriculum based on your learning needs and also get post-training support from the expert during your real-time project implementation.  

The sessions that are conducted are 24 hours of live sessions, with 70+ hours MCQs and Assignments and 23 hours of hands-on sessions.

To attend the online Spark classes, the following is the list of essential requirements:

  • Operating system (Mac OS X, Windows or Linux)
  • A web browser like Chrome, FireFox
  • Proper internet connection

Yes, our lab facility at KnowledgeHut has the latest version of hardware and software and is very well-equipped. We provide Cloudlabs so that you can get a hands-on experience of the features of Apache Spark. Cloudlabs provides you with real-world scenarios can practice from anywhere around the globe. You will have an opportunity to have live hands-on coding sessions. Moreover, you will be given practice assignments to work on after your class.

Here at KnowledgeHut, we have Cloudlabs for all major categories like cloud computing, web development, and Data Science.

This Apache Spark and Scala training course have three projects, viz Adobe Analysis, Interactive Analysis, and Personalizing news pages for Web visitors in Yahoo.

  • Adobe Analysis: Adobe Analytics deal with huge amount of transactions in a day across major mobile and web properties. With the help of this project, you’ll come to know how Spark and Scala are useful in refactoring process.   
  • Interactive Analysis: Apache Spark has various features like Fog computing, IOT and MLib, GraphX etc. It’s most notable feature is its ability to support interactive analysis.
  • Personalizing news pages for Web visitors in Yahoo: Yahoo runs various Spark projects for different applications. Yahoo uses ML algorithms for personalizing news pages.

Scala, SBT, Apache Spark ,IntelliJ Idea Community Edition/Eclipse

The Learning Management System (LMS) provides you with everything that you need to complete your projects, such as the data points and problem statements. If you are still facing any problems, feel free to contact us.

After the completion of your course, you will be submitting your project to the trainer. The trainer will be evaluating your project. After a complete evaluation of the project and completion of your online exam, you will be certified a Spark and Scala professional.

Online Experience

We provide our students with Environment/Server access for their systems. This ensures that every student experiences a real-time experience as it offers all the facilities required to get a detailed understanding of the course.

If you get any queries during the process or the course, you can reach out to our support team.

The trainer who will be conducting our Apache Kafka certification has comprehensive experience in developing and delivering Spark applications. He has years of experience in training professionals in Apache Kafka. Our coaches are very motivating and encouraging, as well as provide a friendly learning environment for the students who are keen about learning and making a leap in their career.

Yes, you can attend a demo session before getting yourself enrolled for the Apache Spark training.

All our Online instructor-led training is an interactive session. Any point of time during the session you can unmute yourself and ask the doubts/ queries related to the course topics.

There are very few chances of you missing any of the Kafka training session at KnowledgeHut. But in case you miss any lecture, you have two options:

  • You can watch the online recording of the session
  • You can attend the missed class in any other live batch.

The online Apache Spark course recordings will be available to you with lifetime validity.

Yes, the students will be able to access the coursework anytime even after the completion of their course.

Opting for online training is more convenient than classroom training, adding quality to the training mode. Our online students will have someone to help them any time of the day, even after the class ends. This makes sure that people or students are meeting their end leaning objectives. Moreover, we provide our learners with lifetime access to our updated course materials.

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

This will be live interactive training led by an instructor in a virtual classroom.

We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.

Finance Related

We accept the following payment options:

  • PayPal
  • American Express
  • Citrus
  • MasterCard
  • Visa

KnowledgeHut offers a 100% money back guarantee if the candidates withdraw from the course right after the first session. To learn more about the 100% refund policy, visit our refund page.

If you find it difficult to cope, you may discontinue within the first 48 hours of registration and avail a 100% refund (please note that all cancellations will incur a 5% reduction in the refunded amount due to transactional costs applicable while refunding).  Refunds will be processed within 30 days of receipt of a written request for refund. Learn more about our refund policy here.

Typically, KnowledgeHut’s training is exhaustive and the mentors will help you in understanding the concepts in-depth.

However, if you find it difficult to cope, you may discontinue and withdraw from the course right after the first session as well as avail 100% money back.  To learn more about the 100% refund policy, visit our Refund Policy.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary up to 50% of the course fees.

To avail scholarships, feel free to get in touch with us at the following link:


The team shall send across the forms and instructions to you. Based on the responses and answers that we receive, the panel of experts takes a decision on the Grant. The entire process could take around 7 to 15 days

Yes, you can pay the course fee in instalments. To avail, please get in touch with us at https://www.knowledgehut.com/contact-us. Our team will brief you on the process of instalment process and the timeline for your case.

Mostly the instalments vary from 2 to 3 but have to be fully paid before the completion of the course.

Visit the following to register yourself for the Apache Spark and Scala Training:


You can check the schedule of the Apache Spark Training by visiting the following link:


We have a team of dedicated professionals known for their keen enthusiasm. As long as you have a will to learn, our team will support you in every step. In case of any queries, you can reach out to our 24/7 dedicated support at any of the numbers provided in the link below: https://www.knowledgehut.com/contact-us

We also have Slack workspace for the corporates to discuss the issues. If the query is not resolved by email, then we will facilitate a one-on-one discussion session with one of our trainers.

Yes, there will be other participants for all the online public workshops and would be logging in from different locations. Learning with different people will be an added advantage for you which will help you fill the knowledge gap and increase your network.

Have More Questions?

Apache Spark and Scala Course in Bangalore

Apache Spark and Scala Training in Bangalore

Bangalore is the capital of Karnataka. The city is also known for its parks, gardens, nightlife, etc. It is also the hub of India's high-tech industry and the second fastest growing metropolis. According to the economic estimations, Bangalore is ranked fourth or fifth largest productive metro region in India. Bangalore has one of the world?s most educated graduates and professionals. This is where courses, including the Apache Spark and Scala Training in Bangalore by KnowledgeHut Training Institute, will help you get a strong foothold in Bangalore's competitive IT environment.

Brief of the Apache Spark and Scala Course in Bangalore

Apache Spark is an open source, fast use, cost-effective, and sophisticated big data processing framework. 71% of Spark users also use Scala programming language according to a recent survey by Databricks. Scala is a JVM-based, secure, and expressive language whose extensions can easily be integrated into it. The Apache Spark and Scala Course in Bangalore is curated to help you materialise the knowledge of this platform and broaden your career opportunities in the Data Analysis field.

The bright side of the Apache Spark and Scala Certification in Bangalore

Scala programming language (designed by Typesafe's founder) helps you develop, code, and deploy things the right way through the best use of the Spark framework. Apache Spark is written in Scala programming language and most widely used by big data development developers to work on Spark projects because of its scalability to JVM. Developers say that using Scala will help to easily access and implement the newest features of Spark. So, enrol for the Apache Spark and Scala Certification in Bangalore.

The great advantages of Apache Spark and Scala Training in Bangalore by KnowledgeHut

KnowledgeHut Academy is a reputed training institute of professional courses and certifications. The Apache Spark and Scala Training in Bangalore is a great coaching program by KnowledgeHut to upgrade the knowledge about Scala. We offer face-to-face, virtual, and e-learning classroom training. We also provide 70 hours of MCQs and Assignments that can help in cracking any Scala-related certifications and exams. Our Scala training is centred on industry-use case studies curated by our experienced mentors. Scala tutors will provide complete insights into the Spark Ecosystem. And, also allow you to take an active part in practice sessions to understand the concepts better. We also provide 24/7 expert support to help you tackle any challenges in mastering Apache Spark.

Hurry up! Get in touch with us to schedule a demo session for the Apache Spark and Scala Training in Bangalore.