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Apache Spark and Scala Training in Boston, MA, United States

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
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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

1. Big Data Introduction

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

2. Introduction on Scala

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

3. Spark Introduction

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

4. Spark Framework & Methodologies

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

5. Spark Data Structure

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

6. Spark Ecosystem

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

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
Prerequisites
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

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the latest training!

Learn through Doing

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

Mentored by Industry Leaders

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.

Curriculum

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.

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

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

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                    

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

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

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

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    

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

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

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

Topics

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

Hands-on:

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.

Topics

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

Project

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.

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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.    

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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.

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Attended Full-Stack Development Bootcamp workshop in April 2021
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The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good experience.

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FAQs

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:

https://www.knowledgehut.com/contact-us

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:

https://www.knowledgehut.com/big-data/apache-spark-and-scala-training/schedule

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

https://www.knowledgehut.com/big-data/apache-spark-and-scala-training/schedule

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?

Mail Us support@knowledgehut.com

Apache Spark and Scala Course in Boston, MA

Apache Spark and Scala Training in Boston

Boston is the capital and largest city of Massachusetts. Boston is the ninth likeliest city globally to become the ?leading technology innovation hub outside of Silicon Valley over the next four years,? according to an annual survey of the tech industry that management consultancy KPMG conducts. The share of tech jobs as a proportion of all job listings is steadily increasing in Boston. So, to gain better opportunities, scale up with better skills with the Apache Spark and Scala Training in Boston .

About Apache Spark and Scala Certification in Boston

Apache Spark is the easy-to-use general engine for big data processing with built-in modules for streaming, SQL, Machine Learning (ML) and graph processing. Scala is a general-purpose programming language that supports functional programming and a strong static type system. Both Apache Spark and Scala are in demand skills for data engineers. Data scientists can benefit from learning spark when doing Exploratory Data Analysis (EDA), feature extraction and ML. Spark provides developers and engineers with Scala API. The KnowledgeHut institute?s Apache Spark and Scala Certification in Boston cover the Scala Spark API within Spark Core, Clustering, Spark SQL, Streaming, Machine Learning and more.

Benefits of Apache Spark and Scala Course in Boston

With the Apache Spark and Scala Course in Boston by KnowledgeHut academy, you will have performance benefits if you write your programs in Scala. Scala is based on the JVM which is native for Hadoop and so seamlessly works with it. Apache Spark is written in Scala and because of its scalability on the JVM, Scala programming is most prominently used programming language by big data developers for working on Spark projects. The performance achieved using Scala is better than many other traditional data analysis tools like R or Python. As a result, individuals having Apache Spark and Scala skills are in demand in the industry and you will be the chosen one in the job market, which will also help you to improve your career prospects.

The KnowledgeHut way o Apache Spark and Scala Training in Boston

The Apache Spark and Scala Training in Boston by the KnowledgeHut training institute is designed to provide you a head-start into Apache Spark and Scala by coaching you on the various ideas to use Big Data using Spark, required concepts of Scala and much more by our professional trainers who are very well experienced in the field. The course has been instrumental in laying the foundation for the beginners, both in terms of conceptual concepts and practical sessions with hands-on experience to work on 3 projects at the end of the course, which will be reviewed by professional developers and feedback provided for further improvement.

Enrol into the Apache Spark and Scala Training in Boston and become Big data experts with these skills!!

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