Intellipaat’s Spark Master’s Training is designed by experts in the industry. In this certification program, you will come across the significant concepts and modules such as Python, Python libraries and frameworks, Spark, Pyspark, Scala, etc. and become proficient in the same. Further, in this certification program, you will attain all the necessary skills to become a professional in this domain. Throughout the entire duration of this training program, we will offer 24 hours of online assistance wherein you can effortlessly clear all your queries. Moreover, you will have lifetime access to the complete training resources. Besides, once you complete the course, you will work on exercises and projects that will help you gain experience in solving problems in the real-world. At Intellipaat, we aim to make you job-ready by conducting a number of mock interviews and also helping you create your resume. Finally, you will receive the Spark Master’s certification from Intellipaat that is collaborated with Microsoft and IBM.
Online Classroom training
Self Paced Training
This in a comprehensive Spark master’s course that offers an easy-to-learn approach in the overall course of this training including the execution of Scala code, concepts of classes in Scala, mutable vs immutable collections, Spark applications, etc. After you complete this course, you will become proficient in the core concepts of Spark and go for corresponding job roles.
In this course, you will cover the following concepts:
Professionals who must definitely sign up for this course include:
To take up this course, you need to have a good knowledge of Python or any other programming language. Besides, you must have a good understanding of SQL or any other database query language. Moreover, having experience working with UNIX or Linux systems can be beneficial for you.
Introduction to Java basics, the various components of Java language, data types, operations, compilation process, class files, loops, conditions, benefits of Java over other programming languages.
What is object-oriented programming, the concept of abstraction, attributes, methods, constructors, inheritance, encapsulation, and polymorphism.
Writing codes in Java, using wrapper classes, applet program UI programs, using io.lang package and deep dive into Java Collections including Vector, ArrayList, TreeSet, HashMap.
What is a Java package, Java interfaces, the various access specifiers, scope specifiers, exception handling in Java, introduction to multi-threading in Java, extending the thread class, synchronizing the thread.
The fundamentals of Extensible Markup Language, its uses in storing and transferring data, writing a XML file, making sense of the XML file using DOM and SAX parsers in Java.
Introduction to Java Database Connectivity, fundamentals of SQL like connect, select, insert, update, the various drivers of JDBC, writing a program to communicate with database using JDBC, the architecture of JDBC, how to do a batch processing transaction.
What is a Java Servlet, extending the capability of the web server, dynamic Java web content technology, the HTTP and generic Servlets, session tracking and filter, forward and include Servlet request dispatchers.
Fundamentals of Java Server Page, writing a code using JSP, the architecture of JSP, declarative, expression and scripts tags, JSP and JDBC.
Database interaction with Hibernate, various operations in databases like insert, delete, update, collections and inheritance, HQL, Hibernate caching, creating code with the Spring framework, auto wiring and dependency injection, Spring bean scope and post processor, integration of Spring with Hibernate framework.
Spring framework Aspect Oriented Programming, database commit and rollback operations, AJAX framework for interacting with server, design patterns in Java Enterprise Edition.
The fundamentals of Service Oriented Architecture, importance of SOA, how SOA is independent of vendor, technology, product, deploying SOA with web services, XML, Simple Object Access Protocol (SOAP), Universal Description, Discovery, and Integration(UDDI) and Web Services Description Language (WSDL).
1.1 Introducing Scala
1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
1.3 Scala REPL, lazy values, and control structures in Scala
1.4 Directed Acyclic Graph (DAG)
1.5 First Spark application using SBT/Eclipse
1.6 Spark Web UI
1.7 Spark in the Hadoop ecosystem.
2.1 The importance of Scala
2.2 The concept of REPL (Read Evaluate Print Loop)
2.3 Deep dive into Scala pattern matching
2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis
3.1 Learning about the Scala Interpreter
3.2 Static object timer in Scala and testing string equality in Scala
3.3 Implicit classes in Scala
3.4 The concept of currying in Scala
3.5 Various classes in Scala
4.1 Learning about the Classes concept
4.2 Understanding the constructor overloading
4.3 Various abstract classes
4.4 The hierarchy types in Scala
4.5 The concept of object equality
4.6 The val and var methods in Scala
5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern
6.1 Understanding traits in Scala
6.2 The advantages of traits
6.3 Linearization of traits
6.4 The Java equivalent
6.5 Avoiding of boilerplate code
7.1 Implementation of traits in Scala and Java
7.2 Handling of multiple traits extending
8.1 Introduction to Scala collections
8.2 Classification of collections
8.3 The difference between iterator and iterable in Scala
8.4 Example of list sequence in Scala
9.1 The two types of collections in Scala
9.2 Mutable and immutable collections
9.3 Understanding lists and arrays in Scala
9.4 The list buffer and array buffer
9.6 Queue in Scala
9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala
10.1 Introduction to Scala packages and imports
10.2 The selective imports
10.3 The Scala test classes
10.4 Introduction to JUnit test class
10.5 JUnit interface via JUnit 3 suite for Scala test
10.6 Packaging of Scala applications in the directory structure
10.7 Examples of Spark Split and Spark Scala
11.1 Introduction to Spark
11.2 Spark overcomes the drawbacks of working on MapReduce
11.3 Understanding in-memory MapReduce
11.4 Interactive operations on MapReduce
11.5 Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
11.6 The overview of Spark and how it is better than Hadoop
11.7 Deploying Spark without Hadoop
11.8 Spark history server and Cloudera distribution
12.1 Spark installation guide
12.2 Spark configuration
12.3 Memory management
12.4 Executor memory vs. driver memory
12.5 Working with Spark Shell
12.6 The concept of resilient distributed datasets (RDD)
12.7 Learning to do functional programming in Spark
12.8 The architecture of Spark
13.1 Spark RDD
13.2 Creating RDDs
13.3 RDD partitioning
13.4 Operations and transformation in RDD
13.5 Deep dive into Spark RDDs
13.6 The RDD general operations
13.7 Read-only partitioned collection of records
13.8 Using the concept of RDD for faster and efficient data processing
13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions
14.1 Understanding the concept of key-value pair in RDDs
14.2 Learning how Spark makes MapReduce operations faster
14.3 Various operations of RDD
14.4 MapReduce interactive operations
14.5 Fine and coarse-grained update
14.6 Spark stack
15.1 Comparing the Spark applications with Spark Shell
15.2 Creating a Spark application using Scala or Java
15.3 Deploying a Spark application
15.4 Scala built application
15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
15.6 Creating an application using SBT
15.7 Deploying an application using Maven
15.8 The web user interface of Spark application
15.9 A real-world example of Spark
15.10 Configuring of Spark
16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions
17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey
18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
1. Building a Recommendation Engine
19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka
1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka
20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming
21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems
22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine
23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions
Project 01: Movie Recommendation
Topics: This is a project wherein you will gain hands-on experience in deploying Apache Spark for the movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a Machine Learning library. You will understand how to deploy collaborative filtering, clustering, regression and dimensionality reduction in MLlib. Upon the completion of the project, you will gain experience in working with streaming data, sampling, testing and statistics.
Project 02: Twitter API Integration for Tweet Analysis
Topics: With this project, you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages, like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.
Project 03: Data Exploration Using Spark SQL – Wikipedia Dataset
Topics: This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real-time analysis of data, performing batch analysis, deploying Machine Learning, creating visualizations and processing of graphs.
1.1 Introduction to Python Language
1.2 Features, the advantages of Python over other programming languages
1.3 Python installation – Windows, Mac & Linux distribution for Anaconda Python
1.4 Deploying Python IDE
1.5 Basic Python commands, data types, variables, keywords and more
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac.
2.1 Built-in data types in Python
2.2 Learn classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug
2.3 Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
2.4 Loop and control statements while, for, if, break, else, continue.
Hands-on Exercise –
1. Write your first Python program
2. Write a Python Function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. create an object
7. Write a for loop
3.1 How to write OOP concepts program in Python
3.2 Connecting to a database
3.3 Classes and objects in Python
3.4 OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation
3.5 Python functions, return types and parameters
3.6 Lambda expressions
Hands-on Exercise –
1. Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS.
4.1 Understanding the Database, need of database
4.2 Installing MySQL on windows
4.3 Understanding Database connection using Python.
Hands-on Exercise – Demo on Database Connection using python and pulling the data.
5.1 Introduction to arrays and matrices
5.2 Broadcasting of array math, indexing of array
5.3 Standard deviation, conditional probability, correlation and covariance.
Hands-on Exercise –
1. How to import NumPy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers
4. Calculating correlation between two variables.
6.1 Introduction to SciPy
6.2 Functions building on top of NumPy, cluster, linalg, signal, optimize, integrate, subpackages, SciPy with Bayes Theorem.
Hands-on Exercise –
1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset.
7.1 How to plot graph and chart with Python
7.2 Various aspects of line, scatter, bar, histogram, 3D, the API of MatPlotLib, subplots.
Hands-on Exercise –
1. Deploying MatPlotLib for creating Pie, Scatter, Line, Histogram.
8.1 Introduction to Python dataframes
8.2 Importing data from JSON, CSV, Excel, SQL database, NumPy array to dataframe
8.3 Various data operations like selecting, filtering, sorting, viewing, joining, combining
Hands-on Exercise –
1. Working on importing data from JSON files
2. Selecting record by a group
3. Applying filter on top, viewing records
9.1 Introduction to Exception Handling
9.2 Scenarios in Exception Handling with its execution
9.3 Arithmetic exception
9.4 RAISE of Exception
9.5 What is Random List, running a Random list on Jupyter Notebook
9.6 Value Error in Exception Handling.
Hands-on Exercise –
1. Demo on Exception Handling with an Industry-based Use Case.
10.1 Introduction to Thread, need of threads
10.2 What are thread functions
10.3 Performing various operations on thread like joining a thread, starting a thread, enumeration in a thread
10.4 Creating a Multithread, finishing the multithreads.
10.5 Understanding Race Condition, lock and Synchronization.
Hands-on Exercise –
1. Demo on Starting a Thread and a Multithread and then perform multiple operations on them.
11.1 Intro to modules in Python, need of modules
11.2 How to import modules in python
11.3 Locating a module, namespace and scoping
11.4 Arithmetic operations on Modules using a function
11.5 Intro to Search path, Global and local functions, filter functions
11.6 Python Packages, import in packages, various ways of accessing the packages
11.7 Decorators, Pointer assignments, and Xldr.
Hands-on Exercise –
1. Demo on Importing the modules and performing various operation on them using arithmetic functions
2. Importing various packages and accessing them and then performing different operations on them.
12.1 Introduction to web scraping in Python
12.2 Installing of beautifulsoup
12.3 Installing Python parser lxml
12.4 Various web scraping libraries, beautifulsoup, Scrapy Python packages
12.5 Creating soup object with input HTML
12.6 Searching of tree, full or partial parsing, output print
Hands-on Exercise –
1. Installation of Beautiful soup and lxml Python parser
2. Making a soup object with input HTML file
3. Navigating using Py objects in soup tree.
Project 01 : Analyzing the Naming Pattern Using Python
Industry : General
Problem Statement : How to analyze the trends and the most popular baby names
Topics : In this Python project, you will work with the United States Social Security Administration (SSA) which has made data on the frequency of baby names from 1880 to 2016 available. The project requires analyzing the data considering different methods. You will visualize the most frequent names, determine the naming trends and come up with the most popular names for a certain year.
Project 02 : – Python Web Scraping for Data Science
In this project, you will be introduced to the process of web scraping using Python. It involves installation of Beautiful Soup, web scraping libraries, working on common data and page format on the web, learning the important kinds of objects, Navigable String, deploying the searching tree, navigation options, parser, search tree, searching by CSS class, list, function and keyword argument.
Project 03 : Predicting Customer Churn in Telecom Company
Industry – Telecommunications
Problem Statement – How to increase the profitability of a telecom major by reducing the churn rate
Topics :In this project, you will work with the telecom company’s customer dataset. This dataset includes subscribing telephone customer’s details. Each of the column has data on phone number, call minutes during various times of the day, the charges incurred, lifetime account duration and whether the customer has churned some services by unsubscribing it. The goal is to predict whether a customer will eventually churn or not.
RDBMS, types of relational databases, challenges of RDBMS, NoSQL database, its significance, how NoSQL suits Big Data needs, introduction to MongoDB and its advantages, MongoDB installation, JSON features, data types and examples
Installing MongoDB, basic MongoDB commands and operations, MongoChef (MongoGUI) installation and MongoDB data types
Hands-on Exercise: Install MongoDB and install MongoChef (MongoGUI)
The need for NoSQL, types of NoSQL databases, OLTP, OLAP, limitations of RDBMS, ACID properties, CAP Theorem, Base property, learning about JSON/BSON, database collection and documentation, MongoDB uses, MongoDB write concern—acknowledged, replica acknowledged, unacknowledged, journaled—and Fsync
Hands-on Exercise: Write a JSON document
Understanding CRUD and its functionality, CRUD concepts, MongoDB query and syntax and read and write queries and query optimization
Hands-on Exercise:Use insert query to create a data entry, use find query to read data, use update and replace queries to update and use delete query operations on a DB file
Concepts of data modelling, difference between MongoDB and RDBMS modelling, model tree structure, operational strategies, monitoring and backup
Hands-on Exercise: Write a data model tree structure for a family hierarchy
In this module, you will learn MongoDB® Administration activities such as health check, backup, recovery, database sharding and profiling, data import/export, performance tuning, etc.
Hands-on Exercise: Use shard key and hashed shard keys, perform backup and recovery of a dummy dataset, import data from a CSV file and export data to a CSV file
Concepts of data aggregation and types and data indexing concepts, properties and variations
Hands-on Exercise: Do aggregation using pipeline, sort, skip and limit and create index on data using single key and using multi-key
Understanding database security risks, MongoDB security concept and security approach and MongoDB integration with Java and Robomongo
Hands-on Exercise: MongoDB integration with Java and Robomongo
Implementing techniques to work with variety of unstructured data like images, videos, log data and others and understanding GridFS MongoDB file system for storing data
Hands-on Exercise: Work with variety of unstructured data like images, videos, log data and others
Project: Working with the MongoDB Java Driver
Problem Statement: How to create table for video insertion using Java
Topics: In this project, you will work with MongoDB Java Driver and become proficient in creating a table for inserting video using Java programming. You will work with collections and documents and understand the read and write basics of MongoDB database and the Java virtual machine libraries.
Multiple Linux installations, automated installation using Kick start, deploying it using web server, installation media and Kick start configuration files.
Linux Package which is Pre-built set of programs, installation of Packages, its libraries and the dependencies, understanding the low-level and high-levels tools needed, configuring Linux Yum Server, understanding the Yum repository Server-Client system.
Deep dive into Linux Services, the important system files, utilities, directories.
Learning about SystemD, the Linux-specific system and service manager, understanding what are the programs that run when Linux loads, familiarising with the systemctl commands.
Linux user management, groups, attributes and file permissions, granting permission based on tasks to be performed, various advanced user administration features, setting user and group disk space quotas, Linux file system quotas.
Managing the Linux File System, understanding the Logical Volume Management, allocating disks, stripping, mirroring, resizing, logical volumes, deploying LVM for setting hard disks for physical volumes.
Understanding the concept of RAID data storage virtualization, the RAID software management tool, learning about Linux Kernel with RAID Support, implementing the software RAID in Linux.
Learning about the Server Configuration in Linux, the FTP/SFTP, the HTTP Server/Client Systems configuration.
Understanding the Samba Open Source tool, the Simple Mail Transfer Protocol, configuring the Samba Server and SMTP with Linux.
Understanding the basics of firewall in Linux, deploying the firewall and iptables in Linux which is a rule-based firewall system in Linux, testing the firewall rules.
Understanding how to configure databases in Linux, working with MySQL database and MariaDB database for configuring it with Linux.
Learning about the various control panels in Linux, its various uses and applications in various services, deploying of these control panels to manage the Linux servers.
Free Career Counselling
Intellipaat’s comprehensive Spark Master certification is led by industry experts from India and the United States. The video sessions available in this training will help you grasp all the significant concepts of Spark Master and acquire the necessary skill-sets. You will also have an online platform in which you can ask and clear any of your doubts on Spark Master at any time throughout the course. Besides, you will also get acquainted with like-minded individuals who are in the same training program and are looking into the same career field.
After the course, you will gain hands-on experience by working in various industry-based projects that will substantiate your learning.
Once you execute all the projects successfully, you will be awarded Intellipaat’s Spark Master Certification which is in collaboration with Microsoft and IBM. Our main aim is to prepare you for job interviews via mock interviews and also resume creation and help to find a lucrative job in a reputed organization.
Our Alumni works at top 3000+ companies
Intellipaat offers one of the best online Master’s courses for Spark. This course aims to help you master all the significant concepts of Spark, Python, Scala, and PySpark. Further, this online training will assist you in acquiring all the necessary skills required to become a Spark professional. Besides, throughout the duration of the course, we will provide 24-hour support.
You will have complete access to the course material and video lectures at no additional cost. After the course, you will work on assignments and real-time projects on certain modules that will give you an idea of your grasp of the concepts learned. You will also receive Spark certification not only from Intellipaat but also from IBM and Microsoft. Additionally, we will also provide job assistance via mock interviews, along with, resume preparation.
At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.
Intellipaat is offering the 24/7 query resolution, and you can raise a ticket with the dedicated support team at anytime. You can avail of the email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our trainers.
You would be glad to know that you can contact Intellipaat support even after the completion of the training. We also do not put a limit on the number of tickets you can raise for query resolution and doubt clearance.
Intellipaat offers self-paced training to those who want to learn at their own pace. This training also gives you the benefits of query resolution through email, live sessions with trainers, round-the-clock support, and access to the learning modules on LMS for a lifetime. Also, you get the latest version of the course material at no added cost.
Intellipaat’s self-paced training is 75 percent lesser priced compared to the online instructor-led training. If you face any problems while learning, we can always arrange a virtual live class with the trainers as well.
Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.
You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.
Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.
Apparently, no. Our job assistance program is aimed at helping you land in your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.