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Welcome to Petaa Bytes

Petaa Bytes is the leading Training provider of Apache Hadoop and related technologies with classroom and online Training around the world. We offer the most comprehensive suite of courses Data Science Course in Mumbai, Hadoop Training in Mumbai to address the Hadoop objectives of every Data professional: Be it Developer, Administrator or Data Scientist. Trainer at Petaa Bytes are very experienced and certified. At Petaa Bytes, We update curriculum regularly to reflect the state of the art in Big Data and delivered by full-time technical instructors.

Big-Data and Hadoop Developer Training in Mumbai and Navi Mumbai, India, Data Science Course in Mumbai, Hadoop Training in Mumbai, Hadoop Online Training in Mumbai, MongoDB Training in Mumbai, Spark Training in Mumbai, Python Programming Training in Mumbai, Best Hadoop Training in Mumbai, MongoDB Training in Navi Mumbai and Airoli. Python Training in Airoli, Python Training in Navi Mumbai.

We train you to see beyond Hadoop and Big Data basic concepts. Petaa-Bytes is one of the outstanding ONLINE and CLASSROOM TRAINING Institute by the finest trainers in the market. We also provide Corporate Training. We offer Online Training to the learners in all parts of the world with the implementation of modern technologies for Training in Mumbai.



Data Science Course in Mumbai

Data Science Course is the leading Training Institute courses in Mumbai and Data Science Course is the study of the generalizable extraction of knowledge from Data Science Course, yet the keyword is science Course. It incorporates varying elements and builds on techniques and theories from many fields of Data Science, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, Data Science engineering, pattern recognition and learning, visualization, uncertainty modeling, Data Science warehousing, and high performance computing with the goal of extracting meaning from Data and creating Data products in Mumbai. Data Science Course is not restricted to only big Data science, although the fact that Data is scaling up makes big Data an important aspect of Data Science Course in Mumbai as well Hadoop Training Course.

Why Take This Data Science Course and Hadoop Training Course

Petaa-Bytes provide you the opportunity to work through a Data science project end to end, from analyzing a Dataset to visualizing and communicating your Data Science analysis in Mumbai. Through working on the class project, Team will be exposed and understand the specialist skills that are needed to become a Data Scientist yourself Training in Mumbai.

Features
Segmentation and Clustering
Model Building and Validation
Machine Learning: Unsupervised Learning
Classification Models
Creating an Analytical Dataset

This course will provide you with the basis of the statistical programming language R, the lingua franca of statistics, which will allow you to write programs that read, manipulate and analyze quantitative Data Science Course in Mumbai. We will explain the installation of the language; You will also see an introduction to the graphics base systems and the ggplot2 graphics package, to visualize this Data. In addition, you'll also cover the use of one of the most popular IDEs among the R user community, called RStudio.

Objective

At the end of the Data Science Course: You will

use the R programming language in order to manipulate Data, generate statistical analysis and graphical representation, through quantitative Data processing in Mumbai.

Form of Data Science Course work in Mumbai

This course seeks to introduce you into the statistical programming language R, a computational language designed for the statistical analysis of Data in Mumbai. This course is aimed at students and professionals interested in using this tool to read, manipulate, analyze and graph Data in Mumbai.

You will use a very popular IDE to work with the R language, called RStudio, which has become the de facto IDE to program in R.

In each module, you will find videos that will guide you in the installation of the tools to use, As well as explanations of the basic operations and the specific elements that offer a deeper handling of the language for Data Science Course Mumbai. You will also find some bibliographical references to delve into the topic that is of interest to you for Course in Mumbai.

To complement the lessons, you will practice language practices, which will have value for the evaluation. Syllabus
Installation of Tools
A small introduction to what we will see in the course and the installation of the tools to use in it, for Windows, Linux, and Mac OS operating systems.

Introduction to Language
In this first week, you will have your first steps in language, you will understand objects, classes, Data types and you will be able to read and write Data to different sources and types of files.

Language Usage
This week you will learn how to use control structures to streamline repetitive tasks, create functions to perform specific or complex tasks, scope rules, handling dates and date variables, the importance of using Vectorized operations to make your code faster, Why it is important to write code with a good style.

Approach to the Graphics System of R
This week you will first know a family of vectorized functions, which will allow you to perform operations with very few lines of code, then you will know the graphics system and the functions that allow you to make modifications in it.

Regular Expressions, Graphing with ggplot2 and Simulation
In the last week, you will learn about regular expressions and how to use them with R, plot with a ggplot2 graphics package, understand how to find errors and fix them and carry out the simulation. Which will allow you to perform operations with very few lines of code, then you will know the graphics system and the functions that allow you to make modifications in it. Regular Expressions, Graphing with ggplot2 and Simulation In the last week you will learn about regular expressions and how to use them with R, plot with a ggplot2 graphics package, understand how to find errors and fix them and carry out the simulation. Which will allow you to perform operations with very few lines of code, then you will know the graphics system and the functions that allow you to make modifications in it. Regular Expressions, Graphing with ggplot2 and Simulation In the last week you will learn about regular expressions and how to use them with R, plot with a ggplot2 graphics package, understand how to find errors and fix them and carry out the simulation.


Hadoop Training in Mumbai


Hadoop Training Introduction

    Hadoop Training in Mumbai

    Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    This brief tutorial provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System

    Audience for Big Data Hadoop Training

    This tutorial has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

    Prerequisites for Big Data Hadoop Training

    Before you start proceeding with this tutorial, we assume that you have prior exposure to Core Java, database concepts, and any of the Linux operating system flavors.

    Hadoop Training - Big Data Overview

    Due to the advent of new technologies, devices, and communication means for social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed Hadoop Training in Mumbai, it is being neglected.

     

    90% of the world’s data was generated in the last few years.

    What is Big Data?

    Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, techniques, and frameworks

    What Comes Under Big Data?

    Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.

    ·        Black Box Data: It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.

    ·        Social Media Data: Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.

    ·        Stock Exchange Data: The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.

    ·        Power Grid Data: The power grid data holds information consumed by a particular node with respect to a base station.

    ·        Transport Data: Transport data includes model, capacity, distance and availability of a vehicle.

     

    ·        Search Engine Data: Search engines retrieve lots of data from different databases.

    [caption id="attachment_529" align="alignnone" width="300"]bigdata hadoop Training in Mumai big data Hadoop Training in Mumbai[/caption]

    Thus Big Data includes huge volume, high velocity, and an extensible variety of data. The data in it will be of three types.

    • Structured data: Relational data.
    • Semi Structured data: XML data.
    • Unstructured data: Word, PDF, Text, Media Logs.

     

    Benefits of Big Data Hadoop Training in Mumbai

    Big data is really critical to our life and its emerging as one of the most important technologies in modern world. Follow are just few benefits which are very much known to all of us:

    ·        Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.

    ·        Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.

     

    ·        Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.

    Big Data Hadoop Technologies

    Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.

    To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.

    There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology:

    Operational Big Data

    This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

    NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

    Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

    Analytical Big Data

    This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.

    MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.

    These two classes of technology are complementary and frequently deployed together.

    Operational vs. Analytical Systems

     

    Operational

    Analytical

    Latency

    1 ms - 100 ms

    1 min - 100 min

    Concurrency

    1000 - 100,000

    1 - 10

    Access Pattern

    Writes and Reads

    Reads

    Queries

    Selective

    Unselective

    Data Scope

    Operational

    Retrospective

    End User

    Customer

    Data Scientist

    Technology

    NoSQL

    MapReduce, MPP Database

    Big Data Hadoop Challenges

    The major challenges associated with big data are as follows:

    • Capturing data
    • Curation
    • Storage
    • Searching
    • Sharing
    • Transfer
    • Analysis
    • Presentation

    To fulfill the above challenges, organizations normally take the help of enterprise servers.

    Traditional Approach

    In this approach, an enterprise will have a computer to store and process big data. Here data will be stored in an RDBMS like Oracle Database, MS SQL Server or DB2 and sophisticated software can be written to interact with the database, process the required data and present it to the users for analysis purpose.

    [caption id="attachment_531" align="alignnone" width="300"]Big Data Hadoop Training in Mumbai Big Data Hadoop Training in Mumbai[/caption]

    Limitation

    This approach works well where we have less volume of data that can be accommodated by standard database servers, or up to the limit of the processor which is processing the data. But when it comes to dealing with huge amounts of data, it is really a tedious task to process such data through a traditional database server.

    Google’s Solution

    Google solved this problem using an algorithm called MapReduce. This algorithm divides the task into small parts and assigns those parts to many computers connected over the network, and collects the results to form the final result dataset.

    [caption id="attachment_530" align="alignnone" width="300"]Big Data Hadoop online Training in Mumbai Big Data Hadoop online Training in Mumbai[/caption]

    Above diagram shows various commodity hardware which could be single CPU machines or servers with higher capacity.

    Hadoop Training in Mumbai

    Doug Cutting, Mike Cafarella, and the team took the solution provided by Google and started an Open Source Project called HADOOP in 2005 and Doug named it after his son's toy elephant. Now Apache Hadoop is a registered trademark of the Apache Software Foundation.

    Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel on different CPU nodes. In short, the Hadoop framework is capable enough to develop applications capable of running on clusters of computers and they could perform complete statistical analysis for huge amounts of data.

    [caption id="attachment_532" align="alignnone" width="300"]Hadoop Framework Training Centre in Mumbai Hadoop Framework Training Centre in Mumbai[/caption]

    Hadoop Training - Introduction

    Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

    Hadoop Architecture

    Hadoop framework includes following four modules:

    • Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provide filesystem and OS level abstractions and contain the necessary Java files and scripts required to start Hadoop.
    • Hadoop YARN: This is a framework for job scheduling and cluster resource management.
    • Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
    • Hadoop MapReduce: This is a YARN-based system for parallel processing of large data sets.

    We can use following diagram to depict these four components available in Hadoop framework.

    [caption id="attachment_533" align="alignnone" width="255"]Hadoop Introduction Hadoop Introduction[/caption]

     

    Since 2012, the term "Hadoop" often refers not just to the base modules mentioned above but also to the collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark etc

    MapReduce

    Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

    The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:

    • The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).
    • The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.

    Typically both the input and the output are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

    The MapReduce framework consists of a single master JobTracker and one slave TaskTracker per cluster node. The master is responsible for resource management, tracking resource consumption/availability and scheduling the jobs component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves TaskTracker execute the tasks as directed by the master and provide task status information to the master periodically.

    The JobTracker is a single point of failure for the Hadoop MapReduce service which means if JobTracker goes down, all running jobs are halted.

    Hadoop Distributed File System

    Hadoop can work directly with any mountable distributed file system such as Local FS, HFTP FS, S3 FS, and others, but the most common file system used by Hadoop is the Hadoop Distributed File System (HDFS).

    The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on large clusters (thousands of computers) of small computer machines in a reliable, fault-tolerant manner.

    HDFS uses a master/slave architecture where master consists of a single NameNode that manages the file system metadata and one or more slave DataNodes that store the actual data.

    A file in an HDFS namespace is split into several blocks and those blocks are stored in a set of DataNodes. The NameNode determines the mapping of blocks to the DataNodes. The DataNodes takes care of reading and writes operation with the file system. They also take care of block creation, deletion, and replication based on an instruction given by NameNode.

    HDFS provides a shell like any other file system and a list of commands are available to interact with the file system. These shell commands will be covered in a separate chapter along with appropriate examples.

    How Does Hadoop Work?

    Stage 1

    A user/application can submit a job to the Hadoop (a Hadoop job client) for required process by specifying the following items:

    1. The location of the input and output files in the distributed file system.
    2. The Java classes in the form of jar file containing the implementation of the map and reduce functions.
    3. The job configuration by setting different parameters specific to the job.

    Stage 2

    The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

    Stage 3

    The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.

    Advantages of Hadoop

    • Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
    • Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
    • Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
    • Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms since it is Java based.

     

    Hadoop History

    As the World Wide Web grew in the late 1900s and early 2000s, search engines, and indexes were created to help locate relevant information amid the text-based content. In the early years, search results were returned by humans. But as the web grew from dozens to millions of pages, automation was needed. Web crawlers were created, many of university-led research projects and search engine start-ups took off (Yahoo, AltaVista, etc.)

    How Is Hadoop Being Used?

    Going beyond its original goal of searching millions (or billions) of web pages and returning relevant results, many organizations are looking to Hadoop as their next big data platform. Popular uses today include:

    Low-cost storage and data archive

    The modest cost of commodity hardware makes Hadoop useful for storing and combining data such as transactional, social media, sensor, machine, scientific, click streams, etc. The low-cost storage lets you keep information that is not deemed currently critical but that you might want to analyze later.

    Sandbox for discovery and analysis

    Because Hadoop was designed to deal with volumes of data in a variety of shapes and forms, it can run analytical algorithms. Big data analytics on Hadoop can help your organization operate more efficiently, uncover new opportunities and derive next-level competitive advantage. The sandbox approach provides an opportunity to innovate with minimal investment.

    Data lake

    Data lakes support storing data in its original or exact format. The goal is to offer a raw or unrefined view of data to data scientists and analysts for discovery and analytics. It helps them ask new or difficult questions without constraints. Data lakes are not a replacement for data warehouses. In fact, how to secure and govern data lakes is a huge topic for IT. They may rely on data federation techniques to create a logical data structures.

    Complement your data warehouse

    We're now seeing Hadoop beginning to sit beside data warehouse environments, as well as certain data sets being offloaded from the data warehouse into Hadoop or new types of data going directly to Hadoop. The end goal for every organization is to have a right platform for storing and processing data of different schema, formats, etc. to support different use cases that can be integrated at different levels.

    IoT and Hadoop

    Things in the IoT need to know what to communicate and when to act. At the core of the IoT is a streaming, always on torrent of data. Hadoop is often used as the data store for millions or billions of transactions. Massive storage and processing capabilities also allow you to use Hadoop as a sandbox for discovery and definition of patterns to be monitored for prescriptive instruction. You can then continuously improve these instructions, because Hadoop is constantly being updated with new data that doesn’t match previously defined patterns.

    Building a recommendation engine in Hadoop

    One of the most popular analytical uses by some of Hadoop's largest adopters is for web-based recommendation systems. Facebook – people you may know. LinkedIn – jobs you may be interested in. Netflix, eBay, Hulu – items you may want. These systems analyze huge amounts of data in real time to quickly predict preferences before customers leave the web page.

     

    How: A recommender system can generate a user profile explicitly (by querying the user) and implicitly (by observing the user’s behavior) – then compares this profile to reference characteristics (observations from an entire community of users) to provide relevant recommendations. Petaa-Bytes provides a number of techniques and algorithms for creating a recommendation system, ranging from basic distance measures to matrix factorization and collaborative filtering – all of which can be done within Hadoop.

    How It Works and a Hadoop Glossary

    Currently, four core modules are included in the basic framework from the Apache Foundation:

     

    Hadoop Common – the libraries and utilities used by other Hadoop modules.

    Hadoop Distributed File System (HDFS) – the Java-based scalable system that stores data across multiple machines without prior organization.

    YARN – (Yet Another Resource Negotiator) provides resource management for the processes running on Hadoop.

    MapReduce – a parallel processing software framework. It is comprised of two steps. Map step is a master node that takes inputs and partitions them into smaller subproblems and then distributes them to worker nodes. After the map step has taken place, the master node takes the answers to all of the subproblems and combines them to produce output.

    Other software components that can run on top of or alongside Hadoop and have achieved top-level Apache project status include:

    Ambari

    A web interface for managing, configuring and testing Hadoop services and components.

    Cassandra

    A distributed database system.

    Flume

    Software that collects, aggregates and moves large amounts of streaming data into HDFS.

    HBase

    A nonrelational, distributed database that runs on top of Hadoop. HBase tables can serve as input and output for MapReduce jobs.

    HCatalog

    A table and storage management layer that helps users share and access data.

    Hive

    A data warehousing and SQL-like query language that presents data in the form of tables. Hive programming is similar to database programming.

    Oozie

    A Hadoop job scheduler.

    Pig

    A platform for manipulating data stored in HDFS that includes a compiler for MapReduce programs and a high-level language called Pig Latin. It provides a way to perform data extractions, transformations and loading, and basic analysis without having to write MapReduce programs.

    Solr

    A scalable search tool that includes indexing, reliability, central configuration, failover and recovery.

    Spark

    An open-source cluster computing framework with in-memory analytics.

    Sqoop

    A connection and transfer mechanism that moves data between Hadoop and relational databases.

    Zookeeper

    An application that coordinates distributed processing.

     

    Commercial Hadoop distributions

    Open-source software is created and maintained by a network of developers from around the world. It's free to download, use and contribute to, though more and more commercial versions of Hadoop are becoming available (these are often called "distros.") With distributions from software vendors, you pay for their version of the Hadoop framework and receive additional capabilities related to security, governance, SQL, and management/administration consoles, as well as training, documentation and other services. Popular distros include Cloudera, Hortonworks, MapR, IBM BigInsights and PivotalHD.

    About Big Data Hadoop Certification Training Course

    It is a comprehensive Hadoop Big Data training course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This is an industry recognized Big Data certification training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics. This Cloudera Hadoop training will prepare you to clear big data certification

    What you will learn in this Big Data Hadoop online training Course?

    1.       Master fundamentals of Hadoop 2.7 and YARN and write applications using them

    2.       Setting up Pseudo node and Multi node cluster on Amazon EC2

    3.       Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase

    4.       Learn Spark, Spark RDD, Graphx, MLlib writing Spark applications

    5.       Master Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting

    6.       Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc

    7.       Detailed understanding of Big Data analytics

    8.       Hadoop testing applications using MR Unit and other automation tools.

    9.       Work with Avro data formats

    10.   Practice real-life projects using Hadoop and Apache Spark

     

    11.   Be equipped to clear Big Data Hadoop Certification.

    Who should take this Big Data Hadoop Online Training Course?

    1.       Programming Developers and System Administrators

    2.       Experienced working professionals , Project managers

    3.       Big DataHadoop Developers eager to learn other verticals like Testing, Analytics, Administration

    4.       Mainframe Professionals, Architects & Testing Professionals

    5.       Business Intelligence, Data warehousing and Analytics Professionals

     

    6.       Graduates, undergraduates eager to learn the latest Big Data technology can take this Big Data Hadoop Certification online training

    What are the prerequisites for taking this Hadoop Certification Training?

    There is no pre-requisite to take this Big data training and to master Hadoop. But basics of UNIX, SQL and java would be good.At Petaa-Bytes, we provide complimentary unix and Java course with our Big Data certification training to brush-up the required skills so that you are good on your Hadoop learning path

Big Data analytics / Data Science / Hadoop Training Testimonials

  • To kick start your career in Big Data analytics, Petaa Bytes is the correct choice. Under Shrikant Sir’s guidance, all the concepts gets clear related to Big data technologies. He is a well experienced mentor and having sound knowledge in this field. I have completed my course and I am still getting support from Petaa bytes.

       Komal Laad

  • I started learning hadoop in 2014 at Petaa Bytes in guidance of Shikant Sir. It helped me in understanding hadoop concepts, hands-on and provided valuable study material. Even after course completion doubts were cleared at earliest. Also, guidance provided at Petaa Bytes helped me in cracking MNC interview.

       Gaurav Singh

  • It was an outstanding classroom training felicitated by Mr Shrikant Gawande. This happens to be one of my best training experiences where he mixed up the technological aspects with real life scenarios in a manner which eased our pressure of learning something really new. He also used his vast industrial exposure to a great extent and understand the real life needs of the technicalities. I would refer his name to have the training on Big Data and Hadoop which shall provide anyone with a perfect blend of conceptual learning and hands on detailing.

       Abhishek Das

  • If anybody wants to experience a sound learning aura and that too from a calm and composed personality like Shrikant sir, please go for Petaa Bytes Training. I joined here back in August 2015 to learn Hadoop and this has been my best learning experience in life so far. The best part of the course was personal attention given to me and to each and every student, which is very difficult to find these days when you join a training institute. I was also given career advice which helped me in refining my doubts for my career in Big Data field. Now that I am confident enough with what I learnt here, just waiting to be called in an arena(interviews) where I can showcase my knowledge.

       Mohit R Sudhera

  • I would recommend Petaa Bytes to anyone who is serious about pursuing a career in BigData -Hadoop, MongoDB, Python or taking their IT skills to the next level. I took the course work with Instructor Shrikanth, and he was super knowledgeable and fantastic! There were three students (including myself) in the class and he taught the class at a pace where we were all able to grasp the material and understand. If any of us got stuck on something, he would stop and help us work out our problem before moving on. The classes were well paced and questions were encouraged. It is totally hands-on.

       Ramesh Shetty

  • To kick start your career in Big Data analytics, Petaa Bytes is the correct choice. Under Shrikant Sir’s guidance, all the concepts gets clear related to Big data technologies. He is a well experienced mentor and having sound knowledge in this field. I have completed my course and I am still getting support from Petaa bytes .

       Komal Laad

  • PetaByte : An excellent gateway to step into BigData Arena/ BigData career, where you can learn Hadoop in depth , Shrikant Sir: Highly experienced and knowledgeable guide and mentor who makes student comfortable with his teaching , it automatically becomes a magical wand for those who aspiring to get into Big Data career. It is one of the best hadoop training in Navi Mumbai.

       Chinmay Palkar