Big Data With Hadoop Training in Noida / Big Data With Spark Training in Noida

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Hadoop is an open-source software framework for storing data and running applications on the bulk commodity hardware on the computer system. It was developed by Doug Cutting and Mike Cafarella and was released in 2006. It is licensed under the Apache v2 license. It provides bulk storage for any type of data, huge processing power, and the ability to handle virtually limitless concurrent tasks or jobs. It was developed, based on the paper written by Google on the Map Reduce system and it applies concepts of functional programming. It is written in the Java language and ranks among the highest-level Apache projects. As the WWW (World Wide Web) grew in the late 1900s and early 2000s, search engines, spiders and indexes were created to help locate relevant and required information amid the text-based content. In the early 90s, search results were returned by humans. But as the web grew from dozens to millions of pages with an increase in population, automation was needed. Web crawlers or spiders were created, many as university-led research projects, and search engine start-ups took off (Yahoo, AltaVista, etc.).

According to its co-founders, the genesis of Hadoop Training Institute in Noida was the Google File System paper that was published in the year 2003 in October. This paper spawned another one from Google which Simplified Data Processing on Large Clusters. Development started on the Apache Nutch project, but because of some reason was moved to the new Hadoop Training subproject in January 2006. Cutting, who was working at Yahoo! at that time, named this technology after his son's toy which was an elephant. In 2006, Owen O’Malley was the first committer to add to the Hadoop project in starting phase. The newest version 0.1.0 was released in April 2006.

Why is Hadoop important?

  • It has the ability to store and process huge amounts of data of any type, quickly.
  • The framework is free because of its open-source platform and uses commodity hardware to store large quantities of data.
  • It’s distributed computing model processes big data very fast.
  • Multiple copies of all data are stored automatically.
  • Fault tolerance
  • Flexibility
  • Scalability

What are the challenges in using Hadoop?

  • MapReduce programming is not a good match for all types of problems in this type of platform.
  • There’s a widely acknowledged talent gap in between.
  • Data security.
  • Full-fledged data management and governance.

Big Data

Big data is a collection of large datasets that cannot be processed by using traditional computing techniques. It is not a single technique rather it has become a complete subject, which involves various tools, techniques, and frameworks. Black Box, Social Media, Stock Exchange, Power Grid, Transport Data, and Search Engine Data come under this technology. Big Data includes huge volumes with high velocity and a flexible variety of data. Big Data With Spark Training in Noida will be of three types: Structured data, Semi Structured data & Unstructured. Big Data can help as follows:

  • Can identify the root causes of failure in near real-time.
  • Can understand customer buying-habits for revamping sales operations.
  • Can re-evaluate risk portfolios.
  • Can detect fraudulent behavior for avoiding disasters.

Benefits of Big Data

  • Using the information kept in social networks like Facebook and Instagram, marketing agencies are learning about the response to their campaigns, promotions, and other advertising mediums, so they can deal with them.
  • Using the information in the social media platforms FB & Instagram. Organizations use the preferences and product perceptions of their consumers, and product companies and retail organizations are planning their production in order to increase productivity.
  • Using the data regarding the previous medical history of patients, hospitals are providing better and quick service to their patients.

What are the challenges in using Big Data?

The major challenges associated are as follows −

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

Any aspirant with Big Data Analytics skills proven to be of great value can get a job in any data-driven company. Data is rising at an exponential rate, and at this point in time, it has become extremely necessary for companies to analyze the raw data that they absorb. Therefore, most companies are hiring Big Data specialists. Hence, Big Data is a great career option as of now for every student if they have an interest. If you are looking to learn Big Data with Hadoop, you have landed at the perfect place. In this Hadoop Training Institute in Noida, you will learn basic to advanced concepts in a very simple and easy step.

Today, the role of Big Data with Hadoop Training in Noida in numerous industries is growing like never before it's eliminated all the restrictions and allowed managers to acquire, refine and analyze information to require measurable steps. It’s actually redoubled the understanding of the market, user behavior, and far additional, which might improve the company's bottom lines. The concepts of big data have merged everything and offers correct results to the research. Moreover, firms have conjointly relieved the risks concerned and improved the operational standards. No doubt, it's fine-tuned the operating capability of the organizations and allowed managers to enhance the operating potency at most the degree of the corporate.

Why Ducat?

Ducat has a dedicated team of highly expert trainers to identify, evaluate, implement, and provide the Best Big Data With Hadoop Training Institute in Noida for our students. Our Trainers leverage a defined methodology that helps identify a CCNP opportunity, develop the most optimal resolution and maturely execute the solution. We have the best trainers across the world to provide Best Hadoop Training in Noida who are highly qualified and are the best in their field. The Training & Placement cell is committed to providing all attainable help to the students in their efforts to seek out employment and internships in every field. The placement department works beside alternative departments as a team in molding the scholars to the necessities of varied industries. We got a proactive and business-clued-in Placement Cells that pride themselves on a robust skilled network across numerous sectors. It actively coordinates with every student and ensures that they get placed with purported MNCs within six months of graduating. We are the Best Big Data With Hadoop Training Institute in Noida, Greater Noida, Faridabad, Gurugram, and Ghaziabad.

Introduction to Big Data

  • Overview of Big Data Technologies and its role in Analytics
  • Big Data challenges & solutions
  • Data Science vs Data Engineering
  • FOUR V's of Big Data given by Google.

Unix & Java

  • Introduction to UNIX shell.
  • Basic Commands of UNIX
  • Create
  • Copy
  • Move
  • Delete etc.
  • Basic of JAVA Programming Language
  • Architecture JVM, JRE, JIT
  • Control Structures
  • OOP's Concept in Java
  • String Classes/Array/Exception Handling
  • Collection Classes

Apache HDFS

  • Understanding the problem statement and challenges persisting to such large data to perceive the need of Distributed File System.
  • Understanding HDFS architecture to solve problems
  • Understanding configuration and creating directory structure to get a solution of the given problem statement
  • Setup appropriate permissions to secure data for appropriate users
  • Setting up Java Development with HDFS libraries to use HDFS Java APIs

Apache Map-Reduce

  • What is Map Reduce.
  • Input and output formats.
  • Data Types in Map Reduce.
  • Flow of Map Reduce Jobs.
  • Wordcount In Map Reduce.
  • How to use Custom Input Formats
  • Use case for Structure Data Sets.
  • Writing Custom Classes.


  • What is HIVE.
  • Architecture of HIVE.
  • Tables in Hive with Load Functions.
  • Query Optimization.
  • Partitioning and Bucketing.
  • Joins in HIVE.
  • Indexing In HIVE.
  • File Formats in HIVE.
  • How to read JSON files in HIVE


  • What is Sqoop.
  • Relation between SQL & Hadoop.
  • Performing Sqoop Import.
  • Incrementals and Conditional Imports
  • Performing Sqoop Export.


  • What is PIG & ETL.
  • Introduction to PIG Architecture.
  • Introduction of PIG Latin.
  • How to Perform ETL on any Kind of data(PIG Eats Everything)
  • Use cases of PIG.
  • Joins in PIG.
  • Co-grouping In PIG.

Introduction to NoSQL Database &OOZIE

  • What is HBASE.
  • Architecture of HBASE.
  • CRUD operations in HBASE
  • Retrival of HBASE Data.
  • Introduction of Apache Oozie (Scheduler tool)

Introduction to Programming in Scala

  • Basic data types and literals used
  • List the operators and methods used in Scala
  • Classes of Scala
  • Traits of Scala.
  • Control Structures in Scala.
  • Collection of Scala.
  • Libraries of Scala.

Introduction to Spark

  • Limitations of MapReduce in Hadoop Objectives
  • Batch vs. Real-time analytics
  • Application of stream processing
  • Spark vs. Hadoop Eco-system

Using RDD for Creating Applications in Spark

  • Features of RDDs
  • How to create RDDs
  • RDD operations and methods
  • Explain RDD functions and describe how to write different codes in Scala

Running SQL queries Using SparkQL

  • Explain the importance and features of SparkQL
  • Describe methods to convert RDDs to DataFrames
  • Explain concepts of SparkSQL
  • Describe the concept of hive integration

Spark ML Programming

  • Explain the use cases and techniques of Machine Learning (ML)
  • Describe the key concepts of Spark ML
  • Explain the concept of an ML Dataset, and ML algorithm, model selection via cross validation