Hadoop

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/NIACL-AO-IT-SPECIALIST/dbms-it-specialist/big-data-systems/hadoop-framework-overview/asset-hadoop after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Aptitude: Ratio and Proportion (Ratios), Data Interpretation, Mixture and Alligation, Permutation and Combination, Number System, Sequence and Series (Series), Speed Time and Distance, Speed Mathematics and Basic Mathematics, Algebra, Percentage, Profit and Loss, Simple Interest and Compound Interest, Powers and Exponents (Surds and Indices), Time and Work, Mensuration and Geometry, Partnership
  2. Reasoning Ability: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Seating Arrangements, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Directions (Direction Test), Ranking, Order and Sequence, Syllogisms, Blood Relations, Puzzle Solving (Puzzles), Inequality, Machine Input-Output
  3. English Language: Vocabulary, Noun, Subject Verb Agreement (Verb Noun Agreement), Adjectives, Tenses, Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Cloze Test (Cloze Passage), Sentence Correction (Error Correction), Sentence Completion (Fill in the blanks), Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Construction, Pronoun, Preposition, Conjunction, Adverb, Verb, Modals, Articles, Determiner, Interjection
  4. General Awareness: Evolution of Indian Banking (Banking & Financial Awareness), GK- Current Affairs, Static General Knowledge, Indian Economy
  5. C Concepts + MCQs (IT Specialist): C Programming by Yash Sir, C Language Core
  6. Java (IT Specialist): Java
  7. Python (IT Specialist): Python for Placements by Yash Sir, Python Basics
  8. OOPs (IT Specialist): Object Oriented Programming (OOPS)
  9. DBMS (IT Specialist): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, ORDBMS, Database Security & Authorization, Query Processing & Optimization, Enhanced Data Models, Data Warehousing & Mining, Big Data Systems, NoSQL
  10. SQL (IT Specialist): SQL
  11. Software Engineering (IT Specialist): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  12. Data Structure (IT Specialist): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  13. Operating System (IT Specialist): Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling, File Management, Windows OS, Linux OS, Security, Distributed Systems, Virtual Machines
  14. Compiler Design (IT Specialist): Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  15. Computer Architecture (IT Specialist): Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Multiprocessors, Programming the Basic Computer
  16. Data Communication & Networking (IT Specialist): Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  17. Descriptive Test (Main Exam): English Writing Ability
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video provides an educational overview of the Hadoop framework, explaining its purpose and core components. It begins by defining Hadoop as a framework that runs applications using the MapReduce algorithm, enabling parallel processing of large datasets for statistical analysis. The lecture then transitions to a diagram illustrating the Hadoop ecosystem, showing how a user interacts with the Hadoop Framework, which processes data from various sources like Big Data, Media Data, System Log Files, and Relational Databases. The final segment details the two core services of Hadoop: HDFS (Hadoop Distributed File System) for scalable storage and YARN (Yet Another Resource Negotiator) for distributed and parallel data processing. The instructor uses on-screen text and diagrams to clearly explain these concepts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video starts with a text-based explanation of Hadoop, stating that it runs applications using the MapReduce algorithm, where data is processed in parallel. It highlights that Hadoop is used to develop applications for complete statistical analysis on huge amounts of data. The text further explains that the Hadoop framework works in an environment providing distributed storage and computation across clusters of computers, designed to scale from a single server to thousands of machines. The instructor then draws a diagram showing the Hadoop Framework as a central processing unit that receives data from various sources like Big Data, Media Data, System Log Files, and Relational Databases, and processes it for a user. The diagram visually represents the flow of data from these sources into the Hadoop Framework, which then outputs results to the user.

  2. 2:00 – 2:10 02:00-02:10

    The video transitions to a new section titled 'Hadoop Core Components'. The on-screen text states that Hadoop provides two core services: HDFS (storage) and YARN (processing). It defines HDFS as the Hadoop Distributed File System, a scalable storage unit, and YARN as the component used to process data stored in HDFS in a distributed and parallel fashion. The instructor emphasizes these two components as the fundamental parts of the Hadoop ecosystem.

The lecture systematically introduces Hadoop by first explaining its core function of enabling parallel processing for big data analysis, then illustrating its architecture with a diagram that shows data flow from various sources into the Hadoop Framework, and finally breaking down the system into its two essential components: HDFS for storage and YARN for processing. This progression from a high-level overview to specific components provides a clear and structured understanding of the Hadoop ecosystem.

Loading lesson…