Hadoop

Duration: 2 min

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Module outline

  1. Communication Round: Vocabulary, Subject Verb Agreement (Verb Noun Agreement), 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), Direct and Indirect Speech, Active Passive Voice (Voice), Verbal Analogy (Word Based Analogy), Articles, Preposition, Conjunction, Sentence Construction, English Writing Ability, Communication Assessment
  2. Quantitative Aptitude: Ratio and Proportion (Ratios), Divisibility Rules, Data Interpretation, Mixture and Alligation, Permutation and Combination, Logarithm, Number System, HCF LCM, Speed Time and Distance, Percentage, Average, Powers and Exponents (Surds and Indices), Time and Work, Mensuration and Geometry, Age Problems, Pipe and Cistern, Probability, Profit and Loss, Simple Interest and Compound Interest, Algebra
  3. Analytical and Logical Reasoning: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Directions (Direction Test), Ranking, Order and Sequence, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Logical Sequence Of Words, Puzzle Solving (Puzzles), Syllogisms, Seating Arrangements, Calendar, Clock, Blood Relations, Cubes & Dices, Venn Diagram, Analytical Reasoning (Counting Figures Reasoning)
  4. Cognizant Gaming Aptitude: Gamified / Game Based Aptitude
  5. Java (Cluster 1): Java
  6. ANSI SQL MySQL (Cluster 1,2,3): SQL
  7. HTML, CSS, JavaScript (Cluster 1,3): HTML, CSS, JavaScript
  8. Python (Cluster 2): Python for Placements by Yash Sir
  9. Cloud Fundamentals (Cluster 2): Emerging Tech Trends
  10. C# (Cluster 3): C#
  11. OOPs: Object Oriented Programming (OOPS)
  12. Technical Assessment (Coding): Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Pseudo Codes
  13. Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  14. DBMS: 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
  15. Operating Systems: 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
  16. Computer Networks: 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, Mobile Technology, Cloud Computing and IoT, Cyber and Info Security
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.

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