Types of BigData

Duration: 5 min

This video lesson is available to enrolled students.

Return to /learn/COCUBES-PREPARATION/dbms/big-data-systems/types-and-sources-of-big-data/asset-types-of-bigdata after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Aptitude: Number System, Divisibility Rules, HCF LCM, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Mixture and Alligation, Partnership, Age Problems, Time and Work, Pipe and Cistern, Speed Time and Distance, Algebra, Arithmetic, Data Interpretation, Sequence and Series (Series), Logarithm, Powers and Exponents (Surds and Indices), Permutation and Combination, Probability, Speed Mathematics and Basic Mathematics, Trigonometry
  2. Logical Reasoning: Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Syllogisms, Inequality, Data Sufficiency, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Series (Number and Letter Series) (Numerical Relations and Reasoning), Venn Diagram, Logical Sequence Of Words, Calendar, Clock, Cubes & Dices, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Noun, Pronoun, Adjectives, Verb, Adverb, Tenses, Subject Verb Agreement (Verb Noun Agreement), Articles, Preposition, Conjunction, Modals, Determiner, Vocabulary, 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), Active Passive Voice (Voice), Direct and Indirect Speech, Sentence Construction
  4. Data Structures (Domain Test): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  5. Algorithms (Domain Test): Algorithm Analysis, Sorting Algorithms, Time Complexity Analysis, Greedy Algorithms
  6. OOPs (Domain Test): Object Oriented Programming (OOPS)
  7. DBMS (Domain Test): 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
  8. SQL (Domain Test): SQL
  9. Operating Systems (Domain Test): 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
  10. Computer Networks (Domain Test): 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
  11. Coding: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir
  12. Written English Test: English Writing Ability
  13. Psychometric Test: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video is a lecture on the types of Big Data, presented in a digital document format. The instructor begins by introducing the three main categories: Structured, Semi-Structured, and Unstructured data. A visual diagram is used to illustrate the differences, showing structured data as a grid of uniform blue squares, semi-structured data as a grid with a mix of colors and some organization, and unstructured data as a chaotic, colorful grid. The lecture then proceeds to define each type in detail. Structured data is defined as data that can be stored and processed in a fixed format, like tables in a relational database. Semi-Structured data is described as data that lacks a formal data model but has some organizational properties, such as tags in HTML, XML, or JSON files. Finally, unstructured data is defined as data with an unknown form that cannot be stored in a relational database and must be transformed into a structured format to be analyzed, with examples including natural language, voice, and social media posts. The instructor uses on-screen text and handwritten annotations to emphasize key points throughout the presentation.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a presentation slide titled 'Types of Big Data'. The instructor states that Big Data can be categorized into three types: Structured, Semi-Structured, and Unstructured. A diagram is displayed to visually represent these types. The 'Structured' data is shown as a grid of uniform blue squares, symbolizing order and a fixed format. The 'Semi-Structured' data is depicted as a grid with a mix of colors and some organization, indicating partial structure. The 'Unstructured' data is shown as a chaotic, colorful grid, representing a lack of inherent order. The instructor uses a digital pen to draw checkmarks next to each type as they are introduced, reinforcing the classification.

  2. 2:00 – 4:49 02:00-04:49

    The lecture transitions to a detailed explanation of each data type. First, 'Structured' data is defined as data that can be stored and processed in a fixed format, with examples like tables with fixed attributes in relational databases. The instructor highlights the text 'fixed format' and 'relational databases' on the screen. Next, 'Semi-Structured' data is explained as data that does not have a formal data model but has organizational properties like tags, with examples including HTML, XML, JSON files, and server logs. The instructor writes 'Record' and 'name: Roshni' to illustrate a data record. Finally, 'Unstructured' data is defined as data with an unknown form that cannot be stored in a relational database and must be transformed into a structured format to be analyzed. Examples provided are natural language, voice, Wikipedia, and Twitter posts. The instructor writes 'Text, voice, handwritten text' and 'CSS, Datal format' to emphasize the nature of this data.

The video provides a clear and structured educational overview of the three primary types of Big Data. It begins with a high-level classification and uses a powerful visual metaphor to distinguish between structured, semi-structured, and unstructured data. The lecture then systematically defines each type, providing concrete examples and highlighting key characteristics. The progression from a simple list to a detailed definition with visual aids and handwritten annotations effectively reinforces the learning, making the complex concept of data classification accessible and understandable for students.

Loading lesson…