What is Big Data

Duration: 2 min

This video lesson is available to enrolled students.

Inside: a video lesson and guided study material.

Module outline

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
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
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
General Awareness: Evolution of Indian Banking (Banking & Financial Awareness), GK- Current Affairs, Static General Knowledge, Indian Economy
C Concepts + MCQs (IT Specialist): C Programming by Yash Sir, C Language Core
Java (IT Specialist): Java
Python (IT Specialist): Python for Placements by Yash Sir, Python Basics
OOPs (IT Specialist): Object Oriented Programming (OOPS)
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
SQL (IT Specialist): SQL
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
Data Structure (IT Specialist): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
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
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
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
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
Descriptive Test (Main Exam): English Writing Ability

AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on the definition of Big Data, displayed on a PDF slide titled 'What is Big Data?'. The instructor explains that Big Data is a collection of large and complex datasets that cannot be processed using traditional computing techniques. The definition is broken down into three key points: the nature of the data (large and complex), the limitation of traditional methods, and the continuous act of generating, capturing, and processing enormous amounts of data. The instructor writes 'Relational Databases' and 'Multi-media' on the slide, and later adds 'Stream' and 'Sensor' to illustrate data sources. The term 'Genetic' is also written, likely referring to genetic data as another source. The instructor uses a yellow highlighter to emphasize the phrase 'enormous amounts of data on a continuing basis'. The video concludes with the slide transitioning to a new section titled 'Big Data Driving Factors'.

Chapters

0:00 – 2:00 00:00-02:00
The video shows a lecture slide titled 'What is Big Data?'. The instructor defines Big Data as a collection of large and complex data sets that cannot be processed using traditional computing techniques. The definition is presented in three bullet points. The instructor writes 'Relational Databases' and 'Multi-media' on the slide, then adds 'Stream' and 'Sensor' to illustrate data sources. The term 'Genetic' is also written. The instructor uses a yellow highlighter to emphasize the phrase 'enormous amounts of data on a continuing basis'. The slide transitions to a new section titled 'Big Data Driving Factors' at the end of the clip.

The lecture provides a foundational definition of Big Data, emphasizing its scale, complexity, and the need for non-traditional processing methods. The instructor uses the slide to build the definition incrementally, adding examples of data sources like relational databases, multimedia, streams, sensors, and genetic data. The key takeaway is that Big Data is not just about volume but also about the continuous and complex nature of data generation and processing, which necessitates new technologies and approaches.