What is Big Data

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/INFYTQ-PREPARATION/dbms/big-data-systems/introduction-and-characteristics-of-big-data/asset-what-is-big-data after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. Programming Fundamentals: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums
  2. Object Oriented Programming: Object Oriented Programming (OOPS)
  3. Data Structures (Basic): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  4. Algorithms (Basic): Algorithm Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms, Time Complexity Analysis
  5. Coding (Basic): Coding Questions by Yash Sir, C Programming by Yash Sir, Java, Python for Placements by Yash Sir, Programming in C++
  6. 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, Enhanced Data Models, Query Processing & Optimization, Data Warehousing & Mining, Big Data Systems, NoSQL
  7. SQL: SQL
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

  1. 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.

Loading lesson…