Challenges in Big Data

Duration: 3 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 presents a lecture on the challenges of big data, structured as a slide presentation. The first part of the lecture details three primary challenges: Quick data growth, Storage, and Syncing Across Data Sources. It explains that the rapid rate of data generation makes it difficult to find relevant insights, that large volumes of data are hard to store and manage without proper tools, and that data from different sources may be out of sync. The second part of the lecture introduces additional challenges, including Security, Unreliable data, and Miscellaneous Challenges. Security is highlighted as a major concern due to the risk of advanced persistent threats, requiring proper authentication and encryption. Unreliable data is discussed as data that may be redundant, incomplete, or contradictory. Miscellaneous challenges include the high cost of solutions, the need for data integration, and the scarcity of skilled talent. The lecture concludes by introducing a solution, the MapReduce algorithm, developed by Google to manage big data.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a slide titled "Challenges of big data:". The first challenge listed is "Quick data growth:", which is explained as the difficulty in finding insights from the vast and rapidly increasing amount of data generated every second. The second challenge is "Storage:", which describes the difficulty organizations face in storing and managing such large volumes of data without appropriate tools and technologies. The third challenge is "Syncing Across Data Sources:", which states that when organizations import data from different sources, the data from one source might not be up to date compared to the other, leading to inconsistencies. The on-screen text clearly outlines these three challenges with their respective definitions.

  2. 2:00 – 3:22 02:00-03:22

    The lecture continues with the next set of challenges. The first is "Security:", which is described as a major challenge because the huge amount of data in organizations can become a target for advanced persistent threats, necessitating proper authentication and data encryption. The next challenge is "Unreliable data:", which refers to data that may contain redundant, incomplete, or contradictory information. The final section is "Miscellaneous Challenges:", which lists other issues such as the high cost of solutions, the need for data integration, the scarcity of skilled talent, and the requirement to process large amounts of data quickly and accurately. The slide concludes by introducing a solution: "Technologies and tools to help manage big data:", specifically mentioning that Google solved this problem using an algorithm called MapReduce.

The lecture systematically outlines the multifaceted challenges of managing big data. It starts with the fundamental issues of data volume and velocity, which create problems of relevance and storage. It then moves to the complexities of data integration and consistency. The discussion progresses to more abstract challenges like data security and reliability, highlighting the risks and quality issues inherent in large datasets. Finally, it introduces the concept of a technological solution, the MapReduce algorithm, to address these challenges, providing a logical flow from problem identification to a potential solution.

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