Big Data Architecture

Duration: 3 min

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Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Ability: Percentage, Profit and Loss, Ratio and Proportion (Ratios), Average, Time and Work, Speed Time and Distance, Simple Interest and Compound Interest, Age Problems, Number System, Divisibility Rules, HCF LCM, Algebra, Permutation and Combination, Probability, Data Interpretation, Logarithm, Sequence and Series (Series), Powers and Exponents (Surds and Indices), Speed Mathematics and Basic Mathematics
  2. Logical Reasoning: Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Data Sufficiency, Venn Diagram, Inequality, Series (Number and Letter Series) (Numerical Relations and Reasoning), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Selection Decision Table (Decision Making), Analogy, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Subject Verb Agreement (Verb Noun Agreement), Tenses, Articles, Preposition, Conjunction, Modals, Active Passive Voice (Voice), Direct and Indirect Speech, Vocabulary, Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Sentence Completion (Fill in the blanks), Sentence Correction (Error Correction), Cloze Test (Cloze Passage), Facts, Inference and Judgements
  4. Programming Fundamentals (Technical): C Fundamentals
  5. Data Structures (Technical): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  6. Algorithms (Technical): Algorithm Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Time Complexity Analysis, Advanced Algorithms
  7. OOPs (Technical): Object Oriented Programming (OOPS)
  8. DBMS (Technical): 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
  9. SQL (Technical): SQL
  10. Operating Systems (Technical): 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
  11. Computer Networks (Technical): 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, Cloud Computing and IoT, Cyber and Info Security
  12. Software Engineering (Technical): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. Coding/Automata Round: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir, Pseudo Codes
  14. Personality / Behavioral Assessment: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
  15. Writing Assessment: English Writing Ability
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on Big Data Systems, beginning with a slide outlining the topics to be covered, including Big Data Architecture. The instructor then explains the core components of a Big Data architecture, referencing a text description that states it includes mechanisms for ingesting, protecting, processing, and transforming data into file systems or database structures. The lecture progresses to a hand-drawn diagram on a digital whiteboard, illustrating the data flow. This diagram shows data originating from various sources, being ingested, and then processed through a central system before being stored in a file system or database. The final stage involves analytics tools and queries running on the data to mine intelligence, which is then output to various vehicles. The instructor uses the diagram to visually reinforce the concepts of data ingestion, processing, storage, and analysis.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video starts with a presentation slide titled 'Big Data Systems:' listing topics such as Introduction, Big Data Characteristics, Types of Big Data, Big Data Architecture, and Hadoop. The instructor then transitions to a new slide that defines Big Data architecture as including mechanisms for ingesting, protecting, processing, and transforming data into file systems or database structures. The instructor begins to draw a diagram on a digital whiteboard, starting with a box labeled 'Data Ingesting Source' to represent the origin of data.

  2. 2:00 – 2:32 02:00-02:32

    The instructor continues to build the diagram, adding a second box to represent the processing stage, which is labeled 'Processing'. An arrow connects the 'Data Ingesting Source' to the 'Processing' box. The instructor then draws a large oval around the entire process, labeling it 'Big Data Architecture'. The diagram is completed with a third box for storage, labeled 'File System or Database', and a final arrow pointing to 'Analytics Tools' and 'Query' to represent the analysis phase, which outputs to various 'vehicles'.

The lecture provides a comprehensive overview of Big Data architecture by first presenting a textual definition and then using a visual diagram to illustrate the complete data lifecycle. The progression from the initial slide to the detailed diagram effectively demonstrates the flow of data from its source through ingestion, processing, storage, and finally to analysis and output. This step-by-step approach helps students understand the interconnectedness of the various components in a big data system.

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