Big Data Architecture

Duration: 3 min

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Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. General Aptitude & Reasoning: Number System, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Time and Work, Speed Time and Distance, Mixture and Alligation, Data Interpretation, Algebra, Probability, Permutation and Combination, Venn Diagram, HCF LCM, Divisibility Rules, Age Problems, Pipe and Cistern, Partnership, Linear Algebra, Sequence and Series (Series), Statistics, Powers and Exponents (Surds and Indices), Arithmetic, Mensuration and Geometry, Calculus, Trigonometry, Vectors, Speed Mathematics and Basic Mathematics, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analogy, Series (Number and Letter Series) (Numerical Relations and Reasoning), Puzzle Solving (Puzzles), Syllogisms, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Calendar, Seating Arrangements, Coding Decoding, Blood Relations, Directions (Direction Test), Clock, Analytical Reasoning (Counting Figures Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Inequality, Data Sufficiency, Cubes & Dices
  3. Computer Networks & Cybersecurity: 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
  4. Programming, Data Structures & Algorithms: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, OOPS with C++, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  5. AI, Machine Learning, Big Data & Cloud: Emerging Tech Trends, Artificial Intelligence, Big Data Systems, Approaches to AI, Search Algorithms, Knowledge Representation, Cloud Computing
  6. Digital Logic & Computer Organization: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer
  7. TOC & Compiler: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines, Complexity Theory, 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
  8. Discrete Mathematics: Set Theory, Relations, Functions, Graph Theory, Group Theory, Propositional and Predicate Logic
  9. Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management, Unix OS, Linux OS, Distributed Systems
  10. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Object Oriented Technology, HTML Basics, JavaScript-Basics, Java, XML
  11. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Relational Calculus, Transaction Management, Concurrency Control, Database Recovery, Data Warehousing & Mining, Enhanced Data Models, NoSQL
  12. Other Recurring CS Topics: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, Multimedia
  13. Live Classes: ISRO Live classes
  14. Full Mock Test:
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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