Big Data Architecture

Duration: 3 min

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Inside: a video lesson and guided study material.

Module outline

  1. 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
  2. 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
  3. 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
  4. General Awareness: Evolution of Indian Banking (Banking & Financial Awareness), GK- Current Affairs, Static General Knowledge, Indian Economy
  5. C Concepts + MCQs (IT Specialist): C Programming by Yash Sir, C Language Core
  6. Java (IT Specialist): Java
  7. Python (IT Specialist): Python for Placements by Yash Sir, Python Basics
  8. OOPs (IT Specialist): Object Oriented Programming (OOPS)
  9. 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
  10. SQL (IT Specialist): SQL
  11. 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
  12. Data Structure (IT Specialist): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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 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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