Big Data Architecture Layers

Duration: 6 min

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Inside: a video lesson and guided study material.

Module outline

  1. Quantitative Aptitude: Number System, Divisibility Rules, HCF LCM, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Simple Interest and Compound Interest, Mixture and Alligation, Partnership, Age Problems, Time and Work, Pipe and Cistern, Speed Time and Distance, Algebra, Arithmetic, Data Interpretation, Sequence and Series (Series), Logarithm, Powers and Exponents (Surds and Indices), Permutation and Combination, Probability, Speed Mathematics and Basic Mathematics, Trigonometry
  2. Logical Reasoning: Coding Decoding, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Seating Arrangements, Puzzle Solving (Puzzles), Data Arrangement, Syllogisms, Inequality, Data Sufficiency, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Series (Number and Letter Series) (Numerical Relations and Reasoning), Venn Diagram, Logical Sequence Of Words, Calendar, Clock, Cubes & Dices, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning)
  3. Verbal Ability: Noun, Pronoun, Adjectives, Verb, Adverb, Tenses, Subject Verb Agreement (Verb Noun Agreement), Articles, Preposition, Conjunction, Modals, Determiner, Vocabulary, 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), Active Passive Voice (Voice), Direct and Indirect Speech, Sentence Construction
  4. Data Structures (Domain Test): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  5. Algorithms (Domain Test): Algorithm Analysis, Sorting Algorithms, Time Complexity Analysis, Greedy Algorithms
  6. OOPs (Domain Test): Object Oriented Programming (OOPS)
  7. DBMS (Domain Test): 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
  8. SQL (Domain Test): SQL
  9. Operating Systems (Domain Test): 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
  10. Computer Networks (Domain Test): 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
  11. Coding: Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir
  12. Written English Test: English Writing Ability
  13. Psychometric Test: Behavioral Assessment (Behavioral Competency Profiling) (Psychometric Test)
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on the four logical layers of a big data architecture, using a PDF document displayed on a computer screen. The instructor begins by introducing the concept of a multi-layered architecture and then proceeds to explain each layer sequentially. The first layer discussed is the 'Big data sources layer,' which encompasses all data inputs, including enterprise applications like ERP and CRM, as well as data from mobile devices, sensors, and social media. The second layer is the 'Data massaging and storage layer,' which is responsible for converting unstructured data into a usable format and storing it in systems like RDBMS, HDFS, or NoSQL databases. The third layer is the 'Analysis layer,' where various analytics tools are used to extract business intelligence from the stored data, with different techniques required for structured versus unstructured data. The final layer is the 'Consumption layer,' which receives the analysis results and presents them to end-users through various outputs like dashboards, reports, and applications. Throughout the lecture, the instructor uses a digital pen to draw a flowchart on the document, visually representing the data flow from source to consumption, and highlights key terms in the text to emphasize important concepts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a discussion of the 'Big data sources layer' in a big data architecture. The on-screen text defines this layer as the origin of all data, which can come from company servers, sensors, third-party providers, or enterprise applications like ERP, CRM, and MS Office docs. The instructor explains that data can be ingested in batch mode or real-time. A diagram is drawn on the screen, showing a box labeled 'Data Source' with an arrow pointing to a 'Split' box, indicating the initial stage of data processing. The instructor also mentions that data sources include databases, mobile devices, sensors, social media, and email.

  2. 2:00 – 5:00 02:00-05:00

    The lecture transitions to the 'Data massaging and storage layer.' The on-screen text explains that this layer receives data from sources, converts unstructured data into a format that analytic tools can understand, and stores it. The instructor highlights that structured data is stored in an RDBMS, while unstructured data is stored in a specialized file system like Hadoop Distributed File System (HDFS) or a NoSQL database. A diagram is drawn to illustrate this process, showing a box labeled 'Data Source' leading to a 'Storage' box, which is further broken down into 'RDBMS' and 'NoSQL/HDFS.' The instructor emphasizes that this layer is crucial for preparing data for analysis.

  3. 5:00 – 5:47 05:00-05:47

    The final layer discussed is the 'Consumption layer.' The on-screen text states that this layer receives the analysis results and presents them to the appropriate output layer. The instructor explains that outputs can be for human viewers, such as dashboards and reports, or for applications and business processes. A diagram is drawn to show the flow from the 'Analysis' layer to the 'Consumption' layer, which then leads to 'Applications' and 'Business Processes.' The instructor highlights the importance of this layer in making the insights actionable for end-users.

The video provides a comprehensive overview of the four logical layers of a big data architecture, emphasizing the flow of data from its source to its final consumption. The lecture systematically breaks down the process, starting with the 'Big data sources layer' where data is collected from diverse origins. It then moves to the 'Data massaging and storage layer,' which is critical for transforming and storing data in a usable format. The 'Analysis layer' is where the data is processed to extract valuable business intelligence, with different methods for structured and unstructured data. Finally, the 'Consumption layer' delivers the results to end-users through various outputs. The visual aid of the flowchart effectively illustrates the sequential nature of this process, reinforcing the concept that each layer is essential for the successful implementation of a big data solution.

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