Big Data Architecture Layers

Duration: 6 min

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Inside: a video lesson and guided study material.

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 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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