Conceptual Modeling of Datawarehouse

Duration: 4 min

This video lesson is available to enrolled students.

Return to /learn/NIACL-AO-IT-SPECIALIST/dbms-it-specialist/data-warehousing-mining/data-modelling-in-warehouses/asset-conceptual-modeling-of-datawarehouse after enrolling

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 is a lecture on conceptual modeling for data warehouses, presented as a slide deck. It begins by introducing the core concepts of modeling data warehouses using dimensions and measures. The first schema discussed is the Star Schema, defined as a fact table in the center connected to a set of dimension tables. An example diagram illustrates this structure with a central Sales Fact Table linked to dimension tables for time, item, branch, location, and supplier. The lecture then transitions to the Snowflake Schema, which is described as a refinement of the star schema where some dimensional hierarchies are normalized into smaller tables, creating a shape resembling a snowflake. An example diagram shows this with the location dimension split into a location table and a city table. Finally, the video introduces the Galaxy Schema (or Fact Constellation), which consists of multiple fact tables that share dimension tables, forming a collection of stars. An example diagram shows a Sales Fact Table and a Shipping Fact Table both connected to shared dimension tables. The instructor uses on-screen text and diagrams to explain each concept.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The lecture begins with a slide titled 'Conceptual Modeling of Data Warehouses'. The instructor introduces the fundamental concepts of modeling data warehouses using dimensions and measures. The first schema presented is the 'Star schema', defined as 'A fact table in the middle connected to a set of dimension tables'. The instructor writes 'Modeling' and 'Schemas' on the slide. The slide also shows a diagram labeled 'Example of Star Schema' with a central 'Sales Fact Table' connected to dimension tables for time, item, branch, and location, with measures like 'units sold' and 'dollars sold' listed in the fact table.

  2. 2:00 – 4:30 02:00-04:30

    The lecture continues by defining the 'Snowflake schema' as a refinement of the star schema where dimensional hierarchies are normalized into smaller tables, forming a snowflake shape. An example diagram is shown, where the 'location' dimension is split into a 'location' table and a 'city' table, which is connected to the location table. The instructor then introduces the 'Galaxy schema' (or 'fact constellation'), defined as multiple fact tables sharing dimension tables, viewed as a collection of stars. An example diagram illustrates this with a 'Sales Fact Table' and a 'Shipping Fact Table' both connected to shared dimension tables like 'time', 'item', and 'location'. The instructor uses the on-screen text and diagrams to explain the relationships and structure of each schema.

The video provides a structured progression through three key data warehouse modeling schemas. It starts with the foundational Star Schema, which is simple and efficient for querying. It then introduces the Snowflake Schema as a more normalized version, which reduces data redundancy but can complicate queries. Finally, it presents the Galaxy Schema to handle complex business scenarios with multiple, related fact tables. The lecture uses clear definitions and visual diagrams to demonstrate the structure and relationships in each schema, showing how they evolve from a simple central model to a more complex, interconnected system.

Loading lesson…