LearnWithMey
  • 👩‍💻Getting started
  • Introduction
    • What and Why Data Analytics?
    • Data Analyst Roadmap
    • Data Analysis Steps
    • Type of Data Analytics
    • Data Analytics Use Cases
    • Data Analytics Tools
    • Skills Requirement
    • Data Analyst vs Data Scientist
      • Key notes
  • STATISTICAL TECHNIQUES IN BUSINESS & ECONOMICS
    • Chapter 1: What is Statistics?
      • 1.1 What is meant by Statistics?
      • 1.2 Types of Statistics?
      • 1.3 Types of Variables
      • 1.4 Levels of Meaurement
      • Chapter Summary
    • Chapter 2: Describing Data
      • 2.1 Introduction
      • 2.2 Constructing a Frequency Table
      • 2.3 Constructing the Frequency Distributions
    • Chapter 3: Describing Data - Numerical Measures
      • 3.1 Introduction
      • 3.2 The population Mean
      • 3.3 The Sample Mean
      • 3.4 Properties of the Arithmetic Mean
      • 3.5 The Weighted Mean
      • 3.6 The Median
      • 3.7 The Mode
      • 3.8 The Geometric Mean
      • 3.9 Why Study Dispersion?
      • 3.10 Measures of Dispersion
      • 3.11 Interpretation and Uses of the Standard Deviation
      • 3.12 The Mean and Standard Deviation of Grouped Data
    • Chapter 4: Describing Data - Display and Explore Data
      • 4.1 Introduction
      • 4.2 Dot Plots
      • 4.3 Stem-and-Leaf Displays
      • 4.4 Measures of Position
      • 4.5 Skewness
      • 4.6 Describing the Relationship between Two Variables
    • Chapter 5: A Survey of Probability Concepts
    • Chapter 6: Discrete Probability Distribution
    • Chapter 7: Continuous Probability Distribution
    • Chapter 8: Sampling Methods and Central Limit Theorem
    • Chapter 9: Estimation and Confidence Intervals
      • 9.1 Introduction
    • Chapter 10: One-Sample Tests of Hypothesis
      • 10.1 Introduction
      • 10.2 Hypothesis Testing
    • Chapter 11: Two-Sample Tests of Hypothesis
    • Chapter 12: Analysis of Variance
    • Chapter 13: Correlation and Linear Regression
    • Chapter 14: Multiple Regression Analysis
    • Chapter 15: Index Number
    • Chapter 16: Time Series and Forcasting
    • Chapter 17: Nonparametric Methods: Goodness-of-Fit Tests
    • Chapter 18: Nonparametric Methods: Analysis of Ranked Data
    • Chapter 19: Statistical Process Control and Quality Management
    • Chapter 20: Decision Theory
    • Tables ( Z and Student's T )
  • Python for Data Science
    • Handbook
    • Introduction
    • Installation
    • Basic Syntax
    • 2. Introduction to Numpy
  • Excel for Data Analysis
    • Introduction
  • Data Warehousing
    • Introduction
    • Data Warehouse Architecture
  • Data Visualization with Power BI
    • Introduction
  • Data Analysis with Python
    • Importing Datasets
      • Understanding Data
      • Python Packages for Data Sciences
    • Data Wrangling
      • Pre-processing Data in Python
      • Dealing with Missing Values in Python
      • Data Formatting in Python
  • Machine Learning
    • Page
    • 1. Introduction
    • Machine Learning Exercises
  • React
    • Lazy Loading
  • Search Tips
  • Secure Product Design
  • Google like a pro
  • Project Management
  • SEO in React.js
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  1. Data Warehousing

Data Warehouse Architecture

Data warehouse architecture refers to the way a data warehouse is structured and organized. There are several common architectural approaches, each with its own strengths and use cases. Here are the main types of data warehouse architectures:

  1. Single-Tier Architecture:

    • All data is stored in a single, centralized repository

    • Rarely used in practice due to performance and scalability limitations

  2. Two-Tier Architecture:

    • Separates the data sources from the data warehouse

    • Consists of a database server and clients accessing it

  3. Three-Tier Architecture: This is the most common and widely used architecture, consisting of:

    a) Bottom Tier: Database server, usually a relational database system b) Middle Tier: OLAP (Online Analytical Processing) server c) Top Tier: Client front-end tools for querying and reporting

  4. Bus Architecture:

    • Utilizes shared dimensions (conformed dimensions) across different data marts

    • Allows for incremental development of the data warehouse

  5. Hub-and-Spoke Architecture:

    • Central data warehouse (hub) feeds departmental data marts (spokes)

    • Ensures consistency across the organization while allowing for customization

  6. Federated Architecture:

    • Integrates multiple autonomous data sources without centralizing them

    • Useful when full integration is impractical or undesirable

  7. Data Vault Architecture:

    • Focuses on long-term resilience to change and auditability

    • Separates business keys, relationships, and descriptive attributes

  8. Kimball's Dimensional Model:

    • Uses fact tables and dimension tables

    • Optimized for query performance and ease of use

  9. Inmon's Corporate Information Factory (CIF):

    • Advocates for a centralized, normalized data warehouse

    • Data marts are derived from this central repository

  10. Lambda Architecture:

    • Combines batch processing and real-time processing

    • Consists of batch layer, speed layer, and serving layer

  11. Data Lakehouse:

    • Combines elements of data warehouses and data lakes

    • Aims to provide structure and performance on top of low-cost storage

Each of these architectures has its own advantages and is suited to different organizational needs, data volumes, and analytical requirements. The choice of architecture depends on factors such as the organization's size, data complexity, reporting needs, and existing infrastructure.

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Last updated 6 months ago