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

Introduction

What is Data Warehouse

A data warehouse is a centralized repository that stores large volumes of structured data from various sources within an organization. It's designed to support business intelligence activities, including reporting, analytics, and data mining. Here are the key aspects of a data warehouse:

  1. Purpose: To provide a unified, consistent view of an organization's data for analysis and decision-making.

  2. Data integration: It combines data from multiple sources, such as transactional databases, operational systems, and external sources.

  3. Historical data: Unlike operational databases, data warehouses typically store historical data over long periods, allowing for trend analysis.

  4. Structure: Data is usually structured in a way that optimizes query performance, often using dimensional modeling techniques.

  5. Subject-oriented: Data is organized around major subjects of the enterprise (e.g., customers, products, sales).

  6. Non-volatile: Once data is loaded into the warehouse, it's generally not modified, ensuring data consistency over time.

  7. Time-variant: All data in the warehouse is associated with a specific time period.

  8. Query-optimized: Designed for complex queries and analysis rather than for transaction processing.

  9. ETL process: Data typically goes through Extract, Transform, Load (ETL) processes before entering the warehouse.

  10. Support for BI tools: Data warehouses are often used in conjunction with business intelligence tools for reporting and analysis.

Data warehouses play a crucial role in helping organizations make data-driven decisions by providing a comprehensive and historical view of their data.

Would you like me to elaborate on any specific aspect of data warehouses?

Explain as real world

Sure, I'll explain data warehouses in a way that's easier to understand, like talking to a 5-year-old:

Imagine you have a big toy box. But instead of toys, this box is full of information about your family:

  • What you eat for dinner

  • Where you go on weekends

  • What games you play

  • How much money your parents spend on groceries

This special box keeps all this information safe and organized. When your parents want to know something, like "What's our favorite food?" or "Where did we go last summer?", they can look in this box and find the answer quickly.

The box is always getting new information, but it never forgets the old stuff. So you can see what you liked to eat last year, or how tall you were when you were younger.

This big box of information is like a data warehouse. It helps grown-ups make smart choices by looking at all the information in one place.

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Last updated 6 months ago