Name: Data Warehouse
Uploaded: 2025-11-11
Duration: 2 min 40 s
Description: Data Warehouse Professional Course Presentation Prepared by: Jashandeep Singh.

[Audio] Data Warehouse Professional Course Presentation Prepared by: Jashandeep Singh. 

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Professional Course Presentation Prepared by: Jashandeep Singh

[Audio] Introduction • Central repository integrating data from multiple sources. • Supports business intelligence and decision-making. • Optimized for querying and analysis, not transaction processing.. 

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• Central repository integrating data from multiple sources. • Supports business intelligence and decision-making. • Optimized for querying and analysis, not transaction processing.

[Audio] Key Characteristics • Subject-oriented • Integrated • Time-variant • Non-volatile. 

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• Subject-oriented • Integrated • Time-variant • Non-volatile

[Audio] Architecture Overview • Data Sources → ETL Process → Data Storage → Presentation Layer • ETL: Extract, Transform, Load • Presentation: Analytical tools, dashboards, reports. 

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• Data Sources → ETL Process → Data Storage → Presentation Layer • ETL: Extract, Transform, Load • Presentation: Analytical tools, dashboards, reports

[Audio] ETL Process • Extraction – Collects data from multiple sources. • Transformation – Cleans, formats, and validates data. • Loading – Stores data into the warehouse.. 

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• Extraction – Collects data from multiple sources. • Transformation – Cleans, formats, and validates data. • Loading – Stores data into the warehouse.

[Audio] OLAP (Online Analytical Processing) • Enables fast, multidimensional data analysis. • Supports slicing, dicing, roll-up, and drill-down operations. • Improves decision-making efficiency.. 

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• Enables fast, multidimensional data analysis. • Supports slicing, dicing, roll-up, and drill-down operations. • Improves decision-making efficiency.

[Audio] Schema Models • Star Schema – Central fact table connected to dimension tables. • Snowflake Schema – Normalized dimension structure. • Galaxy Schema – Multiple fact tables share dimension tables.. 

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• Star Schema – Central fact table connected to dimension tables. • Snowflake Schema – Normalized dimension structure. • Galaxy Schema – Multiple fact tables share dimension tables.

[Audio] Benefits of Data Warehousing • Enhanced decision-making • Consistent and accurate data • Historical trend analysis • Faster query performance. 

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• Enhanced decision-making • Consistent and accurate data • Historical trend analysis • Faster query performance

[Audio] Tools & Technologies • Microsoft SQL Server • Oracle Data Warehouse • Amazon Redshift • Snowflake • Google BigQuery. 

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• Microsoft SQL Server • Oracle Data Warehouse • Amazon Redshift • Snowflake • Google BigQuery

[Audio] Real-World Applications • Retail – Sales forecasting • Banking – Customer analytics • Healthcare – Patient records analysis • Education – Student performance tracking. 

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• Retail – Sales forecasting • Banking – Customer analytics • Healthcare – Patient records analysis • Education – Student performance tracking

[Audio] Conclusion • Data Warehousing integrates and organizes data efficiently. • Helps organizations make data-driven decisions. • Foundation for business intelligence and analytics.. 

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• Data Warehousing integrates and organizes data efficiently. • Helps organizations make data-driven decisions. • Foundation for business intelligence and analytics.

Data Warehouse


Présentation du Cours Professionnel sur le Data Warehouse Préparée par: Jashandeep Singh

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Introduction

The central repository is responsible for integrating data from various sources. It is designed to support business intelligence and aid in decision making. It is specifically optimized for querying and analyzing data, rather than handling transaction processing.


Key Characteristics:

 Subject oriented
 Integrated
 Time variant
 Non volatile


Architecture Overview

Data Sources → E-T-L Process → Data Storage → Presentation Layer 
E-T-L-: Extract, Transform, Load 
Presentation: Analytical tools, dashboards, reports


E-T-L Process

The E-T-L process involves three stages: Extraction, Transformation, and Loading. First, data is extracted from various sources. Then, it is transformed by cleaning, formatting, and validating it. Finally, the data is loaded and stored into the warehouse.


olap (Online Analytical Processing) enables rapid analysis of multidimensional data. It supports the actions of slicing, dicing, roll up, and drill down. It enhances the efficiency of decision making.


Schema Models

Star Schema – A model where a central fact table is connected to dimension tables. 
Snowflake Schema – A model with a normalized dimension structure. 
Galaxy Schema – A model where multiple fact tables share dimension tables.


Some potential benefits of data warehousing are improved decision making, the availability of consistent and accurate data, the ability to analyze historical trends, and faster query performance.


Tools & Technologies
 
Microsoft S-Q-L Server 
Oracle Data Warehouse 
Amazon Redshift 
Snowflake 
Google BigQuery


Real World Applications

Retail  Predicting sales
Banking  Analyzing customer data
Healthcare  Examining patient records
Education  Tracking student performance


Conclusion: 

Data Warehousing is a system that efficiently integrates and organizes data. This helps organizations to make decisions based on data. It serves as the foundation for business intelligence and analytics. 

Data Warehouse

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