Gunadarma Data Warehousing and Mining Study Guide, Study Guides, Projects, Research of Database Management Systems (DBMS)

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Data Warehousing & Mining

Database System

Informatics Engineering

Gunadarma University

Introduction to Data Warehousing: Data warehousing involves the process of collecting, storing, and managing large volumes of data from various sources to support decision-making processes within an organization. A data warehouse is a central repository that integrates data from different operational systems and provides a unified view of the organization's data for analysis and reporting purposes. Key Concepts in Data Warehousing:

1. Data Warehouse Architecture: o Operational Data Sources: Extract data from multiple operational systems, such as transactional databases, ERP systems, and CRM systems. o Data Staging Area: Clean, transform, and integrate data from different sources before loading it into the data warehouse. o Data Warehouse: Central repository for storing structured, historical data optimized for reporting and analysis. o Metadata Repository: Store metadata (data about data) describing the structure, content, and lineage of data in the data warehouse.
2. Data Warehousing Models: o Star Schema: Organize data into a central fact table surrounded by dimension tables, forming a star-like structure. o Snowflake Schema: Extends the star schema by normalizing dimension tables into multiple levels of hierarchy, resembling a snowflake shape. o Fact Constellation: Includes multiple fact tables that share dimension tables, allowing for more complex analytical queries.
3. ETL Process: o Extract: Retrieve data from source systems, including data cleansing and validation. o Transform: Convert and integrate data into a standardized format suitable for analysis, applying business rules and data quality checks.

Example of Data Warehousing and Mining:

1. Data Warehousing Example: o Data Extraction: o Data Transformation: o Data Loading:
2. Data Mining Example: o Classification: o Clustering:

Conclusion: Data warehousing and mining play vital roles in extracting actionable insights and knowledge from large volumes of data. By understanding the concepts of data warehousing architecture, ETL processes, data mining techniques, and algorithms, organizations can leverage their data assets to gain competitive advantages, improve decision-making processes, and drive business growth. Practice implementing data warehousing and mining solutions using real-world datasets to enhance your skills in data analysis and decision support.