ETL DataStage

Data Warehousing is the process of collecting, storing, and managing data from various sources in a central repository. It involves the use of technologies and methodologies to organize and present data for reporting and analysis.

Data Warehousing is beneficial for organizations of all sizes and industries that require a centralized and efficient way to store, manage, and analyze large volumes of data. It is particularly valuable for businesses seeking to make data-driven decisions, perform analytics, and gain insights from their data.

Data Warehouses are required to provide a single source of truth for an organization’s data. They improve data quality, enable historical analysis, support decision-making processes, and enhance business intelligence by consolidating data from various sources into a structured format.

• OLTP (Online Transaction Processing): OLTP systems are designed for real-time transactional processing, focusing on day-to-day operations. They are optimized for high-speed data input and retrieval.
• OLAP (Online Analytical Processing): OLAP systems are designed for analytical and business intelligence purposes. They support complex queries, aggregations, and data analysis to extract insights from historical data.

Data Modeling is the process of creating a structured representation of data and its relationships within a database or data warehouse. It involves defining tables, columns, keys, and constraints to ensure data accuracy and integrity.

The steps in Data Modeling typically include:

• Identifying business requirements.
• Creating an Entity-Relationship Diagram (ERD).
• Defining tables, columns, and relationships.
• Normalizing data to reduce redundancy.
• Applying constraints and data types.
• Documenting the model.

Data Modeling can be categorized into three levels:

• Conceptual Data Modeling: This level focuses on high-level business concepts and relationships.
• Logical Data Modeling: It defines the structure of data without considering specific database technologies.
• Physical Data Modeling: This level describes the actual implementation of the data model in a specific database system.

Common modeling techniques include:

• Entity-Relationship Diagrams (ERD): Visual representations of entities and their relationships.
• Normalization: Organizing data to minimize redundancy.
• Dimensional Modeling: Designing data models for data warehousing and OLAP.

Start Schema and Snowflake Schema are two common techniques used in data warehousing to design the structure of relational databases for efficient querying and reporting.