Data Warehousing Basics - Aamir P
Hello all! Today we will see about Data Warehousing Basics.
In data warehousing, the two main types of data models are the star schema and the snowflake schema. Star schema consists of a central fact table surrounded by dimension tables, while snowflake schema extends the star schema by normalizing dimension tables.
A schema is a logical structure that represents the organization of data in a database. In the context of data warehousing, schemas include star schemas, snowflake schemas, and galaxy schemas.
Dimensions are descriptive attributes or categorical variables by which the data is analyzed (e.g., time, geography, product).
Facts are numerical measures or metrics that represent the business process being analyzed (e.g., sales, revenue).
Measures are quantitative metrics or key performance indicators (KPIs) associated with a fact in a data warehouse. Examples include sales revenue, quantity sold, and profit margin.
ETL processes involve extracting data from source systems, transforming it into a suitable format, and loading it into the data warehouse. ETL is crucial for maintaining data quality and consistency.
OLAP (Online Analytical Processing) refers to a category of tools and technologies that allow users to interactively analyze multidimensional data. OLAP systems are designed for complex queries and reporting.
Data quality is a crucial aspect of data warehousing. It involves ensuring that the data is accurate, consistent, complete, and timely. Poor data quality can lead to inaccurate reporting and decision-making.
This is just an overview, let us know the benefits as well.
Benefits of data warehousing
1. Data warehouses provide a centralized and consistent view of data, facilitating better decision-making.
2. Data warehouses store historical data, enabling trend analysis and long-term planning.
3. Optimized for query and analysis, data warehouses provide faster access to large volumes of data.
Challenges of Data Warehousing
1. Handling large volumes of data and scaling the infrastructure can be challenging.
2. Protecting sensitive data from unauthorized access is a critical concern.
3. Ensuring data quality, integrity, and compliance with regulations requires effective governance.
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Security and Governance
Security measures include access controls, encryption, and auditing to protect sensitive data.
Governance involves policies, processes, and controls to manage data assets effectively and ensure compliance.
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