data ETL

"Comprehensive COVID-19 Data Analysis: A Deep Dive into Vaccination, Case Trends, and Outcomes" The ETL project aims to provide important insights into the effectiveness as well as the effect of COVID-19 vaccinations across various demographic groups. The project will reveal trends and differences in vaccination rates and outcomes, such as infection and hospitalization rates, among different age groups, ethnicities, and genders by combining and analysing different datasets. This analysis is essential for informing public health decisions and strategies for dealing with current and future health crises. For missing values, use data imputation strategies. Standardise and normalise data formats such as date and time, as well as categorical labels. Transforming: Measurements should be converted to a unified scale. Surrogate keys should be used for seamless data integration. Using ETL tools, automate the addition of metadata columns (such as source and timestamp). Data Usage and Sources Data Used: COVID-19 Vaccination Coverage, Citywide COVID-19 Outcomes by Vaccination Status COVID-19 Vaccination and Case Trends by Age Group Sources:  https://data.gov/ Total rows: Approximately 10,660 (738 + 3591 + 5331 from each file). Keys: Primary Keys (PK): Composite keys likely formed by 'Week End', 'Age Group', and other demographic fields. Foreign Keys (FK): Used for linking datasets, possibly through common fields like 'Week End', 'Age Group'. Decision Support and Its Relationship to Excel Decision Support: Analyses COVID-19 vaccination effectiveness and outcomes to inform public health strategies. Identifies demographic groups that are at higher risk or have lower vaccination rates in order to target interventions. In comparison to Excel: Excel can perform basic analysis but is limited in its ability to process large datasets and complex ETL operations. For complex datasets, Excel lacks solid data integration and transformation capabilities. Benefits of This Approach: Increased data processing power for large datasets. ETL capabilities that are more sophisticated for cleaning, transforming, and integrating diverse data sources. Allows for more complex analyses and visualizations, which are required for thorough decision-making.