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First, identify the source from where you will obtain the RKI COVID data. Typically, this data can be accessed via their official website or through a public API endpoint. Download the data in a format suitable for manual processing, such as CSV or JSON.
Ensure that you have Python, a command-line interface, and PostgreSQL installed on your local machine. You will use Python scripts to process the data and interact with PostgreSQL. Verify that the PostgreSQL server is running and accessible.
Using Python, load the downloaded RKI COVID data. You can use libraries like `pandas` for CSV files or `json` for JSON files to read and manipulate the data. Clean and transform the data so that it aligns with the structure of your PostgreSQL tables (e.g., ensuring data types and formats match).
Access your PostgreSQL database through the command line or a GUI tool like pgAdmin. Create the necessary tables to host the RKI COVID data. Define your tables based on the transformed data's structure, specifying appropriate data types for each column.
Develop a Python script that connects to your PostgreSQL database using the `psycopg2` library. This script should iterate over the transformed data and insert each record into the corresponding PostgreSQL table. Use SQL `INSERT` statements within your script, ensuring to handle any exceptions or errors during the insertion process.
Run your Python script to transfer the data from your local environment to the PostgreSQL database. Monitor the process to ensure all records are inserted successfully. Check for any errors in the log output and resolve them as needed.
Once the data is inserted, verify its integrity and accuracy by running SQL queries directly on your PostgreSQL database. Compare the record counts and sample data between your source file and the database to ensure consistency and correctness. Make necessary adjustments if discrepancies are found.
By following these steps, you can manually transfer RKI COVID data into your PostgreSQL database without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
RKI stands for the Robert Koch Institute is continuously monitoring the situation, evaluating all available information, estimating the risk for the population in Germany. RKI Corvid provides selected information on COVID-19 available in English. In the connector source RKI Corvid we want to add streams for the states that include history data , incidence rate , cases , deaths and so on.
The RKI Covid's API provides access to a wide range of data related to the Covid-19 pandemic in Germany. The data can be categorized into the following categories:
1. Case data: This includes information on the number of confirmed cases, deaths, and recoveries in Germany.
2. Testing data: This includes information on the number of tests conducted, the positivity rate, and the number of tests per capita.
3. Hospitalization data: This includes information on the number of hospitalizations, ICU admissions, and ventilator use.
4. Vaccination data: This includes information on the number of people vaccinated, the number of doses administered, and the percentage of the population vaccinated.
5. Geographic data: This includes information on the number of cases and deaths by state, district, and municipality.
6. Demographic data: This includes information on the age, gender, and ethnicity of Covid-19 patients.
7. Time series data: This includes information on the daily and cumulative number of cases, deaths, and vaccinations over time.
Overall, the RKI Covid's API provides a comprehensive set of data that can be used to track the spread of Covid-19 in Germany and inform public health policies and interventions.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
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