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Begin by obtaining the COVID dataset from RKI. This can typically be downloaded in a CSV or JSON format from the RKI’s official website. Ensure you have the latest dataset and save it locally on your system for further processing.
Open the downloaded dataset using a tool like Excel for CSV files or a text editor for JSON files. Review the data structure and clean it if necessary””remove any unwanted columns, fix data inconsistencies, and ensure there are no missing values that could cause issues during the transfer.
Convert the dataset into a format compatible with Starburst Galaxy. Depending on what Starburst Galaxy supports, this may involve using a scripting language like Python or shell scripts to transform the data into a SQL-friendly format or any other required structure.
Obtain access credentials to Starburst Galaxy, including necessary permissions to create tables and insert data. Ensure your account has the appropriate privileges for data manipulation tasks and understand the database schema or tables where the data will be loaded.
Use the Starburst Galaxy interface to create the required tables that match the structure of your transformed dataset. Define the appropriate data types for each column, ensuring they match the content and format of your dataset.
Utilize Starburst Galaxy’s SQL interface to manually load the data. If you have transformed the data into SQL insert statements, execute these directly in the Starburst Galaxy query editor. Alternatively, use the bulk insert feature if available, ensuring the data is correctly mapped to the defined table structure.
Once the data is loaded, run queries to verify that all data has been transferred correctly. Check for any discrepancies or errors in the dataset. Validate the data by comparing a few records with the original dataset to ensure accuracy and completeness.
By following these steps, you can manually move data from an RKI COVID dataset to a Starburst Galaxy environment 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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