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Begin by setting up your Google Cloud environment. Ensure that you have a Google Cloud account and that you are logged into the Google Cloud Console. Create a new project or use an existing one. Enable the Google Cloud Pub/Sub API for your project to allow message publishing and subscription.
In the Google Cloud Console, navigate to the Pub/Sub section and create a new topic. This topic will be the destination for the data you retrieve from the RKI COVID dataset. Take note of the topic name, as you will need it for publishing messages.
Install the necessary tools on your local machine. Ensure you have Python installed along with the `google-cloud-pubsub` library. You can install it using pip with the command: `pip install google-cloud-pubsub`. Additionally, set up authentication by downloading a service account key from your Google Cloud project and set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the key file.
Determine how to access the RKI COVID data. Typically, this data can be accessed via a public API or as downloadable datasets. For this guide, assume you have access to a public API endpoint that provides the COVID data in JSON format.
Write a Python script to fetch data from the RKI COVID API. Use the `requests` library to make HTTP requests to the API endpoint. Parse the JSON response and extract the necessary data that you want to publish to Google Pub/Sub.
Use the `google-cloud-pubsub` library in your Python script to publish the fetched RKI COVID data to the Pub/Sub topic you created. Create a publisher client, and use the `publish` method to send messages to the topic. Convert your data into a string or byte format before publishing.
After publishing the data, verify that it is being sent to the Pub/Sub topic. You can use the Google Cloud Console to monitor the topic and check for incoming messages. Additionally, you can create a subscriber to consume messages from the topic to ensure the data transfer is successful.
By following these steps, you will have successfully moved data from the RKI COVID dataset to Google Pub/Sub without using any 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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