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Begin by setting up Google Firestore in your Google Cloud Platform (GCP) project. Navigate to the Firebase Console, create a new project (or use an existing one), and enable Firestore. Choose between Native and Datastore mode, typically opting for Native for new projects.
Obtain access to the exchange rates API by registering for an API key. If using a free or paid service, ensure you review the documentation to understand authentication methods and endpoint structures.
Write a script to fetch data from the exchange rates API. This can be done in Python, JavaScript, or any preferred language. Use libraries like `requests` in Python or `fetch` in JavaScript to make GET requests to the API, including your API key in the request headers or URL as required.
Process the fetched data to match the structure expected in Firestore. Typically, this involves parsing the JSON response and organizing it into documents and collections. Ensure the data types align with Firestore requirements (e.g., strings, numbers, timestamps).
Configure Firestore security rules to allow your script to write data. If the script will run from a secure environment, you might temporarily set rules to allow read and write access to authenticated users. For more security, consider using Firebase Admin SDK with service account credentials.
Install the Firebase Admin SDK in your development environment to interact with Firestore programmatically. Use package managers like `npm` for Node.js or `pip` for Python to install the SDK. Initialize the SDK with your project credentials, typically a service account JSON key file.
Use the initialized Firebase Admin SDK to write the processed data to Firestore. Create collections and documents that mirror your data structure. Implement error handling to manage API limits and potential write failures. Run your script to periodically fetch and update the data as needed.
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By following these steps, you can effectively move data from an exchange rates API to Google Firestore using direct script-based solutions, ensuring control over the data flow without relying on third-party connectors.
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.
Used by tens of thousands of developers, Exchange Rates API provides accurate and reliable currency data instantly through its free, simple-to-use API interface. With more than 10 years of exceptional API uptime and support, developers trust Exchange Rates API to provide fast and accurate conversion rates for 160 different currencies as well as essential stock market data in JSON format. They have worked hard to achieve their mission of building a remarkably hardware efficient and reliable currency converter API.
Exchange Rates API provides access to various types of data related to currency exchange rates. The API offers real-time and historical exchange rates for over 170 currencies, including cryptocurrencies. The following are the categories of data that the Exchange Rates API provides:
• Real-time exchange rates: The API provides real-time exchange rates for various currencies, which are updated every minute.
• Historical exchange rates: The API offers historical exchange rates for up to 10 years, allowing users to analyze trends and patterns in currency exchange rates.
• Currency conversion: The API allows users to convert one currency to another using the latest exchange rates.
• Time-series data: The API provides time-series data for exchange rates, allowing users to track changes in exchange rates over time.
• Currency metadata: The API provides metadata for various currencies, including their names, symbols, and ISO codes.
• Cryptocurrency data: The API provides real-time exchange rates for various cryptocurrencies, including Bitcoin, Ethereum, and Litecoin.
Overall, the Exchange Rates API provides a comprehensive set of data related to currency exchange rates, making it a valuable resource for businesses and individuals who need to track currency exchange rates.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
