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Begin by setting up a development environment where you can write and execute scripts. Ensure you have Python installed, as it will be used for fetching and processing data. Also, have a Java environment ready since Apache Iceberg is a Java-based library.
Write a Python script to fetch data from the exchange rates API. Use Python's `requests` library to send HTTP requests to the API and retrieve the JSON response. Store this data in a structured format, such as a dictionary or a Pandas DataFrame, to facilitate further processing.
Process the fetched data into a format suitable for loading into Iceberg. If the data requires transformation (e.g., renaming fields, converting data types), perform these operations using Pandas or native Python data structures. This step ensures the data aligns with the schema you intend to use in Iceberg.
Set up a new Iceberg table where the exchange rate data will be stored. Use a SQL engine like Apache Spark or Flink that supports Iceberg, or configure Apache Hive for Iceberg. Define the table schema that matches the processed data structure. For example, set up fields for currency codes, exchange rates, and timestamps.
Use the Pandas library to convert your processed DataFrame into a Parquet file. Parquet is a columnar storage file format optimized for use with big data processing frameworks, including Apache Iceberg. Use `pandas.to_parquet()` function to write your DataFrame to a Parquet file on disk.
Load the Parquet files into the Iceberg table. Using Apache Spark, you can read the Parquet file and write it to Iceberg using Spark SQL. Run a Spark job where you load the Parquet file, create a DataFrame from it, and use Spark's `write` method to insert the data into the Iceberg table.
After loading the data, run queries against the Iceberg table to ensure that the data has been correctly ingested. Use your SQL engine to perform checks, such as counting the number of records, verifying data types, and checking for any discrepancies between the source data and the data in Iceberg. This step ensures the accuracy and consistency of your data transfer process.
By following these steps, you can successfully move data from an exchange rates API to Apache Iceberg 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.
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?
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