Summarize this article with:
Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
First, you need to access the exchange rates API to fetch the data. Use Python's `requests` library to send an HTTP GET request to the API endpoint. Ensure you have the necessary API key if required. Store the response data in a structured format such as JSON.
Once you have the data from the API, transform it into a format suitable for storage. You might need to clean the data or format it as CSV or JSON to make it compatible with your intended storage in Amazon S3. Use Python's `pandas` library for data manipulation if necessary.
Ensure that the AWS CLI is installed and configured with the necessary permissions on your local machine or the EC2 instance from where you will run the script. Use the `aws configure` command to set your AWS Access Key ID, Secret Access Key, and default region.
Use Python's `boto3` library, which is the AWS SDK for Python, to upload the transformed data to an S3 bucket. Create an S3 client using `boto3.client('s3')` and use the `upload_file` or `put_object` methods to write your data to the designated S3 bucket.
In the AWS Management Console, navigate to AWS Glue and create a new Glue Crawler. Configure the crawler to scan the S3 bucket where you have uploaded the data. Set the crawler to update the Glue Data Catalog with the schema of your data.
Execute the AWS Glue Crawler to scan your S3 bucket. This process will create or update tables in the Glue Data Catalog, making your data ready for further ETL operations or queries using AWS Glue jobs or Amazon Athena.
Create an AWS Glue ETL job to process the data as needed. Use the Glue Studio or the Glue console to write a script that reads from the Data Catalog, performs any further transformations, and writes the output back to S3 or another destination. Execute the job and monitor its progress through the AWS Glue console.
By following these steps, you can move data from an exchange rates API to Amazon S3 using AWS Glue, leveraging AWS SDKs and services without any 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:
