How to load data from Exchange Rates Api to BigQuery
Learn how to use Airbyte to synchronize your Exchange Rates Api data into BigQuery within minutes.
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.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- 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
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
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."
How to Sync to Manually
Step 1: Set Up a Google Cloud Platform (GCP) Project
Begin by setting up a Google Cloud Platform project if you don’t already have one. Go to the Google Cloud Console, create a new project, and make a note of the Project ID. Ensure that billing is enabled for your project.
Step 2: Enable BigQuery and Cloud Storage APIs
Navigate to the GCP Console's API & Services dashboard. Enable both the BigQuery API and the Cloud Storage API. This is essential as you will need these services to store and analyze your data.
Step 3: Obtain Exchange Rates API Access
Sign up or log in to the Exchange Rates API service. Generate an API key, which you will use to authenticate your requests to the API. Note down the endpoint URL and any query parameters you might need for fetching the data.
Step 4: Fetch Data Using a Python Script
Write a Python script to perform an HTTP GET request to the Exchange Rates API. Use libraries such as `requests` to fetch the data. Ensure your script processes and formats the data correctly, typically in JSON or CSV format. The script might look like this:
```python
import requests
import json
api_key = 'YOUR_API_KEY'
url = f'https://api.exchangeratesapi.io/v1/latest?access_key={api_key}'
response = requests.get(url)
data = response.json()
# Process and save the data to a local file
with open('exchange_rates.json', 'w') as f:
json.dump(data, f)
```
Step 5: Upload Data to Google Cloud Storage
Use the Google Cloud SDK to upload your JSON or CSV file to a Google Cloud Storage bucket. First, create a storage bucket via the GCP Console. Then, use the `gsutil` command-line tool to upload your file:
```bash
gsutil cp exchange_rates.json gs://your-bucket-name/
```
Step 6: Load Data into BigQuery
Access the BigQuery Console, and navigate to your dataset or create a new one. Use the BigQuery Data Transfer Service to load data from your Google Cloud Storage bucket into BigQuery. You can do this through the UI by selecting "Create Table" and specifying the source as your JSON or CSV file in Cloud Storage. Configure the schema appropriately to match the data structure.
Step 7: Schedule Regular Data Transfers
To automate the data transfer process, schedule a cron job on your local machine or a VM instance in GCP to run your Python script at regular intervals. Ensure the script fetches the latest data, uploads it to Cloud Storage, and then loads it into BigQuery. Use `cron` for Linux or Task Scheduler for Windows to set up these periodic tasks, ensuring the entire pipeline runs smoothly and consistently.
By following these steps, you can efficiently move data from the Exchange Rates API to BigQuery without relying on third-party connectors.