How to load data from Coin API to BigQuery
Learn how to use Airbyte to synchronize your Coin 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 Google Cloud Project
First, log in to your Google Cloud Console and create a new project or select an existing one. Ensure that billing is enabled for the project, as BigQuery requires an active billing account. Navigate to the "BigQuery" section and activate the API if it is not already enabled.
Step 2: Obtain Coin API Key
Visit the Coin API website and sign up for an account if you haven't already. Once registered, navigate to the API section to generate an API key. This key will be used to authenticate your requests to the Coin API.
Step 3: Write a Python Script to Fetch Data
Create a Python script using the `requests` library to fetch data from the Coin API. Use the API key obtained in step 2 for authentication. For example:
```python
import requests
api_key = 'YOUR_COIN_API_KEY'
url = 'https://rest.coinapi.io/v1/exchangerate/BTC/USD'
headers = {'X-CoinAPI-Key': api_key}
response = requests.get(url, headers=headers)
data = response.json()
```
Step 4: Transform Data into BigQuery-Compatible Format
Process the data fetched from the Coin API into a format suitable for BigQuery. Typically, this involves converting the data into a JSON format or a CSV format. Ensure that the data types align with BigQuery's supported data types. For JSON, ensure that the structure is flat or can be easily parsed.
Step 5: Create a BigQuery Dataset and Table
In the Google Cloud Console, navigate to BigQuery. Create a new dataset if one doesn't exist by clicking on "Create dataset,"� and specify the dataset ID. Within the dataset, create a new table with the appropriate schema that matches the data you are importing.
Step 6: Use Google Cloud SDK to Upload Data
Install the Google Cloud SDK on your local machine if it's not already installed. Use the `bq` command-line tool to upload the data file. For example, if your data is in a JSON file:
```bash
bq load --source_format=NEWLINE_DELIMITED_JSON your_dataset.your_table /path/to/your/data.json
```
Ensure that the data types in your JSON file match the schema of the BigQuery table.
Step 7: Automate the Process
To automate the data fetching and uploading process, consider using a task scheduler like `cron` on Unix-based systems or Task Scheduler on Windows. Create a script that runs your Python data-fetching script and then executes the `bq load` command. Schedule it to run at regular intervals to keep your BigQuery data updated.
By following these steps, you can effectively move data from the Coin API to BigQuery without relying on third-party connectors or integrations.