How to load data from BigQuery to Convex
Learn how to use Airbyte to synchronize your BigQuery data into Convex within minutes.
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How to Sync to Manually
Step 1: Understand Data Structure and Export Requirements
Before you begin, clearly define which data you need to move from BigQuery to Convex. Understand the data schema, data types, and any transformations required during the transfer. This will help in planning the export and import processes effectively.
Step 2: Export Data from BigQuery to CSV
Use BigQuery's built-in export functionality to export the data. You can do this via the BigQuery console or using a SQL command. Export the data to Google Cloud Storage (GCS) in CSV format:
```sql
EXPORT DATA OPTIONS(
uri='gs://your-bucket-name/your-file-name-*.csv',
format='CSV',
overwrite=true
) AS
SELECT * FROM your_dataset.your_table;
```
This command exports the data to the specified GCS bucket.
Step 3: Download CSV Files from Google Cloud Storage
Once the data is exported to GCS, download the CSV files to your local machine or a server where you can perform the next steps. You can use the `gsutil` command-line tool:
```
gsutil cp gs://your-bucket-name/your-file-name-*.csv /local/path/
```
Step 4: Prepare Data for Convex Import
Ensure the CSV files meet any specific formatting requirements needed for Convex. This might involve cleaning the data, ensuring proper encoding (e.g., UTF-8), and confirming the delimiter matches what Convex expects.
Step 5: Set Up a Convex API Endpoint
If not already set up, create a Convex project and establish an API endpoint to import data. This involves setting up a function in Convex that can handle the incoming data and insert it into the correct data structures.
Step 6: Write a Script to Parse and Upload Data
Write a script in a language of your choice (e.g., Python, Node.js) to read the CSV files and send the data to the Convex API endpoint. Here"s a basic example in Python using requests:
```python
import csv
import requests
# Define your Convex API endpoint
CONVEX_API_ENDPOINT = "https://your-convex-endpoint/your-function"
# Function to upload data
def upload_to_convex(row):
response = requests.post(CONVEX_API_ENDPOINT, json=row)
if response.status_code != 200:
print(f"Failed to upload row: {row}")
else:
print(f"Successfully uploaded row: {row}")
# Read and upload CSV data
with open('/local/path/your-file-name.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
upload_to_convex(row)
```
Step 7: Verify Data in Convex
After the data is uploaded, verify its integrity and accuracy. Check the Convex database to ensure that all records have been inserted correctly and that the data matches the source. This may involve running validation queries or checks in Convex.
By following these steps, you can efficiently move data from BigQuery to Convex without the need for third-party connectors or integrations.