Check out what I made: A script to sync Consensus data to BigQuery.

Hey everyone, I've been digging into Consensus for a project and really needed a way to get the aggregated research data out for deeper analysis alongside our other metrics. Their API is pretty solid, but I wanted a persistent data warehouse setup.

So I built a script that pulls from the Consensus API and syncs it directly to BigQuery on a schedule. This lets me run complex queries, join research findings with our internal product usage data, and build Looker Studio dashboards. Here's the core of it:

```python
import requests
import pandas as pd
from google.cloud import bigquery

CONSENSUS_API_KEY = 'your_key_here'
PROJECT_ID = 'your_project_id'
DATASET_ID = 'consensus'

def fetch_consensus_papers(query):
url = "https://consensus.app/api/v1/search/"
headers = {"Authorization": f"Bearer {CONSENSUS_API_KEY}"}
params = {"query": query, "size": 50} # adjust size as needed
response = requests.get(url, headers=headers, params=params)
return response.json().get('results', [])

def transform_to_dataframe(results):
# Flatten the nested JSON structure
rows = []
for paper in results:
rows.append({
'paper_id': paper.get('id'),
'title': paper.get('title'),
'year': paper.get('year'),
'citation_count': paper.get('citationCount'),
'consensus_strength': paper.get('consensusStrength'),
'abstract': paper.get('abstract')
})
return pd.DataFrame(rows)

# Fetch and transform
papers_data = fetch_consensus_papers("effects of intermittent fasting")
df = transform_to_dataframe(papers_data)

# Load to BigQuery
client = bigquery.Client(project=PROJECT_ID)
table_ref = f"{PROJECT_ID}.{DATASET_ID}.papers"
job = client.load_table_from_dataframe(df, table_ref)
```

I run this as a Cloud Function triggered by Cloud Scheduler every week. The main things I had to handle:

* **Pagination:** The script above fetches the first page; you'll need a loop to get all results if you have many.
* **Schema management:** I defined the BigQuery table schema upfront to control data types.
* **Incremental updates:** I modified it to use `paper_id` as a key to avoid full reloads.

Now I can easily track things like:
- Which health topics have the strongest consensus over time?
- Correlation between citation count and consensus strength.
- Merging this with our user research data in the same warehouse.

Has anyone else tried piping Consensus data elsewhere? I'm curious about other destinations like Snowflake or Airtable. If you want the full Cloud Function code with error handling, just let me know!