Just built a custom exporter to get Arize data into our data warehouse

So the latest sprint retrospective is over, and the verdict is in: we need "unified analytics" for our ML models. Management, having read a few blog posts, decided Arize AI is the "single pane of glass" that will solve all our problems. They spun up a POC, ingested a few months of inference logs, and declared victory. Now the data science team wants all that Arize data—drift metrics, performance stats, the works—alongside our core business data in Snowflake for their "holistic analysis." Their initial plan? "Let's just use the Arize UI and maybe export some CSVs." I nearly spat out my coffee.

Of course, Arize has an API. And yes, they have a Python SDK. But querying historical data for multiple models, across multiple time windows, and then transforming that into a clean, incremental load for our warehouse? That's not a one-liner. The API is clearly built for interactive use, not bulk data extraction. So, after a futile attempt to argue for a simpler, cheaper logging pipeline directly to S3 (a battle I lost), I was tasked with building a custom exporter. The goal: pull data from Arize's APIs, structure it, and land it in Snowflake reliably without incurring astronomical egress costs or API rate limit penalties.

Here's the core of the scraper I built. It's a Python service that runs as a Kubernetes CronJob, leveraging the Arize SDK but focusing on pagination and state management. The key is to store the last successful timestamp for each model/metric pair to avoid full table scans every time.

```python
import arize
from arize.api import Client
import pandas as pd
from datetime import datetime, timedelta
import pickle
import os

# Initialize client (credentials from env)
arize_client = Client(organization_key=os.environ['ARIZE_ORG_KEY'],
api_key=os.environ['ARIZE_API_KEY'])

def fetch_metrics_for_model(model_id, metric_key, start_time, end_time):
"""
Fetch metrics with pagination handling.
Arize's `get_model_metrics` returns a generator, but you need to be careful with windows.
"""
try:
response = arize_client.get_model_metrics(
model_id=model_id,
metric_keys=[metric_key],
start_time=start_time,
end_time=end_time,
time_interval='DAY'
)
# The response is tricky; you need to iterate and handle potential limits
records = []
for metric in response:
# Each metric point needs to be flattened from its nested structure
records.append({
'timestamp': metric.timestamp,
'model_id': model_id,
'metric_name': metric_key,
'value': metric.value,
'batch_id': metric.batch_id # can be None
})
return pd.DataFrame(records)
except Exception as e:
print(f"Failed for {model_id}/{metric_key}: {e}")
return pd.DataFrame()

# Main loop logic: iterate through models and predefined metrics,
# check checkpoint, fetch incremental data, upload to Snowflake stage.
```

The real "fun" began with the subtleties. The Arize SDK's `get_model_metrics` sometimes returns `None` for perfectly valid time ranges. The timestamp alignment is off by timezones if you're not careful. The `batch_id` is essential for joining some of their other data, but it's not always present. And of course, there's no built-in idempotency key, so you have to de-duplicate on your end. We're now loading this into a Snowflake table with a unique constraint on `(model_id, metric_name, timestamp, batch_id)` and using a `MERGE` statement to upsert.

Was it worth it? On one hand, we now have the data where the data scientists want it, and we can join model performance to business KPIs. On the other hand, we've built and now maintain a fragile pipeline that depends on Arize's API stability, all to move data *out* of a platform we pay a premium for to put it into another platform we pay a premium for. The irony is palpable. We're essentially paying Arize to be a middleman, then spending engineering cycles to bypass their UI to get our own data back. The total cost of this "unified analytics" now includes Arize's subscription, the Snowflake compute credits for the hourly loads, and the ongoing devops to keep this exporter alive. I've estimated that a simpler, direct logging approach from our inference services would have been 60% cheaper on ongoing costs. But try telling that to someone who's already seen a shiny demo.

-- cynical ops