Hey folks, hope everyone's migration projects are going smoothly! 👋
I'm deep into a Segment-to-mParticle migration for a client, and we're hitting a snag with the **raw data export to S3**. The format seems... inconsistent? We're trying to backfill historical events into our new pipelines, but the JSON structure in the exported files is causing our Spark jobs to choke.
Specifically, the main event payload is nested under a `data` key, which is fine, but the schema of objects *within* that array seems to change based on the event type in a way that isn't documented. Our parsers fail when a property is sometimes a string, sometimes an object.
Here's a simplified example of what we're seeing:
```json
{
"event_name": "Product Viewed",
"data": [
{
"timestamp": 1234567890,
"user_id": "abc123",
"properties": {
"product_id": "sku_123", // Sometimes a string...
"attributes": { "color": "blue" } // ...sometimes this whole object is missing.
}
}
]
}
```
And then for another event type, `product_id` might be a nested object with `value` and `type` fields. 😵💫
Has anyone else encountered this? My main questions are:
* Is there a reliable, documented schema for these exports, or are we expected to handle the variability ourselves?
* What's been your strategy for transforming this into a consistent format (e.g., Avro/Parquet) for loading into a data lake?
* Did you have to write a custom parser for each event type, or was there a smarter way?
We're considering using a schema-on-read approach with something like Delta Lake, but I'd love to hear real-world stories before we commit to that path. Any war stories or pro-tips would be massively appreciated!
-pipelinepilot
Pipeline Pilot
Oh wow, this is super helpful to see. I'm just starting to look at mParticle for our startup and data consistency is a huge concern for us down the line. So you're saying the schema for a field can just completely flip from a string to an object without warning?
Is there any kind of mapping or schema definition you can set up inside mParticle itself before the export, or are you just stuck with whatever raw format comes out? This seems like a big pitfall for setting up any kind of reliable data warehouse ingestion.
Yeah, you've hit on the core problem. You're generally stuck with what flows in from the source. mParticle is a pipe, not a data modeler, so if your mobile app sends a field as a string one day and a nested object the next, that's what gets exported.
The "mapping" they offer is mostly for forwarding to other destinations, not for restructuring the raw warehouse dump. You'll end up writing a lot of defensive, schema-on-read parsing logic in your Spark jobs or BigQuery views. Guess how many compute hours that burns through every month?
Show me the bill