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by jvican 807 days ago
Hey! I work on the ML Feature Infra at Netflix, operating a similar system to Chronon but with some crucial differences. What other alternatives aside from Starrocks did you evaluate as potential replacements prior to building Chronon? Curious if you got to try Tecton or Materialize.com.
1 comments

We haven’t tried materialize - IIUC materialized is pure kappa. Since we need to correct upstream data errors and forget selective data(GDPR) automatically - we need a lambda system.

Tecton, we evaluated, but decided that the time-travel strategy wasn’t scalable for our needs at the time.

A philosophical difference with tecton is that, we believe the compute primitives (aggregation and enrichment) need to be composable. We don’t have a FeatureSet or a TrainingSet for that reason - we instead have GroupBy and Join.

This enables chaining or composition to handle normalization (think 3NF) / star-schema in the warehouse.

Side benefit is that, non ml use-cases are able to leverage functionality within Chronon.

FeatureSets are mutable data and TrainingSets are consistent snapshots of feature data (from FeatureSets). I fail to see what that has to do with composability. Join is still available for FeatureSets to enable composable feature views - join is resuse of feature data. GroupBy is just an aggregation in a feature pipeline, not sure your point here. You can still do star schema (and even snowflake schema if you have the right abstractions).
Normalization is a model-dependent transformation and happens after the feature store - needs to be consistent between training and inference pipelines.
Normalization is overloaded. I was referring to schema normalization (3NF etc) not feature normalization - like standard scaling etc.
Ok, but star schema is denormalized. Snowflake is normalized.
To be pedantic, even in star schema - the dim tables are denormalized, fact tables are not.

I agree that my statement would be much better if used snowflake schema instead.

What is the meaning of pure kappa?
Thank you for sharing!