Migrating our Schemaless sharding layer from Python to Go while in production demonstrated that it was possible for us to rewrite the frontend of a massive datastore with zero downtime.
Uber Engineering's data processing platform team recently built and open sourced Hoodie, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hoodie powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.
The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview and the second part is a discussion of architecture.