I'm the creator of Arroyo (and have talked a lot with the Denormalized folks) so maybe can answer from my perspective (and Matt and Amey please correct me on any inaccuracies.)
First the similarities: both Arroyo and Denormalized use DataFusion and Arrow and are focused on high-scale, low-latency stateful stream processing.
Arroyo has been around a lot longer and is overall more mature. It's distributed (I believe Denormalized at this point is a single-node engine), supports consistent snapshotting of its state, event time and watermarks, and has a wide range of supported connectors (https://doc.arroyo.dev/connectors). It ships with a control plane, distributed schedulers, and web ui.
But the use cases we're targeting are different. Arroyo programmed via SQL, and is used primarily for real-time data pipelines; we aim to replace Flink SQL and kSQL.
Denormalized (as I understand it) is focused more on data science use cases where it makes sense to have an embedded engine, rather than a distributed one. It's programmed with a Rust dataframe API (and soon Python).
Fluvio is an edge to core cloud native streaming engine built from the ground up in rust. Compiles to a single 37 Meg binary and deploys on ARM64 devices.
We just released the first public beta version of Stateful DataFlow. Stateful DataFlow is a framework for building unbounded distributed stream processing based on wasm that runs on Fluvio streams.
We are going for a Lean alternative to Kafka + Flink with a user experience of Ruby on Rails.
BTW, Stateful DataFlow has integrations with Arrow, Polars, and the ability to use SQL for dataframes, and other wasm compatible programming languages to express business logic. And Fluvio has Rust, Python, and JS clients.
while haven't checked out Fluvio yet, we are fans of Arroyo. regarding latter my understanding is that the team is going for a SQL first complete replacement for Flink. Denormalized is meant to be an embeddable engine you can import within your project. Our plan is to focus on the developer experience for users building with Python and Typescript in particular.
First the similarities: both Arroyo and Denormalized use DataFusion and Arrow and are focused on high-scale, low-latency stateful stream processing.
Arroyo has been around a lot longer and is overall more mature. It's distributed (I believe Denormalized at this point is a single-node engine), supports consistent snapshotting of its state, event time and watermarks, and has a wide range of supported connectors (https://doc.arroyo.dev/connectors). It ships with a control plane, distributed schedulers, and web ui.
But the use cases we're targeting are different. Arroyo programmed via SQL, and is used primarily for real-time data pipelines; we aim to replace Flink SQL and kSQL.
Denormalized (as I understand it) is focused more on data science use cases where it makes sense to have an embedded engine, rather than a distributed one. It's programmed with a Rust dataframe API (and soon Python).