timely-dataflow VS flow

They have a bit about their technical foundation here[0].

Given that Readyset was co-founded by Jon Gjengset (but has apparently since departed the company), who authored the paper on Noria[1], I would assume that Readyset is the continuation of that research.

So it shares some roots with Materialize. They have a common conceptual ancestry in Naiad, where Materialize evolved out of timely-dataflow.

[0]: https://docs.readyset.io/concepts/streaming-dataflow

[1]: https://jon.thesquareplanet.com/papers/osdi18-noria.pdf

[2]: https://dl.acm.org/doi/10.1145/2517349.2522738

[3]: https://github.com/TimelyDataflow/timely-dataflow

And systems like timely dataflow, https://github.com/TimelyDataflow/timely-dataflow

Project looks cool! Glad you open sourced it. It could use some comments in the code base to help contributors ;). I also like the datafusion usage, that is awesome. BTW I work on github.com/bytewax/bytewax, which is based on https://github.com/TimelyDataflow/timely-dataflow another Rust dataflow computation engine.

Just found this https://github.com/TimelyDataflow/timely-dataflow and my heart skipped a beat.

Bytewax is a Python native binding to the Timely Dataflow library (written in Rust) for building highly scalable streaming (and batch) processing pipelines.

I am working on Bytewax, which is a Python stream processing framework built on Timely Dataflow. It is not exactly a Kafka integration framework because it is a more of a general stream processing framework, but might be interesting for you. We are focused on enabling people to more easily debug, containerize, parallelize and customize and less on enabling a declarative integration framework. It is still early days for us! And we are looking for feedback and ideas from the community.

We used to use a library called Pond.js, https://github.com/esnet/pond, but the reliance on Immutable.JS caused some performance pitfalls, so we wrote a system from scratch that deals with data in a batched streaming fashion. A lot of the concepts were borrowed from a Rust library called timely-dataflow, https://github.com/TimelyDataflow/timely-dataflow.

Though the name "Dataflow" might be an unfortunate name conflict with another Rust project: https://github.com/TimelyDataflow/timely-dataflow

> In the simplest case, I'm talking about regular SQL non-materialized views which are essentially inlined.

I see that now -- makes sense!

> Wish we had some better database primitives to assemble rather than building everything on Postgres - its not ideal for a lot of things.

I'm curious to hear more about this! We agree that better primitives are required and that's why Materialize is written in Rust using using TimelyDataflow[1] and DifferentialDataflow[2] (both developed by Materialize co-founder Frank McSherry). The only relationship between Materialize and Postgres is that we are wire-compatible with Postgres and we don't share any code with Postgres nor do we have a dependence on it.

[1] https://github.com/TimelyDataflow/timely-dataflow

Under the hood, Materialize uses Timely Dataflow (TDF) as the stream-processing engine. This allows Materialize to take advantage of the distributed data-parallel compute engine. The great thing about using TDF is that it has been in open source development since 2014 and has since been battle-tested in production at large Fortune 1000-scale companies.

We use a macaddr8 that embeds a wall-clock timestamp (so they're ascending order, achieving data locality) with some additional randomness. It's worked really well for us:

https://github.com/estuary/flow/blob/master/supabase/migrati...

we use macaddr8 instead of bigint, because it has a postgres serialization / JSON encoding which lossless-ly round-trips with browsers and it works well with PostgREST. The same CANNOT be said for bigint, which is a huge footgun.

I can't claim to know much about CloudQuery, but we are an open-source platform with CDC connectors from PostgreSQL and materializations to BQ and elsewhere. We also have fully-managed connectors if you don't want to deal with hosting.

This is essentially how we model things in Flow (disclosure: I work there). We call them Derivations, which are data products that are built (derived) from other data products. Each data product (we call them Collections) is backed by a set of append-only logs, so they can be read by many different consumers at different times. IDK if our product can work for you since we don't (yet) support stuff like MQTT, but there's a pretty generous free tier if you'd be able to push the data over HTTP. Either way, I just think it's cool that others have independently arrived at similar ideas about how to model streaming tasks!

We Estuary Git Docs have an open-source connector for Kafka -> Snowflake that could perform the tasks of a) flattening the data and b) removing duplicates via exactly once end to end delivery

Estuary Technology | Backend Engineer | Developer Evangelist | Rust, Go | REMOTE OR HYBRID | UTC-7 to UTC+2

Regional offices in NYC & Columbus, OH

Estuary (https://www.estuary.dev/) is the first real-time Data Operations platform for future-proof pipelines, including both historical and real-time data set up in minutes.

Our team is rapidly growing, VC funded and led by two successful, repeat founders.

We primarily develop in Rust and Go and are heavily built on top of gazette which is an internally developed streaming engine.

Flow: https://github.com/estuary/flow

Gazette: https://gazette.readthedocs.io/en/latest/

Backend Engineer: https://www.estuary.dev/about/#backend

Developer Evangelist: https://www.estuary.dev/about/#developerevangelist

^This is an exciting opportunity to make direct impact and shape user perception of a new product that brings a fresh experience to working with real-time data.

As this is a unique role, we are open to a variety of personas (data engineers, backend developers, Solutions Engineers and of course DevRel professionals).

Estuary offers full health benefits, competitive salary, unlimited PTO, 401K, equity, and a culture that values trust, transparency, and a flexible work environment to optimize your work/life balance.

To apply, send your resume and any questions to [email protected]

Flow Gazette We are looking for a backend engineer who is early in their career (around 1-3 years of industry experience) to join our team.

Estuary Technology | Junior Backend Engineer | Rust, Go | REMOTE OR HYBRID | Regional offices in NYC & Columbus, OH

Estuary (https://www.estuary.dev/) is the first real-time Data Operations platform for future-poof pipelines, including both historical and real-time data set up in minutes.

Our team is rapidly growing, VC funded and led by two successful, repeat founders.

We primarily develop in rust and go and are heavily built on top of gazette which is an internally developed streaming engine.

Flow: https://github.com/estuary/flow

Gazette: https://gazette.readthedocs.io/en/latest/

We are looking for a junior backend engineer with 2-3 years of industry experience.

For engineers who have an unquenched curiosity and drive to solve complex distributed systems problems, this is an opportunity to advance your career alongside a team of subject matter experts.

We are focused on expanding our catalog of open-source data connectors and building out our managed service platform.

ESTIMATED COMPENSATION: $110,000 - $150,000.

Estuary offers full health benefits, competitive salary, unlimited PTO, 401K, equity, and a culture that values trust, transparency, and a flexible work environment to optimize your work/life balance.

Email your resume to [email protected] to apply!

It does seem like a weird bug that this would cause errors https://github.com/estuary/flow/runs/5642694619?check_suite_... seems like it should be some kind of warning instead of an error?

where you'd subscribe for live updates.

[1]: https://github.com/estuary/flow