flow VS ploomber

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

- One-click sharing powered by Ploomber Cloud: https://ploomber.io

Documentation: https://jupysql.ploomber.io

Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).

We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!

For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel

And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber

Develop notebook-based pipelines

Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.

We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.

I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.

Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.

If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!

It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2

That's what we do for lineage with https://ploomber.io/