I use it as a free web analytics, the UX is so bad and convoluted, it's like a bad joke.
770 karma · joined March 1, 2012
I've been working in data space for ~7 years now. I drank the ELT koolaid and then painful learned about its inefficiencies. I'm trying to build tools that take an SRE/DevOps approach to data infrastructure!
I'm building a pipeline tool SQLFlow that aims to enable DevOps for data:
- Configuration Driven - CI Test framework integrated - First class metrics and monitoring
https://github.com/turbolytics/sql-flow
I wrote about some of these while launching SQLFlow on the render platform.
I'm happy to answer any questions or just talk modern data!
Thank you!
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I'm trying to build a whole ecosystem. SQLFlow is the stateful stream processor.
dbhealth is a way to monitor postgres and table freshness and schema changes.
control is a way to monitor both of these :)
https://github.com/turbolytics/sql-flow/commit/e7cc28b7bdd2f...
docs are updated:
https://github.com/turbolytics/sql-flow
DuckDB has been a fantastic engine to build on (in python), and processes thousands of events per second, day in an day out, without issue
Our first offering is OrderProof which records Shopify transaction evidence and generates PDF to help merchants streamline chargeback disputes.
https://turboops.io/products/orderproof
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more about the problem here:
https://www.linkedin.com/pulse/you-losing-shopify-chargeback...
https://turboops.io/platform/public-tracker
Our first offering is a tracker for makers, small businesses and contractors to show job status. Create a real time status page for your products, build trust and reduce customer inquiries.
We’re working on email notification support right now and have evidence (tracking numbers, job pictures, contracts/documents, etc) support coming next week!
The stream is achieved by the continuous flow of data from Kafka.
SQLFlow exposes a variable for batch size. Setting the batch size to 1 will make it so SQLFlow reads a kafka message, applies the processor SQL logic and then ensures it successfully commits the SQL results to the sink, one after another.
SQLFlow provides at least once delivery guarantees. It will only commit the source message once it successfully writes to the pipeline output (sink).
https://sql-flow.com/docs/operations/handling-errors
The batch table is just a convention which allows for seamless batch size configuration. If your throughput is low, or if you require message by message processing, SQLFlow can be toggled to a batch of 1. If you need higher throughput and can tolerate the latency, then the batch can be toggled higher.
Based on the tributary documentation, I understand that tributary embeds kafka consumers into duckdb. This makes duckdb the main process that you run to perform consumption. I think that this makes creating stream processing POCs very accessible. It looks like it is quite easy to start streaming data into duckdb. What I don't see is a full story around Devops, operations, testing, configuration as code etc.
SQLFlow is a service that embeds DuckDB as the storage and processing brains. Because of this, we're able to offer metrics, testing utilities, pipelines as code, and all the other DevOps utilities that are necessary to run a huge number of streaming instances 24x7. SQLFlow was created as a tool that I wish I had to for simple stream processing in production in high availability contexts :)
SQLFlow uses duckdb internally for windowing and stream state storage :), and I'll look at extending it to support stream / stream joins.
Could you describe a bit more about your use case? I'd really appreciate it if you could create an issue in the repo describing your use case and desired functionality a bit!
https://github.com/turbolytics/sql-flow/issues
We were looking at solving some of the simplier use cases first before branching out into these more complicated ones :)
I'm building kafka connect (i.e. Debezium) compatible replication:
https://github.com/turbolytics/librarian/pull/41
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I really like the mongo change stream API. I think it elegantly hides a lot of the complexity of replication. As a side-effect of building postres -> kafka replication, I'm also trying to build a nicer replication primitive for postgres, that hides some of the underlying replication protocol complexity!
DuckDB for stream processing:
https://github.com/turbolytics/sql-flow
Lightweight kafka stream processing using DuckDB as the execution engine. 300MiB runtime can easily handle thousands of messages / second.
Working on a Kafka Connect alternative:
https://github.com/turbolytics/librarian
Right now mongo replication (through changestreams) is supported to kafka. Working on Postgres support right now.
The reason I’m wondering is because it’s striking how much more financially challenged my Main Street is compared to Vc texh.
I see so much opportunity for a small medium business consultation in the analytics and process space but these companies are like really strapped for money and largely set in their ways in my experience.
In my experience, people are open to solving their problems. It’s just the money is hard making it financially viable so it’s just the big money is just like an order of magnitude smaller.
Another thing I’ve noticed is that I think the general level of sort of like process thinking and data driven decision making in tech is at just like a higher baseline than on Main Street
A lot of my discussions are challenging how to sort of like present the problem in a way that somebody that doesn’t have decades of experience and operations understands.
Another challenge that I face regularly with Main Street companies is just people seem to be happy like they’re not trying to continuously optimize like I’m used to doing coming for a big tech. Even when it’s easy to present like positive ROI opportunities there’s just like a comfort with the way things are done and a lot of people seem just happy governed by their scaling factors in exchange for that that comfort.
TLDR; challenges are financial and mentality.
https://github.com/turbolytics/sql-flow
Building a company around a tool is hard. There's been some interest but streaming is kind of commoditized.
I'm taking everything I learned building it and working on a customer-facing security product, more to come on that :)
https://github.com/turbolytics/sql-flow
It has some interest, unfortunately building tools as a business strategy is rough.
Beginning to work on first actual product! More soon :)
For my uses it's great that it has both test suite mode and individual invocation mode. I use it to execute a test suite of HTTP requests against a service in CI.
I'm not a super big fan of the configuration language, the blocks are not intuitive and I found some lacking in the documentation assertions that are supported.
Overall the tool has been great, and has been extremely valuable.
I started using interface testing when working on POCs. I found this helps with LLM-assisted development. Tests are written to directly exercise the HTTP methods, it allows for fluidity and evolution of the implementations as the project is evolving.
I also found the separation of testing very helpful, and it further enforces the separation between interface and implementation. Before hurl, the tests I wrote would be written in the test framework of the language the service is written in. The hurl-based tests really help to enforce the "client" perspective. There is no backdoor data access or anything, just strict separation betwen interface, tests and implementation :)
I’d Spend all morning in the dumpster with some friends. Name brand clothes were good finds, also pretty much all the textbooks carried a trade in value. Lots of sealed food snacks as well.
I don’t know if the kids that threw them away were lazy or they just didn’t know about buy back, but the books easily brought me $100 for a couple hours of morning dumpster diving.
https://github.com/turbolytics/sql-flow
I think the industry lacks lightweight fully featured stream processing solutions. I think it’s either heavyweight jvm or bespoke custom solutions
Sqlflow aims to be a middle ground , performant, fully featured, observable and supports sql
The article talks about how it’s more of a younger generation phenomenon suggesting older generations still maintain their friendships
I’m grappling with this myself, it requires a lot of energy to form adult friendships. I keep seeing my neighbors out at the playground, I reach out and say hey and hi and ask them how they are doing but stop short of investing the time necessary to form real friendships with them and I know deep down that it’s perpetuating late stage individualism
- read from an input (source)
- perform some sort of processing
- write the data to some output (sink)
This may either be batch or continuous (stream). The inputs may change, the outputs may change.
I personally think that sql and duckdb are well positioned to do this. SQL is declarative, standardized and has decades worth of mature implementations.
The “source” can be modeled as a table.
The “sink” can also be modeled as a table.
What does a custom dsl provide over sql?
I have a side project called Sqlflow which is attempting to do something similar/
https://github.com/turbolytics/sql-flow
It’s not a DSL but the pipeline is standardized using the source, process, sink stages. Right now the process is pure sql but the source and sink are declarative. SQL has so much prior art, linters and a huge ecosystem with many practitioners.
Arroyo on paper looks amazing, high performance, advanced streaming primitives like windows and joins.
I think / hope the foundations provide a long term alternative to flink and spark streaming, the extremely heavy jvm-based incumbents.
I only have ~5 hours of experience with it so def an ultra beginner, but it was very UI configuration heavy when I tried it. API based config was mentioned but didn’t slot into the way $dayjob provisions so it was a very hard sell.
In response to this I created a lightweight high performance dev-centric streaming solution powered by duckdb, it’s getting a little traction
https://github.com/turbolytics/sql-flow
Iceberg has been one of the most popular feature so far:
I wonder the fitness levels of the people that experienced cardiac arrests. Like were these first time runners that signed up to run a marathon?
Can someone help explain this? I’m having a hard time wrapping my head around this. The tiny packet has this much surface area due to how much space exists within the balls???
I don’t really get this. Our communities have so much in common and so much overlap, we shop at the same stores, go to the same parks, get stuck in the same traffic, our kids are at the same schools,our neighbors care for us medically, teach our kids, maintain our dwellings, work on our cars, and contribute to our local municipalities through property tax. We vacation at the same places.
We have so much in common but we put our heads down and duck into our homes ignoring our neighbors. To be honest it makes me really sick to think about. Like the internet has allowed us to live these parallel lives, highly dependent on our neighbors but completely isolated from them. We smile and nod then go to the ballots and kick our spite up to the federal level (in the US).
To me, we have the majority of our lives in common.
Social media and the political engines preys on our differences making them the focus of our interactions ignoring the fact that 90% of our day-to-day lives are overlapping and our concerns are similar: health, wealth, prosperity, safety, education and recreation.
It’s not much, but as I get older I’m making a point to slow down and talk to my neighbors, have real conversations with them, many of them fly political flags that are contrary to my political beliefs but I find out we have so much In common because we have such similar day-to-day lives and experiences.