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!
Ha Yes! A pipeline assumes a "batch" of data, which is backed by an ephemeral duckdb in memory table. The goal is to provide SQL table semantics and implement pipelines in a way where the batch size can be toggled without a change to the pipeline logic.
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).
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.
Oh yes!! I've seen this a couple times. I am far from an expert in tributary so please take with a grain of salt.
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 :)
Stream to stream joins are NOT currently supported. This is a regularly requested feature, and I'll look at prioritizing it.
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!
Great Article! This is also timely for me, I spent all last week deep in the postgres documentation learning about replication (wish I had this article).
I'm building kafka connect (i.e. Debezium) compatible replication:
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!
How are you defining “B2C businesses on Main Street.”
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.
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...