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Dynamic YAML with Python computed properties for fusing API workflows and SQL

sequor.dev

11–12 of 12 posts

Re: Dynamic YAML with Python computed properties for fusing API workflows and SQL

#11

forgive me for asking a few daft questions but i want to know a few things - who is the target audience for this (programmers / sql admins / companies with these guys) - what are they gaining using this tool - who are some other providers that offer similar stuff - how is your offering different from theirs - is this a commercial product, do you have plans to commercialize it like turning it into a subscription based…

Great questions! Let me break this down:

Target audience:

1) Enterprise IT teams who already know SQL/YAML - they can build complex integrations after ~1 hour of training using our examples, no prior Python needed

2) Modern data teams using dbt - Sequor complements it perfectly for data ingestion and activation

What they gain:

Full flexibility with structure. Enterprise IT folks go from zero to building end-to-end solutions in an hour without needing developer support. Think "dbt but for API integrations."

Competitors & differentiation:

1) Zapier/n8n: GUI looks easy but gets complex fast, poor database integration, can't handle bulk data

2) Fivetran/Airbyte: Pre-built connectors only, zero customization, ingestion-only

3) Us: Only code-first solution using open tech stack (SQL+YAML+Python) - gives you flexibility with Fivetran reliability

Business model:

1) Core engine: Open source, free forever

2) Revenue: On-premise server with enterprise features (RBAC, observability and execution monitoring with notifications, audit logs) - flat fee per installation, no per-row costs like competitors

3) Services: Custom connector development and app-to-app integration flows (we love this work!)

4) Cloud version maybe later - everyone wants on-premise now

The key difference:

we're the only tool that's both easy to learn AND highly customizable for all major API integration patterns: data ingestion, reverse ETL, and multi-step iPaaS workflows - all in one platform.

Re: Dynamic YAML with Python computed properties for fusing API workflows and SQL

#12
post #5

Recalculating customer metrics like that in your main example seems like a massive waste of snowflake resources, no?

Good catch! Yes, recalculating metrics across all historical data every run would be expensive in Snowflake. I chose this example for simplicity to show how the three operations work together, but you're absolutely right about the inefficiency. The flow can easily be optimized for incremental processing - pull only recent orders and update metrics for just the affected customers: steps: # Step 1: Pull only NEW orders…

I appreciate the response and detail. The code in your response definitely piqued my interest in the product more than the initial demo code does, but I do understand why you’d want simplicity on your homepage.
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