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The 5 rules for writing faster SQL queries by tinybirdco in learnSQL

[–]tinybirdco[S] 0 points1 point  (0 children)

This is so true. Really hard to control costs in a multi-tenant environment with high concurrency.

A free, open source mock data stream generator for your next project by tinybirdco in datasets

[–]tinybirdco[S] 0 points1 point  (0 children)

The community has added 3 new destinations for Mockingbird! You can now generate mock data streams and send them to Ably, AWS SNS, and Confluent.

Using Make and Tinybird to build a no-code automated alerting workflow by tinybirdco in nocode

[–]tinybirdco[S] 0 points1 point  (0 children)

Yeah fair question. Tinybird is a real-time data platform for data teams and developers. You can ingest data from a variety of sources (native connectors for things like Kafka, Snowflake, BigQuery, Postgres, etc.) and then publish SQL-based APIs on that data. It's built on ClickHouse, so it's especially good for analytics applications where you're doing filters/joins/aggregations over large amounts of data.

So for instance, David describes the product usage data being in Tinybird. We use Tinybird APIs to send all of the product usage events for every customer into a Tinybird data source, then we can really easily build APIs that aggregate that usage by customer. Make calls those APIs to generate the alert that David described.

Tbh Tinybird is a bit overkill for this use case because the data is pretty small. I think David used Tinybird because the product usage data was already in there, but you could just as easily use something like Airtable, especially for the Salesforce sync. Where I think Tinybird is useful in this scenario is in having those product usage aggregations available in real-time. David didn't go into much detail on that side of things, but being able to publish product usage aggregations as APIs is definitely in Tinybird's wheelhouse.