🏎️ SQLMesh now supports @ClickHouse, bringing precise, error-free data transformation to one of the world’s fastest analytical databases. Build, test & deploy with zero disruption real-time analytics at unmatched scale. Read more about our integration: tobikodata.com/sqlmesh-click…

I'm with @ericriddoch and Ben Epstein live coding with SQLmesh. Come join us youtube.com/live/6Ivmza-3mVM…

This week’s episode of @drilltodetail features @Captaintobs from @TobikoData on @Airbnb, #DataOps and the engineering innovation behind @SQLMesh

After a positive experience moving most of our data models @atmdotcom to @SQLMesh I couldn't help but put this together. github.com/nicosuave/awesome…

dlt-SQLMesh generator: A case of metadata handover With this integration, incremental models can be automatically generated in SQLMesh based on dlt metadata dlthub.com/blog/sqlmesh-dlt-…

Tired of messy data pipelines? 🥹🫣 Check out the @SQLMesh @dltHub integration for seamless metadata handovers, faster scaffolding, and incremental processing. 💻 Simplify your data workflows! 🔗 tobikodata.com/integrated-da… #DataEngineering #DataPipelines

Featuring SQLMesh on this week's episode of Open-Source Spotlight, our series where we're discovering open-source tools. @TobikoData's Toby Mao, @Captaintobs, joined us in demonstrating how this tool can help data team workflows. Watch the demo here: youtu.be/ASiBidAFdwM

Join @Captaintobs and @Al_Grigor from @DataTalksClub on their Open-Source Spotlight series, where they talk through the benefits of @SQLMesh like: ♦ Column-level lineage ♦ Environment Management ♦ Instant Prod deployments and much more! youtu.be/ASiBidAFdwM

Want to learn about running your #dbt project with @SQLMesh? Check out our latest YouTube video to run through a workflow using the classic Jaffle Shop example project! youtube.com/watch?v=X9RA6aO-…

This rounds out our Quickstart guide! What would you like to learn about next?

The @SnowflakeDB #summit this year looks awesome! 😍❄️

Some exciting new features just dropped for @SQLMesh v0.100! Snowpark's DataFrame api is now available to use in Python models! Now you can leverage the power of Python and Snowpark to run advanced transformations that run directly on Snowflake! Python models are super easy to write and debug with SQLMesh because they run wherever you run SQLMesh. The driver code runs on your laptop while the distributed code (dataframe api) runs in the warehouse. It's the best of both worlds! Another really cool feature we've added is the new Incremental by Partition model kind / materialization. These are special models that will incrementally replace partitions while keeping existing partitions intact. For example, if you have a dataset that is partitioned by country, and every day, you want to only refresh a set of countries. sqlmesh.readthedocs.io/en/st… sqlmesh.readthedocs.io/en/st…

The start of a busy 3 weeks for the @TobikoData #team!

its a packed #conference season for the @TobikoData team 🥳 #conferencealerts

Get a sneak preview of the #CaseStudies from #real #opensource SQLMesh users!

#data #intelligence for all! 🤓📊

More to come! #opensource #community

Yeah! Gotta love that #data 😍

Come visit us at our #DataCloudDevDay #booth 🥳

I'm really glad to see unit testing is becoming normal in the analytics engineering workflow with the release of dbt's version of unit tests. It's something we at @TobikoData cared a lot about during our days at Netflix and Airbnb and we knew it had to be part of @SQLMesh on day 1. But we certainly understand that testing data is a relatively new concept and seen as a challenging chore. But hopefully with dbt's new addition, together we can make testing data as widespread as testing software. We've done a lot of thinking about how to make unit testing data easier and less painful. Here are some unique features that SQLMesh unit tests provides. 1. We can run your tests on DuckDB even if your project normally runs on a warehouse like Bigquery or Snowflake. This is made possible by SQLGlot. It's way faster, cheaper, and easier to setup then running tests against your actual warehouse. If your SQL cannot be automatically translated, you can of course run your tests on any warehouse you like sqlmesh.readthedocs.io/en/st… 2. No one likes to define the input data for tests, so we added a tool to create tests for you. Just write a query and the results will form your initial test which you can then tweak! sqlmesh.readthedocs.io/en/st…