Update slack link (#767)

README.md

@@ -14,7 +14,7 @@ Follow the [tutorial](https://sqlmesh.readthedocs.io/en/stable/quick_start/) to
 ## Join our community
 We'd love to join you on your data journey. Connect with us in the following ways:
 
-* Join the [Tobiko Slack community](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg) to ask questions, or just to say hi!
+* Join the [Tobiko Slack community](https://tobikodata.com/slack) to ask questions, or just to say hi!
 * File an issue on our [GitHub](https://github.com/TobikoData/sqlmesh/issues/new).
 * Send us an email at [[email protected]]([email protected]) with your questions or feedback.
 

docs/index.md

@@ -72,4 +72,4 @@ SQLMesh was built on three core principles:
 ## Next steps
 * [Jump right in with the quickstart](quick_start.md)
 * [Learn more about SQLMesh concepts](concepts/overview.md)
-* [Join our Slack community](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg)
+* [Join our Slack community](https://tobikodata.com/slack)

docs/integrations/engines.md

@@ -49,7 +49,7 @@ sqlmesh_airflow = SQLMeshAirflow(
 ## Databricks - Local/Built-in Scheduler
 If your project contains Python models that use PySpark DataFrames AND you are using the built-in scheduler, then you must run plan/apply on a Databricks cluster.
 This can be done using the [Notebook magic](../reference/notebook.md) or by using the [CLI](../reference/cli.md).
-This is something we are looking into improving — please leave us feedback in [our Slack channel](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg) if this impacts you.
+This is something we are looking into improving — please leave us feedback in [our Slack channel](https://tobikodata.com/slack) if this impacts you.
 A potential workaround until this support is added is to use [Databricks Connect](https://docs.databricks.com/dev-tools/databricks-connect.html). This will make it look like you are running on a cluster, and should theoretically work.
 
 Databricks has a few options for connection types to choose from:

docs/integrations/github.md

@@ -6,4 +6,4 @@ This will be done without copying or rebuilding data using SQLMesh's [Virtual Da
 Once approved, the CI/CD bot will automatically run [plan/apply](../concepts/plans.md) to the production environment and merge the PR upon completion.
 This allows you to always have your main branch and prod environments in sync.
 
-We will be launching this CI/CD bot soon — in the meantime, please leave any feedback or questions in [our Slack channel](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg)!
+We will be launching this CI/CD bot soon — in the meantime, please leave any feedback or questions in [our Slack channel](https://tobikodata.com/slack)!

docs/quick_start.md

@@ -252,4 +252,4 @@ Double-check that the data did indeed update in prod by running `sqlmesh fetchdf
 Congratulations, you've now conquered the basics of using SQLMesh!
 
 * [Learn more about SQLMesh concepts](concepts/overview.md)
-* [Join our Slack community](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg)
+* [Join our Slack community](https://tobikodata.com/slack)

posts/virtual_data_environments.md

@@ -158,4 +158,4 @@ With **Virtual Data Environments**, SQLMesh is able to provide fully **isolated*
 
 To streamline deploying changes to production, our team is about to release the SQLMesh [CI/CD bot](https://github.com/TobikoData/sqlmesh/blob/main/docs/integrations/github.md), which will help automate this process.
 
-Don't miss out - join our [Slack channel](https://join.slack.com/t/tobiko-data/shared_invite/zt-1tofr385z-vi~hDISNABiYIgkfGM3Khg) and stay tuned!
+Don't miss out - join our [Slack channel](https://tobikodata.com/slack) and stay tuned!