Slack Access and Integration Logs connector
This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.
The managed Slack Access and Integration Logs connector in Lakeflow Connect allows you to ingest workspace access logs and app integration change logs from Slack into Databricks.
Feature availability
Feature | Availability |
|---|---|
UI-based pipeline authoring |
|
API-based pipeline authoring |
|
Declarative Automation Bundles |
|
Incremental ingestion |
|
Unity Catalog governance |
|
Orchestration using Databricks Workflows |
|
SCD type 2 |
Slack access and integration logs are append-only. |
Automated schema evolution: New and deleted columns |
|
Automated schema evolution: Data type changes |
|
Automated schema evolution: Column renames |
Requires a full refresh. |
Authentication methods
Authentication method | Availability |
|---|---|
OAuth U2M |
|
OAuth M2M |
|
Basic authentication (username/password) |
|
What to know before you start
Topic | Why it matters |
|---|---|
The workflow depends on your Databricks user persona:
| |
The steps to create a pipeline depend on the interface. | |
The pipeline schedule depends on your latency and cost requirements. | |
Depending on your ingestion needs, the pipeline might use configurations like history tracking, column selection, and row filtering. Supported configurations vary by connector. See Feature availability. |
Start ingesting from Slack Access and Integration Logs
The following table provides an overview of the end-to-end Slack Access and Integration Logs ingestion flow, based on user type:
User | Steps |
|---|---|
Admin |
|
Non-admin | Use any supported interface to create a pipeline from an existing connection. See Ingest data from Slack Access and Integration Logs. |