Update product names: Workflows→Jobs, Delta Live Tables→Spark Declarative Pipelines

acceptance/bundle/help/bundle-generate-pipeline/output.txt

@@ -1,13 +1,13 @@
 
 >>> [CLI] bundle generate pipeline --help
-Generate bundle configuration for an existing Delta Live Tables pipeline.
+Generate bundle configuration for an existing pipeline.
 
-This command downloads an existing Lakeflow Spark Declarative Pipeline's configuration and any associated
+This command downloads an existing pipeline's configuration and any associated
 notebooks, creating bundle files that you can use to deploy the pipeline to other
 environments or manage it as code.
 
 Examples:
-  # Import a production Lakeflow Spark Declarative Pipeline
+  # Import a production pipeline
   databricks bundle generate pipeline --existing-pipeline-id abc123 --key etl_pipeline
 
   # Organize files in custom directories

acceptance/bundle/help/bundle-open/output.txt

@@ -4,7 +4,7 @@ Open a deployed bundle resource in the Databricks workspace.
 
 Examples:
   databricks bundle open                    # Prompts to select a resource to open
-  databricks bundle open my_job             # Open specific job in Workflows UI
+  databricks bundle open my_job             # Open specific job in Jobs UI
   databricks bundle open my_dashboard       # Open dashboard in browser
 
 Use after deployment to quickly navigate to your resources in the workspace.

acceptance/bundle/paths/invalid_pipeline_globs/databricks.yml

@@ -9,5 +9,5 @@ resources:
 
 variables:
   notebook_dir:
-    description: Directory with DLT notebooks
+    description: Directory with SDP notebooks
     default: non-existent

acceptance/bundle/paths/pipeline_expected_file_got_notebook/databricks.yml

@@ -6,5 +6,5 @@ include:
 
 variables:
   notebook_dir:
-    description: Directory with DLT notebooks
+    description: Directory with SDP notebooks
     default: notebooks

acceptance/bundle/paths/pipeline_globs/root/databricks.yml

@@ -6,8 +6,8 @@ include:
 
 variables:
   notebook_dir:
-    description: Directory with DLT notebooks
+    description: Directory with SDP notebooks
     default: notebooks
   file_dir:
-    description: Directory with DLT files
+    description: Directory with SDP files
     default: files

acceptance/bundle/run_as/pipelines_legacy/output.txt

@@ -1,6 +1,6 @@
 
 >>> [CLI] bundle validate -o json
-Warning: You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the DLT pipelines in your DAB as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.
+Warning: You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the pipelines in your DABs project as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.
   at experimental.use_legacy_run_as
   in databricks.yml:8:22
 

acceptance/bundle/telemetry/deploy-experimental/output.txt

@@ -1,6 +1,6 @@
 
 >>> [CLI] bundle deploy
-Warning: You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the DLT pipelines in your DAB as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.
+Warning: You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the pipelines in your DABs project as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.
   at experimental.use_legacy_run_as
   in databricks.yml:5:22
 

acceptance/bundle/templates/dbt-sql/output/my_dbt_sql/README.md

@@ -102,7 +102,7 @@ on CI/CD setup.
 ## Manually deploying to Databricks with Declarative Automation Bundles
 
 Declarative Automation Bundles can be used to deploy to Databricks and to execute
-dbt commands as a job using Databricks Workflows. See
+dbt commands as a job using Databricks Jobs. See
 https://docs.databricks.com/dev-tools/bundles/index.html to learn more.
 
 Use the Databricks CLI to deploy a development copy of this project to a workspace:
@@ -117,7 +117,7 @@ is optional here.)
 This deploys everything that's defined for this project.
 For example, the default template would deploy a job called
 `[dev yourname] my_dbt_sql_job` to your workspace.
-You can find that job by opening your workpace and clicking on **Workflows**.
+You can find that job by opening your workpace and clicking on **Jobs & Pipelines**.
 
 You can also deploy to your production target directly from the command-line.
 The warehouse, catalog, and schema for that target are configured in `dbt_profiles/profiles.yml`.

acceptance/bundle/templates/default-scala/output/my_default_scala/README.md

@@ -21,7 +21,7 @@ The 'my_default_scala' project was generated by using the default-scala template
     This deploys everything that's defined for this project.
     For example, the default template would deploy a job called
     `[dev yourname] my_default_scala_job` to your workspace.
-    You can find that job by opening your workspace and clicking on **Workflows**.
+    You can find that job by opening your workspace and clicking on **Jobs & Pipelines**.
 
 4. Similarly, to deploy a production copy, type:
    ```

acceptance/bundle/templates/default-sql/output/my_default_sql/README.md

@@ -21,7 +21,7 @@ The 'my_default_sql' project was generated by using the default-sql template.
     This deploys everything that's defined for this project.
     For example, the default template would deploy a job called
     `[dev yourname] my_default_sql_job` to your workspace.
-    You can find that job by opening your workpace and clicking on **Workflows**.
+    You can find that job by opening your workpace and clicking on **Jobs & Pipelines**.
 
 4. Similarly, to deploy a production copy, type:
    ```

acceptance/bundle/templates/default-sql/output/my_default_sql/src/orders_daily.sql

@@ -1,4 +1,4 @@
--- This query is executed using Databricks Workflows (see resources/my_default_sql_sql.job.yml)
+-- This query is executed using Databricks Jobs (see resources/my_default_sql_sql.job.yml)
 
 USE CATALOG {{catalog}};
 USE IDENTIFIER({{schema}});

acceptance/bundle/templates/default-sql/output/my_default_sql/src/orders_raw.sql

@@ -1,4 +1,4 @@
--- This query is executed using Databricks Workflows (see resources/my_default_sql_sql.job.yml)
+-- This query is executed using Databricks Jobs (see resources/my_default_sql_sql.job.yml)
 --
 -- The streaming table below ingests all JSON files in /databricks-datasets/retail-org/sales_orders/
 -- See also https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-streaming-table.html

bundle/config/mutator/resourcemutator/capture_uc_dependencies.go

@@ -12,7 +12,7 @@ import (
 
 type captureUCDependencies struct{}
 
-// If a user defines a UC schema in the bundle, they can refer to it in DLT pipelines,
+// If a user defines a UC schema in the bundle, they can refer to it in SDP pipelines,
 // UC Volumes, Registered Models, Quality Monitors, or Model Serving Endpoints using the
 // `${resources.schemas.<schema_key>.name}` syntax. Using this syntax allows TF to capture
 // the deploy time dependency this resource has on the schema and deploy changes to the
@@ -110,7 +110,7 @@ func (m *captureUCDependencies) Apply(ctx context.Context, b *bundle.Bundle) dia
 		if p == nil {
 			continue
 		}
-		// "schema" and "target" have the same semantics in the DLT API but are mutually
+		// "schema" and "target" have the same semantics in the SDP API but are mutually
 		// exclusive i.e. only one can be set at a time.
 		p.Schema = resolveSchema(b, p.Catalog, p.Schema)
 		p.Target = resolveSchema(b, p.Catalog, p.Target)

bundle/config/mutator/resourcemutator/run_as.go

@@ -183,7 +183,7 @@ func setRunAsForAlerts(b *bundle.Bundle) {
 	}
 }
 
-// Legacy behavior of run_as for DLT pipelines. Available under the experimental.use_run_as_legacy flag.
+// Legacy behavior of run_as for SDP pipelines. Available under the experimental.use_run_as_legacy flag.
 // Only available to unblock customers stuck due to breaking changes in https://github.com/databricks/cli/pull/1233
 func setPipelineOwnersToRunAsIdentity(b *bundle.Bundle) {
 	runAs := b.Config.RunAs
@@ -233,7 +233,7 @@ func (m *setRunAs) Apply(_ context.Context, b *bundle.Bundle) diag.Diagnostics {
 		return diag.Diagnostics{
 			{
 				Severity:  diag.Warning,
-				Summary:   "You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the DLT pipelines in your DAB as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.",
+				Summary:   "You are using the legacy mode of run_as. The support for this mode is experimental and might be removed in a future release of the CLI. In order to run the pipelines in your DABs project as the run_as user this mode changes the owners of the pipelines to the run_as identity, which requires the user deploying the bundle to be a workspace admin, and also a Metastore admin if the pipeline target is in UC.",
 				Paths:     []dyn.Path{dyn.MustPathFromString("experimental.use_legacy_run_as")},
 				Locations: b.Config.GetLocations("experimental.use_legacy_run_as"),
 			},