diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 63b7205..fba5fa1 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,56 +1,56 @@
-# We want to run CI processes that can run independent of databricks as branch rules so that we dont # deploy at cost code that we already should know needs changing
-# such as linting, and unit test for python, maybe dab? verify
-# we run these on all pull request because if there is a hot fix it may not have passed through 
-# staging for example
-# qqqq check this is up to date
-name: CI - Pull Request Checks
-
-# Run CI on all pull requests
-on:
-  pull_request:
-    branches:
-      - '**'  # all branches
-
-jobs:
-  ci_checks:
-    name: "Linting, Unit Tests, DAB Verify"
-    runs-on: ubuntu-latest
-
-    steps:
-      # Checkout code
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      # Set up Python
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.x"
-
-      # Install dependencies used for linting and unit tests
-      - name: Install dependencies
-        run: pip install -r requirements-dev.txt
-
-      # Run python unit tests
-      - name: Run Unit Tests
-        run: pytest tests/unit
-
-      # Run python lint
-      # qqqq on example used flake8 instead
-      # pyproject.toml will need configuring
-      - name: Run Linting
-        run: pylint src
-        
-      # qqqq to do run commit lint step and put in commit lint config
-      # see TELBlazor
-      - name: Commit lint
-      run: |
-          echo "Commit lint not implemented"
-          exit 1
-        
-      # qqqq to do run version generation step and put in commit lint config
-      # see TELBlazor
-      - name: Version Generation Test Run
-      run: |
-          echo "Version test run not implemented"
-          exit 1
+# We want to run CI processes that can run independent of databricks as branch rules so that we dont # deploy at cost code that we already should know needs changing
+# such as linting, and unit test for python, maybe dab? verify
+# we run these on all pull request because if there is a hot fix it may not have passed through 
+# staging for example
+# qqqq check this is up to date
+name: CI - Pull Request Checks
+
+# Run CI on all pull requests
+on:
+  pull_request:
+    branches:
+      - '**'  # all branches
+
+jobs:
+  ci_checks:
+    name: "Linting, Unit Tests, DAB Verify"
+    runs-on: ubuntu-latest
+
+    steps:
+      # Checkout code
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      # Set up Python
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.x"
+
+      # Install dependencies used for linting and unit tests
+      - name: Install dependencies
+        run: pip install -r requirements-dev.txt
+
+      # Run python unit tests
+      - name: Run Unit Tests
+        run: pytest tests/unit
+
+      # Run python lint
+      # qqqq on example used flake8 instead
+      # pyproject.toml will need configuring
+      - name: Run Linting
+        run: pylint src
+        
+      # qqqq to do run commit lint step and put in commit lint config
+      # see TELBlazor
+      - name: Commit lint
+      run: |
+          echo "Commit lint not implemented"
+          exit 1
+        
+      # qqqq to do run version generation step and put in commit lint config
+      # see TELBlazor
+      - name: Version Generation Test Run
+      run: |
+          echo "Version test run not implemented"
+          exit 1
diff --git a/devops/README.md b/devops/README.md
new file mode 100644
index 0000000..b4ee10e
--- /dev/null
+++ b/devops/README.md
@@ -0,0 +1,5 @@
+# Development Deployment
+
+It would be nice without the terminal and without needing to push to github to trigger unit tests, bundle validation, and bundle deployment for the local development user areas.
+
+This doesnt seem do-able with a notebook, and enabling the terminal is an option, so using the databrick.yml ui deploy, and remembering to triggered any unit tests seems like it will be the process for now.
\ No newline at end of file
diff --git a/resources/pipeline/ods_ingestion.yml b/resources/pipeline/ods_ingestion.yml
index 2685775..7c91c29 100644
--- a/resources/pipeline/ods_ingestion.yml
+++ b/resources/pipeline/ods_ingestion.yml
@@ -1,86 +1,86 @@
-###############################
-## POC notes - DELETE LATER
-###############################
-## We should think about these resource files I think potentially a .yml per layer bronze.yml may make sense
-## We will not define schemas here
-## We use this file to expose from databricks.yml the variables we need to set up the pipeline
-## We will define too variables just for the set of pipelines here too if we start running layer based .ymls then we would have layer level variables here
-###############################
-## If we want specific pipeline resource file per .py file we should use this i think
-  #     libraries:
-  # - notebook:
-  #     path: ../../src/ingestion/ods_ingest.py
-## if we want per layer maybe
-      # libraries:
-      #   - glob:
-      #       # if doing a pipeline per layer would do something like
-      #       include: ../../src/ingestion/**.py
-## if we want per domain maybe
-      # libraries:
-      #   - glob:
-      #       # if doing a pipeline per layer would do something like
-      #       include: ../../src/ingestion/ods_*.py
-###############################
-
-# qqqq discus where want these things to live if it was using a wheel then the python file could be literally a table and a foreach
-#####
-# If we are running multlipe pipelines we may define all their vars at the top
-#####
-
-
-# qqqq
-## im thinking var for in script var <-- also no because i cand get bundle.xyz and no all vars seem accessible everywhere i get catalog from databricks.yml
-## bundle for vars originating from databricks.ymly
-### i get vars from databricks 
-## pipeline. from pipeline files
-## but files run, it shouldnt be bundle and pipeline it should represent the scope they come from
-
-## qqqq i like the top level config value to pass i do not like construction vars in a yml instead of python but 
-# Error: cannot create pipeline: The target schema field is required for UC pipelines. Reason: DLT requires specifying a target schema for UC pipelines. Please use the TEMPORARY keyword in the CREATE MATERIALIZED VIEW or CREATE STREAMING TABLE statement if you do not wish to publish your dataset..
-# Error: cannot update pipeline: Specified 'schema' field in the pipeline settings is illegal. Reason: Cannot unset 'schema' field once it's defined in the pipeline spec. Please create a new DLT pipeline. For more information about publishing modes, see https://docs.databricks.com/en/dlt/migrate-to-dpm.html.
-variables:
-  layer:
-    default: bronze
-    description: bronze, silver, transfrormations etc
-
-
-x-bronze-config: &bronze-config
-  bundle.env_name: ${var.env_name}
-  bundle.storage_account: ${var.storage_account} #storage is environment specific so defined in databricks.yml
-  pipeline.layer: ${var.layer} # if we are doing layer based resource files qqqq get from var
-  # f"{ADLS_PROTOCOL}{container}@{storage_account}{ADLS_SUFFIX}/        -> py adds {folder_name}/"
-  pipeline.storage_container_path: "abfss://${var.layer}@${var.storage_account}.dfs.core.windows.net/"
-
-resources:
-  pipelines:
-    pipeline_ods_ingestion:
-      name: ods_ingestion
-      libraries:
-        - glob:
-            # if doing a pipeline per layer would do something like
-            # include: ../../src/ingestion/ - might work
-            # include: ../../src/ingestion/*.py   - doesnt work 
-            include: ../../src/ingestion/ods_ingest.py 
-      photon: true
-      # qqqq good practice to specify its something to do with dlt having beta version?
-      channel: current
-      # By defining catalog here we set it for all jobs in the pipeline without needing to specify it witht he variable when defining a table
-      catalog: ${var.catalog}
-      target: ${var.schema_prefix}${var.layer}_ods ## AI said missing this qqqq i dont want this hard coded here
-      serverless: true
-      # qqqq dont think i need this here DELETE root_path: ../../src/ingestion
-      # qqqq config is only at pipeline level use yml anchor points if need to reuse
-      configuration:
-        ################ Map Databricks Bundle variables to Spark Config Properties ################
-        # Map the Bundle variables (from databricks.yml) to Spark config properties
-        # The key names here MUST match what you use in spark.conf.get() in Python!
-        # bundle.env_name: ${var.env_name}
-        # bundle.schema_prefix: ${var.schema_prefix} - qqqq setting schema now in the yml
-        # bundle.storage_account: ${var.storage_account}
-        ############### Resource yml files for set of pipelines #################
-        # If we do bronze, silver ... tranformation based layers with own yml files will define layer level vars here
-        # for example this would be
-        # bundle.layer_name: bronze -> #schema_layer = "bronze_" -> # schema_layer = park.conf.get("bundle.layer_name")
-      # configuration:
-        <<: *bronze-config #config anchor point for bronze layer so all pipelines in this file will have this set of configs
+###############################
+## POC notes - DELETE LATER
+###############################
+## We should think about these resource files I think potentially a .yml per layer bronze.yml may make sense
+## We will not define schemas here
+## We use this file to expose from databricks.yml the variables we need to set up the pipeline
+## We will define too variables just for the set of pipelines here too if we start running layer based .ymls then we would have layer level variables here
+###############################
+## If we want specific pipeline resource file per .py file we should use this i think
+  #     libraries:
+  # - notebook:
+  #     path: ../../src/ingestion/ods_ingest.py
+## if we want per layer maybe
+      # libraries:
+      #   - glob:
+      #       # if doing a pipeline per layer would do something like
+      #       include: ../../src/ingestion/**.py
+## if we want per domain maybe
+      # libraries:
+      #   - glob:
+      #       # if doing a pipeline per layer would do something like
+      #       include: ../../src/ingestion/ods_*.py
+###############################
+
+# qqqq discus where want these things to live if it was using a wheel then the python file could be literally a table and a foreach
+#####
+# If we are running multlipe pipelines we may define all their vars at the top
+#####
+
+
+# qqqq
+## im thinking var for in script var <-- also no because i cand get bundle.xyz and no all vars seem accessible everywhere i get catalog from databricks.yml
+## bundle for vars originating from databricks.ymly
+### i get vars from databricks 
+## pipeline. from pipeline files
+## but files run, it shouldnt be bundle and pipeline it should represent the scope they come from
+
+## qqqq i like the top level config value to pass i do not like construction vars in a yml instead of python but 
+# Error: cannot create pipeline: The target schema field is required for UC pipelines. Reason: DLT requires specifying a target schema for UC pipelines. Please use the TEMPORARY keyword in the CREATE MATERIALIZED VIEW or CREATE STREAMING TABLE statement if you do not wish to publish your dataset..
+# Error: cannot update pipeline: Specified 'schema' field in the pipeline settings is illegal. Reason: Cannot unset 'schema' field once it's defined in the pipeline spec. Please create a new DLT pipeline. For more information about publishing modes, see https://docs.databricks.com/en/dlt/migrate-to-dpm.html.
+variables:
+  layer:
+    default: bronze
+    description: bronze, silver, transfrormations etc
+
+
+x-bronze-config: &bronze-config
+  bundle.env_name: ${var.env_name}
+  bundle.storage_account: ${var.storage_account} #storage is environment specific so defined in databricks.yml
+  pipeline.layer: ${var.layer} # if we are doing layer based resource files qqqq get from var
+  # f"{ADLS_PROTOCOL}{container}@{storage_account}{ADLS_SUFFIX}/        -> py adds {folder_name}/"
+  pipeline.storage_container_path: "abfss://${var.layer}@${var.storage_account}.dfs.core.windows.net/"
+
+resources:
+  pipelines:
+    pipeline_ods_ingestion:
+      name: ods_ingestion
+      libraries:
+        - glob:
+            # if doing a pipeline per layer would do something like
+            # include: ../../src/ingestion/ - might work
+            # include: ../../src/ingestion/*.py   - doesnt work 
+            include: ../../src/ingestion/ods_ingest.py 
+      photon: true
+      # qqqq good practice to specify its something to do with dlt having beta version?
+      channel: current
+      # By defining catalog here we set it for all jobs in the pipeline without needing to specify it witht he variable when defining a table
+      catalog: ${var.catalog}
+      target: ${var.schema_prefix}${var.layer}_ods ## AI said missing this qqqq i dont want this hard coded here
+      serverless: true
+      # qqqq dont think i need this here DELETE root_path: ../../src/ingestion
+      # qqqq config is only at pipeline level use yml anchor points if need to reuse
+      configuration:
+        ################ Map Databricks Bundle variables to Spark Config Properties ################
+        # Map the Bundle variables (from databricks.yml) to Spark config properties
+        # The key names here MUST match what you use in spark.conf.get() in Python!
+        # bundle.env_name: ${var.env_name}
+        # bundle.schema_prefix: ${var.schema_prefix} - qqqq setting schema now in the yml
+        # bundle.storage_account: ${var.storage_account}
+        ############### Resource yml files for set of pipelines #################
+        # If we do bronze, silver ... tranformation based layers with own yml files will define layer level vars here
+        # for example this would be
+        # bundle.layer_name: bronze -> #schema_layer = "bronze_" -> # schema_layer = park.conf.get("bundle.layer_name")
+      # configuration:
+        <<: *bronze-config #config anchor point for bronze layer so all pipelines in this file will have this set of configs
         pipeline.domain: ods # if we then want to apply per pipeline variable here
\ No newline at end of file