Parameterization

To handle environment-specific values committed to git, use a parameter.yml file. This file supports programmatically changing values based on the environment field in the FabricWorkspace class. If the environment value is not found in the parameter.yml file, any dependent replacements will be skipped.

Raise a feature request for additional parameterization capabilities.

find_replace

For generic find-and-replace operations. This will replace every instance of a specified string in every file. Specify the find value as the key and the replace value for each environment. See the Example page for a complete yaml file and an example use case below.

Note: A common use case for this function is to replace connection strings. I.e. find and replace a connection guid referenced in data pipeline.

find_replace:
    <find-this-value>:
        <environment-1>: <replace-with-this-value>
        <environment-2>: <replace-with-this-value>

spark_pool

Environments attached to custom spark pools need to be parameterized because the instance-pool-id in the Sparkcompute.yml file isn't supported in the create/update environment APIs. Provide the instance-pool-id as the key, and the pool type and name as the values.

Environment parameterization(PPE/PROD) is not supported. If needed, raise a feature request.

spark_pool:
    <instance-pool-id>:
        type: <Capacity-or-Workspace>
        name: <pool-name>

Example Use Case

When deploying the Example notebook from a feature workspace to PPE or PROD environments, the attached Example_LH lakehouse id needs to be updated. This update ensures the notebook points to the correct lakehouse in the respective environments.

In the notebook-content.py file, the referenced lakehouse guid 123e4567-e89b-12d3-a456-426614174000 must be replaced with the corresponding guid for Example_LH lakehouse in the target environment. This replacement is managed by the library, which takes the find_replace input in Parameter.yml and finds every instance of the guid string within the repository files and replaces it with the guid string for the deployed environment.

Note: In this example, the lakehouse workspace id in the notebook file is also replaced using Parameter.yml.

Parameters.yml

find_replace:
    "123e4567-e89b-12d3-a456-426614174000": # lakehouse guid to be replaced
        PPE: "f47ac10b-58cc-4372-a567-0e02b2c3d479" # PPE lakehouse guid
        PROD: "9b2e5f4c-8d3a-4f1b-9c3e-2d5b6e4a7f8c" # PROD lakehouse guid
    "8f5c0cec-a8ea-48cd-9da4-871dc2642f4c": # workspace id to be replaced
        PPE: "d4e5f6a7-b8c9-4d1e-9f2a-3b4c5d6e7f8a" # PPE workspace id
        PROD: "a1b2c3d4-e5f6-7a8b-9c0d-1e2f3a4b5c6d" # PROD workspace id

notebook-content.py

# Fabric notebook source

# METADATA ********************

# META {
# META   "kernel_info": {
# META     "name": "synapse_pyspark"
# META   },
# META   "dependencies": {
# META     "lakehouse": {
# META       "default_lakehouse": "123e4567-e89b-12d3-a456-426614174000",
# META       "default_lakehouse_name": "Example_LH",
# META       "default_lakehouse_workspace_id": "8f5c0cec-a8ea-48cd-9da4-871dc2642f4c"
# META     },
# META     "environment": {
# META       "environmentId": "a277ea4a-e87f-8537-4ce0-39db11d4aade",
# META       "workspaceId": "00000000-0000-0000-0000-000000000000"
# META     }
# META   }
# META }

# CELL ********************

print("Example notebook")

# METADATA ********************

# META {
# META   "language": "python",
# META   "language_group": "synapse_pyspark"
# META }

# CELL ********************

df = spark.sql("SELECT * FROM Example_LH.Table1 LIMIT 1000")
display(df)

# METADATA ********************

# META {
# META   "language": "python",
# META   "language_group": "synapse_pyspark"
# META }