Extract Snowflake data based on environment

[ndw]

Hi all,

In the development workspace, I need to extract data from a table/view in Snowflake development environment. Example table is called as VD_DWH.SALES.SALES_DETAIL

When we deploy the code into production, it needs to extract data from a table/view in Snowflake production environment. Example table is called as VP_DWH.SALES.SALES_DETAIL

On brief, for dev database name is VD_DWH

for production database name is VP_DWH

How to implement this in Databricks?

[nayan_wylde]

@ndw Can you please share more details on the request. Are you trying to extract data from snowflake in databricks?

[ndw]

There is a migration from the legacy data warehouse Snowflake to the lakehouse in the Databricks. Until the migration has completed, I need to transfer Snowflake data into Databricks. but dev and prod snowflake databases have different names.

In the development environment, data migration source is VD_DWH.SALES.SALES_DETAIL; in the production environment the source is VP_DWH.SALES.SALES_DETAIL

According to the environment I need to replace source table in the select statement 

[nayan_wylde]

Option 1 — Notebook widgets (simple & reliable)

Use a Databricks widget to pass the environment (e.g., via Jobs/Workflows parameter), then build the table name dynamically.

# Databricks notebook
dbutils.widgets.dropdown("env", "dev", ["dev", "prod"], "Environment")
env = dbutils.widgets.get("env")

# Map env → Snowflake database
db_map = {"dev": "VD_DWH", "prod": "VP_DWH"}
source_db = db_map.get(env, "VD_DWH")  # default to dev

source_table_fqn = f"{source_db}.SALES.SALES_DETAIL"

# Snowflake connection options (store secrets in Databricks Secrets)
sfOptions = {
    "sfUrl": dbutils.secrets.get("scope", "sfUrl"),
    "sfUser": dbutils.secrets.get("scope", "sfUser"),
    "sfPassword": dbutils.secrets.get("scope", "sfPassword"),
    "sfWarehouse": dbutils.secrets.get("scope", "sfWarehouse"),
    "sfRole": dbutils.secrets.get("scope", "sfRole"),
}

# Read from Snowflake table into Spark DataFrame
df = (spark.read
      .format("snowflake")
      .options(**sfOptions)
      .option("dbtable", source_table_fqn)
      .load())

# Do your transforms, then write to Delta/UC
df.write.mode("overwrite").saveAsTable("lakehouse.sales_detail")
``

Option 2 — Jobs/Workflows parameters (no widgets in code)

If you’re scheduling via Databricks Jobs (or your orchestrator like ADF/Airflow), pass env as a parameter and read it with dbutils.widgets.get. You can also pass the database name directly as a parameter to avoid mapping.

Option 3 — Configuration-driven (robust for many tables)

Centralize environment settings (e.g., in a UC Volume/Delta table or notebook-scoped JSON) and look them up

import json

config_json = """
{
  "dev": {"source_db": "VD_DWH"},
  "prod": {"source_db": "VP_DWH"}
}
"""
cfg = json.loads(config_json)
env = dbutils.widgets.get("env")  # or default 'dev'
source_db = cfg[env]["source_db"]

source_table_fqn = f"{source_db}.SALES.SALES_DETAIL"
# ... proceed to read from Snowflake

[ndw]

Thank you

I will schedule via Databricks Job. I need to pass env as dev in the development and prod in the production. How can I pass different values with the same job in different environments? Should I use DABs for passing env?