From 191de419686364fd1764678b31e2501ab722a8fe Mon Sep 17 00:00:00 2001
From: Anders Westrheim <anders.westrheim@dnv.com>
Date: Fri, 11 Apr 2025 10:22:34 +0200
Subject: [PATCH 1/3] rename secret scope

---
 src/dataworkbench/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/dataworkbench/utils.py b/src/dataworkbench/utils.py
index b4975ab..5670a7a 100644
--- a/src/dataworkbench/utils.py
+++ b/src/dataworkbench/utils.py
@@ -23,7 +23,7 @@ def is_databricks():
     return os.getenv("DATABRICKS_RUNTIME_VERSION") is not None
 
 
-def get_secret(key: str, scope: str = "secrets") -> str:
+def get_secret(key: str, scope: str = "dwsecrets") -> str:
     """
     Retrieve a secret from dbutils if running on Databricks, otherwise fallback to env variables.
     """

From ba57f0538241546e8e90d55f6c790e552d2f3aef Mon Sep 17 00:00:00 2001
From: Anders Westrheim <anders.westrheim@dnv.com>
Date: Fri, 11 Apr 2025 10:28:16 +0200
Subject: [PATCH 2/3] updated readme with schema example

---
 README.md | 31 +++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index dc3cae4..7b5e9aa 100644
--- a/README.md
+++ b/README.md
@@ -51,28 +51,39 @@ import dataworkbench
 
 To use it on your local machine, it requires you to set a set of variables to connect to the Veracity Dataworkbench API.
 
-### Basic Example
 
+
+## Examples
+
+### Saving a Spark DataFrame to the Data Catalogue
 ```python
 from dataworkbench import DataCatalogue
 
 df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["letter", "number"])
 
-datacatalogue = DataCatalogue()  # Naming subject to change
-datacatalogue.save(df, "Dataset Name", "Description", tags={"environment": ["test"]})
+datacatalogue = DataCatalogue()
+datacatalogue.save(
+    df,
+    "Dataset Name",
+    "Description",
+    tags={"environment": ["test"]}
+)  # schema_id is optional - if not provided, schema will be inferred from the dataframe
 ```
-
-## Examples
-
-### Saving a Spark DataFrame to the Data Catalogue
-
+#### Using an existing schema
+When you have an existing schema that you want to reuse:
 ```python
 from dataworkbench import DataCatalogue
 
 df = spark.createDataFrame([("a", 1), ("b", 2), ("c", 3)], ["letter", "number"])
 
-datacatalogue = DataCatalogue()  # Naming subject to change
-datacatalogue.save(df, "Dataset Name", "Description", tags={"environment": ["test"]})
+datacatalogue = DataCatalogue()
+datacatalogue.save(
+    df,
+    "Dataset Name",
+    "Description",
+    tags={"environment": ["test"]},
+    schema_id="abada0f7-acb4-43cf-8f54-b51abd7ba8b1"  # Using an existing schema ID
+)
 ```
 
 ## API Reference

From 2a0e2685314b4c7f2b17b0d0cc721319ae7dde06 Mon Sep 17 00:00:00 2001
From: Anders Westrheim <anders.westrheim@dnv.com>
Date: Fri, 11 Apr 2025 10:34:08 +0200
Subject: [PATCH 3/3] fixed readme missing schema_id

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7b5e9aa..3269962 100644
--- a/README.md
+++ b/README.md
@@ -90,7 +90,7 @@ datacatalogue.save(
 
 ### DataCatalogue
 
-- `save(df, name, description=None, tags=None)`: Save a Spark DataFrame to the Data Workbench Data Catalogue
+- `save(df, name, description, schema_id=None, tags=None)`: Save a Spark DataFrame to the Data Workbench Data Catalogue
 
 
 ## License