Add unity catalog section and add get catalogs

Author: Robert-Ziegltrum

streamlit/view_groups.py

@@ -104,6 +104,17 @@
                 "icon": ":material/lan:",
             },
         ],
+    },
+        {
+        "title": "Unity Catalog",
+        "views": [
+            {
+                "label": "Get Catalogs",
+                "help": "Get meta data.",
+                "page": "views/unity_catalog_get.py",
+                "icon": ":material/lan:",
+            },
+        ],
     },
     {
         "title": "Authentication",

streamlit/views/unity_catalog_get.py

@@ -0,0 +1,137 @@
+import pandas as pd
+import streamlit as st
+import datetime
+import matplotlib.pyplot as plt
+from databricks.sdk import WorkspaceClient
+
+
+workspace = WorkspaceClient()
+
+def get_catalogs():
+    catalogs = workspace.catalogs.list()
+    # Parse metadata into a list of dictionaries
+    catalogs_data = []
+    for catalog in catalogs:
+        catalogs_data.append({
+            "Catalog Name": catalog.name,
+            "Owner": catalog.owner,
+            "Comment": catalog.comment,
+            "Created At": datetime.datetime.fromtimestamp(catalog.created_at/1000),
+            "Updated At": datetime.datetime.fromtimestamp(catalog.updated_at/1000),
+        })    
+    return pd.DataFrame(catalogs_data)
+
+def get_schemas():
+    schema_data = []
+    for catalog in workspace.catalogs.list():
+        schemas = workspace.schemas.list(catalog_name=catalog.name)
+        for schema in schemas:
+            print(schema.catalog_name)
+            print(schema)
+            schema_data.append({
+                "Catalog Name": schema.catalog_name,
+                "Catalog Type": schema.catalog_type,
+                "Schema Name": schema.full_name,
+                "Owner": schema.owner,
+                "Comment": schema.comment,
+                "Created At": datetime.datetime.fromtimestamp(schema.created_at/1000),
+                "Updated At": datetime.datetime.fromtimestamp(schema.updated_at/1000),
+                "Effective Predictive Optimization": schema.effective_predictive_optimization_flag,
+                "Properites": schema.properties
+
+            })
+    return pd.DataFrame(schema_data)
+
+
+
+st.header(body="Unity Catalog", divider=True)
+st.subheader("Get catalog and schema information")
+st.write(
+    "This receipt gets the meta data for the catalogs and the schemas."
+)
+
+tab_a, tab_b = st.tabs(["**Try it**", "**Code snippets**"])
+
+with tab_a:
+    if st.button("Try It"):
+        st.write('### Databricks Catalogs')
+        st.dataframe(get_catalogs())
+
+        st.write('### Databricks Schema')
+        
+        schemas = get_schemas()
+        st.dataframe(schemas)
+
+
+table = [
+    {
+        "type": "Get Catalog",
+        "param": "get_catalog",
+        "description": "Get the catalogs.",
+        "code": """
+        ```python
+        from databricks.sdk import WorkspaceClient
+
+
+        workspace = WorkspaceClient()
+
+        def get_catalogs():
+            catalogs = workspace.catalogs.list()
+            # Parse metadata into a list of dictionaries
+            catalogs_data = []
+            for catalog in catalogs:
+                catalogs_data.append({
+                    "Catalog Name": catalog.name,
+                    "Owner": catalog.owner,
+                    "Comment": catalog.comment,
+                    "Created At": datetime.datetime.fromtimestamp(catalog.created_at/1000),
+                    "Updated At": datetime.datetime.fromtimestamp(catalog.updated_at/1000),
+                })    
+            return pd.DataFrame(catalogs_data)
+        st.write('### Databricks Catalogs')
+        st.dataframe(get_catalogs())
+        ```
+        """,
+    },
+    {
+        "type": "Get Schemas",
+        "param": "get_schemas",
+        "description": "Get the schemas",
+        "code": """
+        ```python
+        from databricks.sdk import WorkspaceClient
+        
+        workspace = WorkspaceClient()
+                
+        def get_schemas():
+            schema_data = []
+            for catalog in workspace.catalogs.list():
+                schemas = workspace.schemas.list(catalog_name=catalog.name)
+                for schema in schemas:
+                    print(schema.catalog_name)
+                    print(schema)
+                    schema_data.append({
+                        "Catalog Name": schema.catalog_name,
+                        "Catalog Type": schema.catalog_type,
+                        "Schema Name": schema.full_name,
+                        "Owner": schema.owner,
+                        "Comment": schema.comment,
+                        "Created At": datetime.datetime.fromtimestamp(schema.created_at/1000),
+                        "Updated At": datetime.datetime.fromtimestamp(schema.updated_at/1000),
+                        "Effective Predictive Optimization": schema.effective_predictive_optimization_flag,
+                        "Properites": schema.properties
+
+                    })
+            return pd.DataFrame(schema_data)
+        schemas = get_schemas()
+        st.dataframe(schemas)
+        ```
+        """,
+    },
+]
+
+with tab_b:
+    for i, row in enumerate(table):
+        with st.expander(f"**{row['type']} ({row['param']})**", expanded=(i == 0)):
+            st.markdown(f"**Description**: {row['description']}")
+            st.markdown(row["code"])