databricks-solutions · pedrozanlorensi · Mar 11, 2026
diff --git a/data-warehousing/README.md b/data-warehousing/README.md
@@ -1 +1,15 @@
 # Data Warehousing
+
+## Projects
+
+### [Databricks Metric Views](./dbrx-metric-views/)
+
+A demo showcasing how to use Unity Catalog Metric Views in Databricks to define semantic models directly on the platform. Built on top of the [Retail Store Star Schema Dataset](https://www.kaggle.com/datasets/shrinivasv/retail-store-star-schema-dataset?select=fact_sales_denormalized.csv), it demonstrates how embedding your semantic layer in Databricks provides unified governance through Unity Catalog alongside optimal query performance — eliminating the need for external semantic modeling tools.
+
+### [Genie Space CI/CD](./genie-cicd/)
+
+An automated CI/CD pipeline for promoting Databricks AI/BI Genie spaces across environments. The project uses Databricks Asset Bundles (DABs) to export a Genie space configuration from a Dev workspace, version-control it in Git, and deploy it to a Prod workspace with automatic Unity Catalog catalog/schema reference replacement. It supports both creating new and updating existing Genie spaces, runs on serverless compute by default, and is ready to integrate with CI/CD platforms like GitHub Actions or Azure DevOps.
+
+### [Genie Room Creation](./genie-room-creation/)
+
+A Databricks notebook that enables programmatic creation of AI/BI Genie spaces using the Databricks Python SDK and interactive widgets. It provides a guided, widget-driven experience for configuring a new Genie space — including title, description, warehouse selection, table identifiers, and sample instructions — all without writing manual HTTP requests. The notebook also demonstrates advanced patterns such as listing existing spaces, customizing data sources with sample questions, and leveraging the SDK's built-in authentication and retry capabilities.
diff --git a/data-warehousing/dbrx-metric-views/0_IngestData.ipynb b/data-warehousing/dbrx-metric-views/0_IngestData.ipynb
@@ -0,0 +1,275 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "318cb8e6-9c5e-4fc3-bf7f-c3222c1c47b6",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install kagglehub\n",
+    "dbutils.library.restartPython()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "9918dd73-dce5-4f93-92d8-fefdcbcb26c8",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define parameters\n",
+    "dbutils.widgets.text(\"CATALOG_NAME\", \"pedroz_catalog\", \"Catalog Name\")\n",
+    "dbutils.widgets.text(\"SCHEMA_NAME\", \"metric_views_schema\"s, \"Schema Name\")\n",
+    "CATALOG_NAME = dbutils.widgets.get(\"CATALOG_NAME\")\n",
+    "SCHEMA_NAME = dbutils.widgets.get(\"SCHEMA_NAME\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "038ad156-d93b-4c82-81a2-415c23c1bb52",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "# You need to define this variable in order to download the Kaggle files to your Volume\n",
+    "os.environ[\"KAGGLEHUB_CACHE\"] = f\"/Volumes/pedroz_catalog/metric_views_schema/dimensional_model/kaggle_files\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "9277a6c9-63b8-4280-9995-f392d718d481",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import kagglehub\n",
+    "\n",
+    "# Download latest version\n",
+    "path = kagglehub.dataset_download(\"shrinivasv/retail-store-star-schema-dataset\")\n",
+    "\n",
+    "print(\"Path to dataset files:\", path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "80f1d37c-096a-4876-a7e4-1e782dc013f7",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "\n",
+    "csv_folder = path  # path variable from kagglehub.dataset_download\n",
+    "csv_files = glob.glob(f\"{csv_folder}/*.csv\")\n",
+    "\n",
+    "for csv_file in csv_files:\n",
+    "    table_name = os.path.splitext(os.path.basename(csv_file))[0]\n",
+    "    df = spark.read.csv(csv_file, header=True, inferSchema=True)\n",
+    "    # Clean column names: replace spaces with underscores\n",
+    "    for c in df.columns:\n",
+    "        df = df.withColumnRenamed(c, c.replace(\" \", \"_\"))\n",
+    "    full_table_name = f\"{CATALOG_NAME}.{SCHEMA_NAME}.{table_name}\"\n",
+    "    spark.sql(f\"DROP TABLE IF EXISTS {full_table_name}\")\n",
+    "    df.write.saveAsTable(full_table_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "e8dff81e-29b6-4bfc-988d-610d36b4012c",
+     "showTitle": false,
+     "tableResultSettingsMap": {
+      "0": {
+       "dataGridStateBlob": "{\"version\":1,\"tableState\":{\"columnPinning\":{\"left\":[\"#row_number#\"],\"right\":[]},\"columnSizing\":{\"database\":179},\"columnVisibility\":{}},\"settings\":{\"columns\":{}},\"syncTimestamp\":1766428355279}",
+       "filterBlob": null,
+       "queryPlanFiltersBlob": null,
+       "tableResultIndex": 0
+      }
+     },
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "display(spark.sql(f\"SHOW TABLES IN {CATALOG_NAME}.{SCHEMA_NAME}\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "application/vnd.databricks.v1+cell": {
+     "cellMetadata": {
+      "byteLimit": 2048000,
+      "implicitDf": true,
+      "rowLimit": 10000
+     },
+     "inputWidgets": {},
+     "nuid": "c677244c-bf81-418a-bcac-06053e52f650",
+     "showTitle": false,
+     "tableResultSettingsMap": {},
+     "title": ""
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%sql\n",
+    "SELECT *\n",
+    "FROM $CATALOG_NAME.$SCHEMA_NAME.dim_campaigns\n",
+    "LIMIT 10"
+   ]
+  }
+ ],
+ "metadata": {
+  "application/vnd.databricks.v1+notebook": {
+   "computePreferences": {
+    "hardware": {
+     "accelerator": null,
+     "gpuPoolId": null,
+     "memory": null
+    }
+   },
+   "dashboards": [],
+   "environmentMetadata": {
+    "base_environment": "",
+    "environment_version": "4"
+   },
+   "inputWidgetPreferences": null,
+   "language": "python",
+   "notebookMetadata": {
+    "mostRecentlyExecutedCommandWithImplicitDF": {
+     "commandId": 7680324554559970,
+     "dataframes": [
+      "_sqldf"
+     ]
+    },
+    "pythonIndentUnit": 2
+   },
+   "notebookName": "0_IngestData",
+   "widgets": {
+    "CATALOG_NAME": {
+     "currentValue": "pedroz_catalog",
+     "nuid": "0f8987ce-14af-4a61-aec6-8f2bdf7e0859",
+     "typedWidgetInfo": {
+      "autoCreated": false,
+      "defaultValue": "pedroz_catalog",
+      "label": "Catalog Name",
+      "name": "CATALOG_NAME",
+      "options": {
+       "widgetDisplayType": "Text",
+       "validationRegex": null
+      },
+      "parameterDataType": "String"
+     },
+     "widgetInfo": {
+      "widgetType": "text",
+      "defaultValue": "pedroz_catalog",
+      "label": "Catalog Name",
+      "name": "CATALOG_NAME",
+      "options": {
+       "widgetType": "text",
+       "autoCreated": null,
+       "validationRegex": null
+      }
+     }
+    },
+    "SCHEMA_NAME": {
+     "currentValue": "metric_views_schema",
+     "nuid": "4f108163-255b-4738-83a3-72a2e2d7dc19",
+     "typedWidgetInfo": {
+      "autoCreated": false,
+      "defaultValue": "metric_views_schema",
+      "label": "Schema Name",
+      "name": "SCHEMA_NAME",
+      "options": {
+       "widgetDisplayType": "Text",
+       "validationRegex": null
+      },
+      "parameterDataType": "String"
+     },
+     "widgetInfo": {
+      "widgetType": "text",
+      "defaultValue": "metric_views_schema",
+      "label": "Schema Name",
+      "name": "SCHEMA_NAME",
+      "options": {
+       "widgetType": "text",
+       "autoCreated": null,
+       "validationRegex": null
+      }
+     }
+    }
+   }
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}