diff --git a/src/ca_biositing/datamodels/utils/database_joins.ipynb b/src/ca_biositing/datamodels/utils/database_joins.ipynb
new file mode 100644
index 00000000..dbb836f2
--- /dev/null
+++ b/src/ca_biositing/datamodels/utils/database_joins.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Database Joins and Denormalized Views\n",
+    "\n",
+    "This notebook demonstrates how to use SQLAlchemy to perform complex joins on the project's data models and create denormalized views suitable for API endpoints.\n",
+    "\n",
+    "It replaces the older `database_interaction.ipynb` and uses the new generated data models.\n",
+    "\n",
+    "**Note:** Ensure you are running this notebook in the project's Pixi environment where `ca-biositing-datamodels` is installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python Executable: /Users/pjsmitty301/ca-biositing/.pixi/envs/default/bin/python\n",
+      "✅ You are running in a Pixi environment.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify Kernel\n",
+    "import sys\n",
+    "print(f\"Python Executable: {sys.executable}\")\n",
+    "\n",
+    "# Check if we are in the pixi environment (path should contain .pixi)\n",
+    "if \".pixi\" in sys.executable:\n",
+    "    print(\"✅ You are running in a Pixi environment.\")\n",
+    "else:\n",
+    "    print(\"⚠️ You might be running in the wrong kernel. Please select the 'default' kernel created by Pixi.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries and models imported successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine, select\n",
+    "from sqlalchemy.orm import Session\n",
+    "\n",
+    "# Import models from ca_biositing.py as the source of truth\n",
+    "from ca_biositing.datamodels.schemas.generated.ca_biositing import *\n",
+    "\n",
+    "print(\"Libraries and models imported successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Connected to database.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Database Connection\n",
+    "DATABASE_URL = \"postgresql+psycopg2://biocirv_user:biocirv_dev_password@localhost:5432/biocirv_db\"\n",
+    "engine = create_engine(DATABASE_URL)\n",
+    "print(f\"Connected to database.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Join LandIQ Record and Observation\n",
+    "\n",
+    "We will join `LandiqRecord` and `Observation` tables on `observation.record_id = landiq_record.id`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "<class 'sqlalchemy.orm.decl_api.DeclarativeMeta'> is not a generic class",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[34]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m stmt2 = select(LandiqRecord, PrimaryCrop).join(\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m     \u001b[43mPrimaryCrop\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mname\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m, \n\u001b[32m      3\u001b[39m     LandiqRecord.main_crop == PrimaryCrop.id).limit(\u001b[32m10\u001b[39m)\n\u001b[32m      5\u001b[39m \u001b[38;5;28mprint\u001b[39m(stmt2)\n\u001b[32m      7\u001b[39m df2 = pd.read_sql(stmt2, engine)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/ca-biositing/.pixi/envs/default/lib/python3.12/typing.py:398\u001b[39m, in \u001b[36m_tp_cache.<locals>.decorator.<locals>.inner\u001b[39m\u001b[34m(*args, **kwds)\u001b[39m\n\u001b[32m    396\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[32m    397\u001b[39m     \u001b[38;5;28;01mpass\u001b[39;00m  \u001b[38;5;66;03m# All real errors (not unhashable args) are raised below.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m398\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/ca-biositing/.pixi/envs/default/lib/python3.12/typing.py:1110\u001b[39m, in \u001b[36m_generic_class_getitem\u001b[39m\u001b[34m(cls, params)\u001b[39m\n\u001b[32m   1108\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m prepare \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   1109\u001b[39m         params = prepare(\u001b[38;5;28mcls\u001b[39m, params)\n\u001b[32m-> \u001b[39m\u001b[32m1110\u001b[39m \u001b[43m_check_generic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__parameters__\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1112\u001b[39m new_args = []\n\u001b[32m   1113\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m param, new_arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mcls\u001b[39m.__parameters__, params):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/ca-biositing/.pixi/envs/default/lib/python3.12/site-packages/typing_extensions.py:3111\u001b[39m, in \u001b[36m_check_generic\u001b[39m\u001b[34m(cls, parameters, elen)\u001b[39m\n\u001b[32m   3106\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Check correct count for parameters of a generic cls (internal helper).\u001b[39;00m\n\u001b[32m   3107\u001b[39m \n\u001b[32m   3108\u001b[39m \u001b[33;03mThis gives a nice error message in case of count mismatch.\u001b[39;00m\n\u001b[32m   3109\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   3110\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m elen:\n\u001b[32m-> \u001b[39m\u001b[32m3111\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m is not a generic class\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   3112\u001b[39m alen = \u001b[38;5;28mlen\u001b[39m(parameters)\n\u001b[32m   3113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m alen != elen:\n",
+      "\u001b[31mTypeError\u001b[39m: <class 'sqlalchemy.orm.decl_api.DeclarativeMeta'> is not a generic class"
+     ]
+    }
+   ],
+   "source": [
+    "stmt2 = select(LandiqRecord, PrimaryCrop).join(\n",
+    "    PrimaryCrop, \n",
+    "    LandiqRecord.main_crop == PrimaryCrop.id).limit(10)\n",
+    "\n",
+    "print(stmt2)\n",
+    "\n",
+    "df2 = pd.read_sql(stmt2, engine)\n",
+    "\n",
+    "df2.columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SQL Query:\n",
+      "SELECT landiq_record.dataset_id, landiq_record.polygon_id, landiq_record.main_crop, landiq_record.secondary_crop, landiq_record.tertiary_crop, landiq_record.quaternary_crop, landiq_record.confidence, landiq_record.irrigated, landiq_record.acres, landiq_record.version, landiq_record.note, landiq_record.test, landiq_record.id, landiq_record.created_at, landiq_record.updated_at, landiq_record.etl_run_id, landiq_record.lineage_group_id, observation.dataset_id AS dataset_id_1, observation.record_type, observation.record_id, observation.parameter_id, observation.value, observation.unit_id, observation.dimension_type_id, observation.dimension_value, observation.dimension_unit_id, observation.note AS note_1, observation.id AS id_1, observation.created_at AS created_at_1, observation.updated_at AS updated_at_1, observation.etl_run_id AS etl_run_id_1, observation.lineage_group_id AS lineage_group_id_1 \n",
+      "FROM landiq_record JOIN observation ON observation.record_id = landiq_record.id\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the join query\n",
+    "stmt = select(LandiqRecord, Observation).join(\n",
+    "    Observation,\n",
+    "    Observation.record_id == LandiqRecord.id\n",
+    ")\n",
+    "\n",
+    "print(\"SQL Query:\")\n",
+    "print(stmt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 0 rows.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "dataset_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "polygon_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "main_crop",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "secondary_crop",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "tertiary_crop",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "quaternary_crop",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "confidence",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "irrigated",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "acres",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "version",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "note",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "test",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "created_at",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "updated_at",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "etl_run_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "lineage_group_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "dataset_id_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "record_type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "record_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "parameter_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "value",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "unit_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "dimension_type_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "dimension_value",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "dimension_unit_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "note_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "id_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "created_at_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "updated_at_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "etl_run_id_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "lineage_group_id_1",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "edc059e1-7169-4eb9-ad76-c55b46d93053",
+       "rows": [],
+       "shape": {
+        "columns": 32,
+        "rows": 0
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>polygon_id</th>\n",
+       "      <th>main_crop</th>\n",
+       "      <th>secondary_crop</th>\n",
+       "      <th>tertiary_crop</th>\n",
+       "      <th>quaternary_crop</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>irrigated</th>\n",
+       "      <th>acres</th>\n",
+       "      <th>version</th>\n",
+       "      <th>...</th>\n",
+       "      <th>unit_id</th>\n",
+       "      <th>dimension_type_id</th>\n",
+       "      <th>dimension_value</th>\n",
+       "      <th>dimension_unit_id</th>\n",
+       "      <th>note_1</th>\n",
+       "      <th>id_1</th>\n",
+       "      <th>created_at_1</th>\n",
+       "      <th>updated_at_1</th>\n",
+       "      <th>etl_run_id_1</th>\n",
+       "      <th>lineage_group_id_1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [dataset_id, polygon_id, main_crop, secondary_crop, tertiary_crop, quaternary_crop, confidence, irrigated, acres, version, note, test, id, created_at, updated_at, etl_run_id, lineage_group_id, dataset_id_1, record_type, record_id, parameter_id, value, unit_id, dimension_type_id, dimension_value, dimension_unit_id, note_1, id_1, created_at_1, updated_at_1, etl_run_id_1, lineage_group_id_1]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 32 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Execute and load into DataFrame\n",
+    "try:\n",
+    "    with Session(engine) as session:\n",
+    "        # Using pandas read_sql to execute the statement and fetch results\n",
+    "        # Note: Columns with same names (like 'id') might be duplicated or suffixed automatically by pandas.\n",
+    "        df = pd.read_sql(stmt, session.bind)\n",
+    "        \n",
+    "        print(f\"Loaded {len(df)} rows.\")\n",
+    "        display(df.head())\n",
+    "except Exception as e:\n",
+    "    print(f\"Error executing query: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['dataset_id', 'polygon_id', 'main_crop', 'secondary_crop',\n",
+       "       'tertiary_crop', 'quaternary_crop', 'confidence', 'irrigated', 'acres',\n",
+       "       'version', 'note', 'test', 'id', 'created_at', 'updated_at',\n",
+       "       'etl_run_id', 'lineage_group_id', 'dataset_id_1', 'record_type',\n",
+       "       'record_id', 'parameter_id', 'value', 'unit_id', 'dimension_type_id',\n",
+       "       'dimension_value', 'dimension_unit_id', 'note_1', 'id_1',\n",
+       "       'created_at_1', 'updated_at_1', 'etl_run_id_1', 'lineage_group_id_1'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "dataset_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "record_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "main_crop",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "parameter_id",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "value",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "unit_id",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "5c95c323-56ec-4546-ad20-84e7b50c7748",
+       "rows": [],
+       "shape": {
+        "columns": 7,
+        "rows": 0
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>record_id</th>\n",
+       "      <th>main_crop</th>\n",
+       "      <th>parameter_id</th>\n",
+       "      <th>value</th>\n",
+       "      <th>unit_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [id, dataset_id, record_id, main_crop, parameter_id, value, unit_id]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[[\"id\", \"dataset_id\", \"record_id\", \"main_crop\", \"parameter_id\", \"value\", \"unit_id\"]]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/ca_biositing/datamodels/utils/query_landiq_observation.py b/src/ca_biositing/datamodels/utils/query_landiq_observation.py
new file mode 100644
index 00000000..6a7a0ee4
--- /dev/null
+++ b/src/ca_biositing/datamodels/utils/query_landiq_observation.py
@@ -0,0 +1,22 @@
+from sqlalchemy import select
+from ca_biositing.datamodels.schemas.generated.ca_biositing import LandiqRecord, Observation
+
+def generate_query():
+    """
+    Generates a SQL query joining LandiqRecord and Observation tables.
+    """
+    # Create the select statement
+    # We select from LandiqRecord and join Observation
+    # The join condition is explicitly specified as requested:
+    # observation.record_id = landiq_record.id
+    stmt = select(LandiqRecord, Observation).join(
+        Observation,
+        Observation.record_id == LandiqRecord.id
+    )
+
+    return stmt
+
+if __name__ == "__main__":
+    query = generate_query()
+    print("Generated SQL Query:")
+    print(query)