saturncloud · GeoSegun · Jan 3, 2026 · Jan 4, 2026 · Jan 5, 2026 · Jan 6, 2026
diff --git a/examples/data-science-analystics/cpu-bokeh-geoviz/Icon_bokeh_geo.png b/examples/data-science-analystics/cpu-bokeh-geoviz/Icon_bokeh_geo.png
diff --git a/examples/data-science-analystics/cpu-bokeh-geoviz/bokeh_geo_visualization.ipynb b/examples/data-science-analystics/cpu-bokeh-geoviz/bokeh_geo_visualization.ipynb
diff --git a/examples/data-science-analystics/cpu-bokeh-geoviz/make_notebook.py b/examples/data-science-analystics/cpu-bokeh-geoviz/make_notebook.py
@@ -0,0 +1,173 @@
+import json
+
+# Define the notebook structure with your new standard format
+notebook_content = {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div align=\"left\">\n",
+    "  <img src=\"./bokeh-geo-icon.png\" width=\"80\">\n",
+    "</div>\n",
+    "\n",
+    "# 🌍 Bokeh Notebook — Geo Visualization\n",
+    "\n",
+    "### **Template Review**\n",
+    "This template provides a production-ready setup for geospatial data analysis and interactive mapping on **Saturn Cloud**. Optimized for **CPU resources**, it demonstrates how to process geographic coordinates and render high-performance interactive maps. The primary goal is to showcase \"Map Incidents\" with interactive hover filters, allowing users to inspect localized data points dynamically.\n",
+    "\n",
+    "### **Dataset Overview**\n",
+    "The template utilizes a **Geospatial Incident** toy dataset. This dataset contains simulated event coordinates (latitude and longitude), incident types, and severity levels. It serves as a benchmark for testing spatial joins, coordinate reference system (CRS) transformations, and interactive glyph rendering in a mapping environment.\n",
+    "\n",
+    "### **Tech Stack**\n",
+    "* **Python**: The core language for spatial logic and data processing.\n",
+    "* **GeoPandas**: Extends Pandas to allow spatial operations on geometric types, handling the transformation of raw coordinates into map-ready shapes.\n",
+    "* **Bokeh**: A powerful visualization library used here to create interactive, web-ready maps with custom hover tools and real-time filtering capabilities.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 🚀 Quick Start\n",
+    "The Saturn Cloud environment is pre-configured for Jupyter. Run the following cells to install the specialized geospatial libraries and launch the interactive map.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Step 1: Install Required Libraries**\n",
+    "In this step, we install the specific libraries needed for geospatial visualization. This includes **Bokeh** for the interactive mapping engine and **GeoPandas** for handling spatial data structures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": None,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install geospatial and interactive visualization libraries\n",
+    "!pip install bokeh geopandas shapely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Step 2: Load and Prepare Geospatial Data**\n",
+    "We initialize a GeoDataFrame containing incident coordinates. We ensure the data is projected into the Web Mercator format (EPSG:3857), which is the standard coordinate system used by Bokeh and most web-based map tiles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": None,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "from shapely.geometry import Point\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Create toy incident data\n",
+    "data = {\n",
+    "    'Incident_ID': [1, 2, 3, 4],\n",
+    "    'Type': ['Maintenance', 'Emergency', 'Inquiry', 'Maintenance'],\n",
+    "    'Severity': ['Low', 'High', 'Medium', 'Low'],\n",
+    "    'lat': [37.7749, 37.7849, 37.7649, 37.7549],\n",
+    "    'lon': [-122.4194, -122.4094, -122.4294, -122.4394]\n",
+    "}\n",
+    "\n",
+    "df = pd.DataFrame(data)\n",
+    "# Convert to GeoDataFrame\n",
+    "gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat), crs=\"EPSG:4326\")\n",
+    "\n",
+    "# Project to Web Mercator for Bokeh compatibility\n",
+    "gdf = gdf.to_crs(\"EPSG:3857\")\n",
+    "gdf['x'] = gdf.geometry.x\n",
+    "gdf['y'] = gdf.geometry.y\n",
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Step 3: Build the Interactive Map with Hover Filters**\n",
+    "Using Bokeh, we render a map background (tile provider) and overlay the incident points. We configure a **HoverTool** to display incident details when the user moves their cursor over a point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": None,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bokeh.plotting import figure, show\n",
+    "from bokeh.models import HoverTool\n",
+    "from bokeh.io import output_notebook\n",
+    "\n",
+    "output_notebook()\n",
+    "\n",
+    "# Initialize the map figure\n",
+    "p = figure(x_axis_type=\"mercator\", y_axis_type=\"mercator\", \n",
+    "           title=\"Incident Map: Localized Hover Filters\",\n",
+    "           active_scroll=\"wheel_zoom\")\n",
+    "\n",
+    "# Add OpenStreetMap background tiles\n",
+    "p.add_tile(\"OSM\")\n",
+    "\n",
+    "# Plot incidents as circles\n",
+    "p.circle(x='x', y='y', size=10, color=\"red\", alpha=0.7, source=gdf)\n",
+    "\n",
+    "# Add Hover Tool with filters for ID, Type, and Severity\n",
+    "hover = HoverTool()\n",
+    "hover.tooltips = [\n",
+    "    (\"ID\", \"@Incident_ID\"),\n",
+    "    (\"Type\", \"@Type\"),\n",
+    "    (\"Severity\", \"@Severity\")\n",
+    "]\n",
+    "p.add_tools(hover)\n",
+    "\n",
+    "show(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🔗 Resources and Support\n",
+    "For further information on the platform or the libraries used in this template, please refer to the following official links:\n",
+    "\n",
+    "* **Platform**: [Saturn Cloud Dashboard](https://saturncloud.io/)\n",
+    "* **Support**: [Saturn Cloud Documentation](https://saturncloud.io/docs/)\n",
+    "* **Library**: [Bokeh Documentation](https://docs.bokeh.org/)\n",
+    "* **Library**: [GeoPandas Documentation](https://geopandas.org/)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
+
+# Save as .ipynb
+with open("bokeh_geo_visualization.ipynb", "w", encoding="utf-8") as f:
+    json.dump(notebook_content, f, indent=1)
+
+print("SUCCESS: bokeh_geo_visualization.ipynb has been created!")
diff --git a/examples/data-science-analystics/cpu-data-versioning/asset/DAG-icon.png b/examples/data-science-analystics/cpu-data-versioning/asset/DAG-icon.png
diff --git a/examples/data-science-analystics/cpu-data-versioning/data_versioning_dvc.ipynb b/examples/data-science-analystics/cpu-data-versioning/data_versioning_dvc.ipynb
@@ -0,0 +1,176 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "id": "f7a343b1",
+            "metadata": {},
+            "source": [
+                "# 📂 Data Versioning with DVC & S3\n",
+                "\n",
+                "<div align=\"center\">\n",
+                "  <img src=\"./asset/DAG-icon.png\" width=\"200\">\n",
+                "</div>\n",
+                "\n",
+                "### **Template Review**\n",
+                "This template demonstrates a professional data management workflow using **DVC**. Optimized for **Saturn Cloud Jupyter Notebooks**, it allows you to track large datasets and maintain 100% reproducibility without bloat in your Git repository. \n",
+                "\n",
+                "**Core Workflow:** We will use a **Local Directory** to simulate an S3 bucket for free, while providing the boilerplate code required to switch to a **Live AWS S3** bucket.\n",
+                "\n",
+                "### **Tech Stack**\n",
+                "* **DVC**: The core data versioning engine.\n",
+                "* **S3 (Boto3)**: Backend support for cloud object storage.\n",
+                "* **Infrastructure**: [Saturn Cloud](https://saturncloud.io/) CPU Jupyter Instance."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "2a1efbac",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# Wrap package in quotes to prevent 'zsh: no matches found' errors\n",
+                "!pip install \"dvc[s3]\" boto3 pandas -q"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "c4bc7738",
+            "metadata": {},
+            "source": [
+                "### **Step 1: Initialize DVC**\n",
+                "Set up the local environment to track data metadata."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "c9992ae3",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!dvc init --no-scm -f\n",
+                "print(\"DVC initialized successfully.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "d81638aa",
+            "metadata": {},
+            "source": [
+                "### **Step 2: Configure Remotes (Local vs. Remote S3)**\n",
+                "We use a local folder to simulate S3 for testing, but provide the logic for a real S3 bucket."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "30f75985",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import os\n",
+                "\n",
+                "# --- OPTION A: LOCAL SIMULATION (Free) ---\n",
+                "local_storage = \"/tmp/dvc_simulated_s3\"\n",
+                "os.makedirs(local_storage, exist_ok=True)\n",
+                "!dvc remote add -d local_remote {local_storage} -f\n",
+                "\n",
+                "# --- OPTION B: REMOTE S3 BUCKET (Production Setup) ---\n",
+                "# To use a real bucket, uncomment the lines below and provide your URI\n",
+                "# S3_URI = \"s3://your-real-bucket-name/data-folder\"\n",
+                "# !dvc remote add -d s3_remote {S3_URI} -f\n",
+                "\n",
+                "print(f\"✅ Active Remote set to: {local_storage}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "8006eadb",
+            "metadata": {},
+            "source": [
+                "### **Step 3: Track, Version, and Push Data**\n",
+                "We create a dummy dataset and 'push' it to our remote simulation."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "21d00790",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import pandas as pd\n",
+                "\n",
+                "# 1. Create dataset\n",
+                "df = pd.DataFrame({'feature_x': [10, 20, 30], 'label': [1, 0, 1]})\n",
+                "df.to_csv('my_dataset.csv', index=False)\n",
+                "\n",
+                "# 2. Track with DVC\n",
+                "!dvc add my_dataset.csv\n",
+                "\n",
+                "# 3. Push to storage (Local Sim or S3)\n",
+                "!dvc push\n",
+                "\n",
+                "print(\"\\nData pushed to remote storage. Tracking file 'my_dataset.csv.dvc' created.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "59b5e4d7",
+            "metadata": {},
+            "source": [
+                "### **Step 4: The Recovery Test**\n",
+                "Prove reproducibility by deleting the local data and pulling it back from the remote."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "7fd78c57",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "os.remove('my_dataset.csv')\n",
+                "print(\"Local dataset deleted.\")\n",
+                "\n",
+                "!dvc pull\n",
+                "print(\"\\n✅ Data successfully recovered from remote storage!\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "---\n",
+                "## 🏁 Conclusion & Next Steps\n",
+                "You have successfully versioned a dataset using **DVC**. To transition this to a production environment, simply update your DVC remote to a real S3 URI and ensure your **AWS Access Keys** are set in your environment variables.\n",
+                "\n",
+                "### **Resources & Backlinks**\n",
+                "* **Cloud Infrastructure**: [Deploy on Saturn Cloud](https://saturncloud.io/)\n",
+                "* **DVC Guide**: [S3 Remote Configuration](https://dvc.org/doc/user-guide/data-management/remote-storage/amazon-s3)"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "cpu-plotly-env",
+            "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": "3.13.11"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 5
+}
diff --git a/examples/data-science-analystics/cpu-duckdb-sql/assets/barChart.png b/examples/data-science-analystics/cpu-duckdb-sql/assets/barChart.png
diff --git a/examples/data-science-analystics/cpu-duckdb-sql/assets/sql-1.png b/examples/data-science-analystics/cpu-duckdb-sql/assets/sql-1.png