notebooks/spark_integration_example.ipynb

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    "# Spark Integration Example\n",
    "\n",
    "This notebook demonstrates how to connect to Spark and interact with Iceberg tables using Spark Connect.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "**⚠️ This notebook requires the integration test infrastructure to be running.**\n",
    "\n",
    "To start the infrastructure, use one of these commands:\n",
    "- `make test-integration-setup` - Start just the infrastructure\n",
    "- `make notebook-infra` - Start infrastructure and launch JupyterLab\n",
    "\n",
    "The infrastructure includes:\n",
    "- Spark Connect server (port 15002)\n",
    "- Iceberg REST catalog\n",
    "- S3-compatible storage (MinIO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6cc20c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1a3fad",
   "metadata": {},
   "source": [
    "## Connecting to Spark\n",
    "\n",
    "Connect to the Spark server using Spark Connect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28bf42fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create SparkSession against the remote Spark Connect server\n",
    "spark = SparkSession.builder.remote(\"sc://localhost:15002\").getOrCreate()\n",
    "spark.sql(\"SHOW CATALOGS\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "550f2a5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show available namespaces/databases\n",
    "spark.sql(\"SHOW NAMESPACES\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9971fd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show tables in the default namespace\n",
    "spark.sql(\"SHOW TABLES FROM default\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8d3463",
   "metadata": {},
   "source": [
    "## Exploring Iceberg Tables\n",
    "\n",
    "Use Spark SQL commands to explore Iceberg table structure and metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b9d7dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Describe a table\n",
    "spark.sql(\"DESCRIBE TABLE default.test_all_types\").show()"
   ]
  }
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