Merge pull request #14 from stewartbryson/stewart

Notebook.

README.md

@@ -19,6 +19,9 @@ Secondly, I just couldn't stand for having the `DimTrade` table be "both a dimen
 This decision was made during an era when storage and compute were constrained, so in my design, I created both `DIM_TRADE` and `FACT_TRADE` tables.
 Finally, I used a Medallion Lakehouse Architecture with Bronze, Silver, and Gold zones, with the logical model above materialized in the Gold zone.
 
+# Demo Notebook
+I've included a [demo Jupyter notebook](demo.ipynb) that walks through a subset of the instructions below. This notebook pairs with a presentation I give that focuses more on how Snowpark Python was used in the CLI, and also the motivation for using Dynamic Tables with dbt. But it may still be helpful.
+
 # Using DIGen.jar to Generate Source Files
 The Java program to generate the source files is downloaded by [filling out a form on the TPC-DI website](https://www.tpc.org/TPC_Documents_Current_Versions/download_programs/tools-download-request5.asp?bm_type=TPC-DI&bm_vers=1.1.0&mode=CURRENT-ONLY) and clicking a link in an email. Once unzipped, we have to make one slight change for running on macOS:
 

demo.ipynb

@@ -154,6 +154,7 @@
     "import json\n",
     "from snowflake.snowpark.types import *\n",
     "from snowflake.snowpark.functions import *\n",
+    "from snowflake.snowpark import Session\n",
     "\n",
     "# Read the credentials.json file\n",
     "with open(\"credentials.json\") as jsonfile:\n",
@@ -165,7 +166,10 @@
     "    .builder\n",
     "    .configs(credentials_dict)\n",
     "    .create()\n",
-    ")"
+    ")\n",
+    "\n",
+    "# Constants\n",
+    "source_path = '/Users/stewartbryson/dev/tpcdi-output/Batch1'"
    ]
   },
   {
@@ -183,8 +187,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# File paths\n",
-    "source_path = '/Users/stewartbryson/dev/tpcdi-output/Batch1'\n",
+    "# Constants\n",
     "stage_path = \"@tpcdi/Batch1\"\n",
     "\n",
     "# Put the file\n",