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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating a Grafana Dashboard with grafwiz\n",
+    "\n",
+    "This tutorial demonstrates how to use [grafwiz](https://github.com/v3io/grafwiz), Iguazio's open-source Python library for generating a Grafana dashboard programmatically."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Setup](#grafwiz-setup)\n",
+    "- [Generating Data](#grafwiz-gen-data)\n",
+    "- [Creating a DataFrame with the Generated Data](#grafwiz-df-create)\n",
+    "- [Writing the Data to the Platform's Data Store](#grafwiz-write-to-data-store)\n",
+    "- [Adding a Platform Data Source to Grafana](#grafwiz-add-data-source)\n",
+    "- [Creating a Grafana Dashboard](#grafwiz-grafana-dashboard-create)\n",
+    "- [Adding Dashboard Visualization Elements](#grafwiz-add-dashboard-visualization-elements)\n",
+    "- [Deploying the Dashboard to Grafana](#grafwiz-grafana-dashboard-deploy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-setup\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Initialize and configure your environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-install\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installing grafwiz\n",
+    "\n",
+    "Run the following code to ensure that the `grafwiz` Python package is installed, and the restart the Jupyter kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install git+https://github.com/v3io/grafwiz --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-setup-grafana\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a Grafana Service\n",
+    "\n",
+    "1.  Ensure that you have a running platform Grafana service.\n",
+    "    You can create such a service from the platform dashboard's **Services** page.\n",
+    "2.  Copy the URL of your Grafana service from the service-name link on the **Services** dashboard page."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-define-variables\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining Variables\n",
+    "\n",
+    "Define variables for your environment.\n",
+    "\n",
+    "> **Note:** Replace the `<Grafana URL>` placeholder with the URL of your Grafana service, as copied in the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "grafana_url = '<Grafana URL>' # TODO: Replace <Grafana URL> with the API URL of your Grafana API service.\n",
+    "v3io_container = 'users'\n",
+    "stocks_kv_table = os.path.join(os.getenv(\"V3IO_USERNAME\"),'stocks_kv_table')\n",
+    "stocks_tsdb_table = os.path.join(os.getenv(\"V3IO_USERNAME\"),'stocks_tsdb_table')\n",
+    "sym = 'XYZ'\n",
+    "rows = 3450"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-import-libs\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing Libraries\n",
+    "\n",
+    "Import required libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from grafwiz import *\n",
+    "import v3io_frames as v3f\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-create-frames-client\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a V3IO Frames Client\n",
+    "\n",
+    "Create a V3IO Frames client object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = v3f.Client('framesd:8081',container=v3io_container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-gen-data\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating Data\n",
+    "\n",
+    "Generate random data to visualize on the Grafana dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "\n",
+    "def generate_date(rows):\n",
+    "    \n",
+    "    datetimes = [datetime.datetime.today() - (random.random() * datetime.timedelta(minutes=15)) for i in range(rows)]\n",
+    "    return datetimes\n",
+    "\n",
+    "time = sorted(generate_date(rows))\n",
+    "volume = np.random.randint(low=100, high=10000, size=rows)\n",
+    "price = np.cumsum([0.0001] * rows + np.random.random(rows))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-df-create\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a DataFrame with the Generated Data\n",
+    "\n",
+    "Store the generated data in a pandas DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stocks_df = pd.DataFrame(\n",
+    "    {'last_updated': time,\n",
+    "     'volume': volume,\n",
+    "     'price': price\n",
+    "    })\n",
+    "stocks_df['symbol'] = sym\n",
+    "stocks_df = stocks_df.sort_values('last_updated')\n",
+    "stocks_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the `last_updated` column (attribute) as a DataFrame index column, which will be used to identify the ingestion times of the TSDB metric samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stocks_df_tsdb = stocks_df\n",
+    "stocks_df_tsdb = stocks_df.reset_index()\n",
+    "stocks_df_tsdb = stocks_df.set_index(['last_updated'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-write-to-data-store\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing the Data to the Platform's Data Store\n",
+    "\n",
+    "Use the V3IO Frames API to write the data from the pandas DataFrame to TSDB and NoSQL tables in the platform's persistent data store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-tsdb-table-write\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Writing the Data to a TSDB Table\n",
+    "\n",
+    "Write the data from the DataFrame to a new platform TSDB table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client.create(backend='tsdb', table=stocks_tsdb_table, rate='1/m', if_exists=1)\n",
+    "client.write(backend='tsdb', table=stocks_tsdb_table, dfs=stocks_df_tsdb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-nosql-table-write\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Writing the Data to a NoSQL Table\n",
+    "\n",
+    "Write the data from the DataFrame to a new platform NoSQL table in order of rows arrival, to simulate real-time data consumption."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expr_template = \"symbol='{symbol}';price='{price}';volume='{volume}';last_updated='{last_updated}'\"\n",
+    "# Write the stock data to a NoSQL table\n",
+    "for idx, record in stocks_df.iterrows():\n",
+    "    stock = {'symbol': sym, 'price': record['price'], 'volume': record['volume'], 'last_updated': record['last_updated']}\n",
+    "    expr = expr_template.format(**stock)\n",
+    "    client.execute('kv', stocks_kv_table, 'update', args={'key': sym, 'expression': expr})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Infer the schema of the NoSQL table to verify that it can be accessed and displayed on the dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Infer the schema of the NoSQL table\n",
+    "client.execute(backend='kv', table=stocks_kv_table, command='infer')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-add-data-source\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a Platform Data Source to Grafana\n",
+    "\n",
+    "Add an \"Iguazio\" data source for the platform's custom `iguazio` Grafana data source to your Grafana service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a data source\n",
+    "DataSource(name='Iguazio').deploy(grafana_url, use_auth=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-grafana-dashboard-create\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a Grafana Dashboard\n",
+    "\n",
+    "Create a new Grafana dashboard that uses the platform's `iguazio` data source."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create grafana dashboard\n",
+    "dash = Dashboard(\"stocks\", start='now-15m', dataSource='Iguazio', end='now')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-add-dashboard-visualization-elements\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Dashboard Visualization Elements\n",
+    "\n",
+    "Create a table for the NoSQL table and graphs for each of the metrics in the TSDB table, to be used for visualizing the data on the Grafana dashboard.\n",
+    "\n",
+    "> **Note:** It might take a few minutes for the graphs to be updated with the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a table and log viewer for the NoSQL table in one row\n",
+    "tbl = Table('Current Stocks Value', span=12).source(table=stocks_kv_table,fields=['symbol','volume', 'price', 'last_updated'],container=v3io_container)\n",
+    "dash.row([tbl])\n",
+    "\n",
+    "# Create TSDB-metric graphs\n",
+    "metrics_row = [Graph(metric).series(table=stocks_tsdb_table, fields=[metric], container=v3io_container) for metric in ['price','volume']]\n",
+    "dash.row(metrics_row)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id=\"grafwiz-grafana-dashboard-deploy\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying the Dashboard to Grafana\n",
+    "\n",
+    "Deploy the new Grafana dashboard to your Grafana service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Deploy to Grafana\n",
+    "dash.deploy(grafana_url)"
+   ]
+  }
+ ],
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