diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 8e59a3f..13ee3e1 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.0
+current_version = 0.8.1
 tag = False
 
 [bumpversion:file:soam/__init__.py]
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2daa541..d3fb0f3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.8.1 - 2021-08-06]
+
+### Added
+- Notebook with use cases of the TimeSeriesExtractor, Slicer and Store classes.
+
 ## [0.8.0 - 2021-08-02]
 
 ### Added
diff --git a/notebook/examples/extractor_slicer_store_example.ipynb b/notebook/examples/extractor_slicer_store_example.ipynb
new file mode 100644
index 0000000..5891be3
--- /dev/null
+++ b/notebook/examples/extractor_slicer_store_example.ipynb
@@ -0,0 +1,1521 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Series Extractor, Slicer and Store Introduction\n",
+    "\n",
+    "This notebook intends to show you different queries that can be done with the `TimeSeriesExtractor` class that `soam` provides, such as:\n",
+    "- Simple extract * query.\n",
+    "- Categorical data filters or conditions.\n",
+    "- Temporal data filters or conditions.\n",
+    "- Aggregated fields.\n",
+    "\n",
+    "At the same time, we provide a brief introduction to the `Slicer` class that `soam` provides to generate slices of your `DataFrame`. Finally, we show how to persist the results by using the `Store` class from the `soam` workflow.\n",
+    "\n",
+    "## Database used\n",
+    "We will be using a database that stores information on Apple's and Tesla's stocks over a period of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "The setup consists of:\n",
+    "- Import the needed dependencies.\n",
+    "- Establish the connection with our database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from soam.workflow.time_series_extractor import TimeSeriesExtractor\n",
+    "from muttlib.dbconn import get_client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sqlite_cfg = {\n",
+    "    \"db_type\": \"sqlite\",\n",
+    "    \"database\": \"soam_quickstart.db\"\n",
+    "}\n",
+    "\n",
+    "sqlite_client = get_client(sqlite_cfg)[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Extractor\n",
+    "\n",
+    "- Instantiate our extractor object.\n",
+    "\n",
+    "> To build a query you should create a `dictionary` of `{str: obj}` that will be later used for the extraction by executing the `extractor.run` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extractor = TimeSeriesExtractor(db=sqlite_client, table_name='stock')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple extract * query\n",
+    "\n",
+    "Simple query, just retrieving all the data from the database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query generated:  \n",
+      "          \n",
+      "          SELECT *\n",
+      "          FROM stock\n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': '*'\n",
+    "}\n",
+    "\n",
+    "print('Query generated: ', extractor.build_query(columns= '*')[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_num_trades</th>\n",
+       "      <th>avg_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id        date symbol  avg_num_trades  avg_price\n",
+       "0   1  2021-03-01   AAPL         80000.0      125.0\n",
+       "1   2  2021-03-02   AAPL         70000.0      126.0\n",
+       "2   3  2021-03-03   AAPL         80000.0      123.0\n",
+       "3   4  2021-03-04   AAPL         70000.0      121.0\n",
+       "4   5  2021-03-05   AAPL         80000.0      119.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Categorical data filters or conditions\n",
+    "Adding some filters and conditionals:\n",
+    "- Querying only a subset of the columns.\n",
+    "- Renaming some columns with aliases.\n",
+    "- Filtering data by just retrieving Apple's stock valuations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query generated:  \n",
+      "          \n",
+      "          SELECT date, symbol, avg_price AS Valuation\n",
+      "          FROM stock\n",
+      "          \n",
+      "          \n",
+      "          WHERE symbol = 'AAPL'\n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': ['date', 'symbol', 'avg_price AS Valuation'],\n",
+    "    'extra_where_conditions': [\"symbol = 'AAPL'\"]\n",
+    "}\n",
+    "\n",
+    "print('Query generated: ', extractor.build_query(columns = ['date', 'symbol', 'avg_price AS Valuation'],\n",
+    "                                                 extra_where_conditions = [\"symbol = 'AAPL'\"])[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>Valuation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  Valuation\n",
+       "0  2021-03-01   AAPL      125.0\n",
+       "1  2021-03-02   AAPL      126.0\n",
+       "2  2021-03-03   AAPL      123.0\n",
+       "3  2021-03-04   AAPL      121.0\n",
+       "4  2021-03-05   AAPL      119.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Temporal data filters or conditions\n",
+    "Adding some extra filters and sorting results:\n",
+    "- Filtering data by certain dates.\n",
+    "- Sorting results based on their dates in ascending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query generated:  \n",
+      "          \n",
+      "          SELECT date, symbol, avg_price AS Valuation\n",
+      "          FROM stock\n",
+      "          \n",
+      "          \n",
+      "          WHERE date >= '2021-03-01' AND date <= '2021-03-20' AND symbol = 'AAPL'\n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          ORDER BY date ASC\n",
+      "          \n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': ['date', 'symbol', 'avg_price AS Valuation'],\n",
+    "    'timestamp_col': 'date',\n",
+    "    'start_date': \"2021-03-01\",\n",
+    "    'end_date': \"2021-03-20\",\n",
+    "    'extra_where_conditions': [\"symbol = 'AAPL'\"],\n",
+    "    'order_by': [\"date ASC\"]\n",
+    "}\n",
+    "\n",
+    "print('Query generated: ', extractor.build_query(columns = ['date', 'symbol', 'avg_price AS Valuation'],\n",
+    "                                                 timestamp_col = 'date',\n",
+    "                                                 start_date = \"2021-03-01\",\n",
+    "                                                 end_date = \"2021-03-20\",\n",
+    "                                                 extra_where_conditions = [\"symbol = 'AAPL'\"],\n",
+    "                                                 order_by = [\"date ASC\"])[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>Valuation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  Valuation\n",
+       "0  2021-03-01   AAPL      125.0\n",
+       "1  2021-03-02   AAPL      126.0\n",
+       "2  2021-03-03   AAPL      123.0\n",
+       "3  2021-03-04   AAPL      121.0\n",
+       "4  2021-03-05   AAPL      119.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregated Fields I\n",
+    "\n",
+    "Adding some aggregated data.\n",
+    "- Multiply the average valuation with the amount of trades to obtain the transactional volume per day.\n",
+    "- Group by date and symbol by using the dimensions method.\n",
+    "- Filter by a certain level of volume by using the having method.\n",
+    "- Sort results by volume in descending order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query generated:  \n",
+      "          \n",
+      "          SELECT date, symbol, avg_num_trades * avg_price AS Volume\n",
+      "          FROM stock\n",
+      "          \n",
+      "          \n",
+      "          WHERE date >= '2021-03-01' AND date <= '2021-03-20'\n",
+      "          \n",
+      "          \n",
+      "          GROUP BY date, symbol\n",
+      "          \n",
+      "          \n",
+      "          HAVING Volume > 1000000\n",
+      "          \n",
+      "          \n",
+      "          ORDER BY Volume DESC\n",
+      "          \n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': ['date', 'symbol', 'avg_num_trades * avg_price AS Volume'],\n",
+    "    'dimensions': ['date','symbol'],\n",
+    "    'timestamp_col': 'date',\n",
+    "    'start_date': \"2021-03-01\",\n",
+    "    'end_date': \"2021-03-20\",\n",
+    "    'order_by': [\"Volume DESC\"],\n",
+    "    'extra_having_conditions': ['Volume > 1000000']\n",
+    "}\n",
+    "\n",
+    "print('Query generated: ', extractor.build_query(columns = ['date', 'symbol', 'avg_num_trades * avg_price AS Volume'],\n",
+    "                                                 dimensions = ['date','symbol'],\n",
+    "                                                 timestamp_col = 'date',\n",
+    "                                                 start_date = \"2021-03-01\",\n",
+    "                                                 end_date = \"2021-03-20\",\n",
+    "                                                 order_by = [\"Volume DESC\"],\n",
+    "                                                 extra_having_conditions = ['Volume > 1000000'])[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-19</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>16320000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-20</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>16320000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-17</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>15180000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-16</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>14280000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-18</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>14190000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol      Volume\n",
+       "0  2021-03-19   AAPL  16320000.0\n",
+       "1  2021-03-20   AAPL  16320000.0\n",
+       "2  2021-03-17   AAPL  15180000.0\n",
+       "3  2021-03-16   AAPL  14280000.0\n",
+       "4  2021-03-18   AAPL  14190000.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregated Fields II\n",
+    "\n",
+    "Adding some aggregated data.\n",
+    "- Retrieve the day with the biggest transactional volume for each company."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query generated:  \n",
+      "          \n",
+      "          SELECT date, symbol, max(avg_num_trades * avg_price) AS Max_Volume\n",
+      "          FROM stock\n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          GROUP BY symbol\n",
+      "          \n",
+      "          \n",
+      "          \n",
+      "          ORDER BY Max_Volume DESC\n",
+      "          \n",
+      "        \n"
+     ]
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': ['date', 'symbol', 'max(avg_num_trades * avg_price) AS Max_Volume'],\n",
+    "    'dimensions': ['symbol'],\n",
+    "    'order_by': [\"Max_Volume DESC\"],\n",
+    "}\n",
+    "\n",
+    "print('Query generated: ', extractor.build_query(columns = ['date', 'symbol', 'max(avg_num_trades * avg_price) AS Max_Volume'],\n",
+    "                                                 dimensions = ['symbol'],\n",
+    "                                                 order_by = [\"Max_Volume DESC\"]\n",
+    "                                                )[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>Max_Volume</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-22</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>21300000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-08</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>10324000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  Max_Volume\n",
+       "0  2021-03-22   AAPL  21300000.0\n",
+       "1  2021-03-08   TSLA  10324000.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Slicer\n",
+    "\n",
+    "Slice a dataframe upon given dimensions.\n",
+    "\n",
+    "#### 1. Instantiate the class:\n",
+    "\n",
+    "Slice the incoming data upon the given dimensions\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        dimensions:\n",
+    "            str or list of str labels of categorical columns to slices\n",
+    "        metrics:\n",
+    "            str or list of str labels of metrics columns to slices\n",
+    "        ds_col:\n",
+    "            str of datetime column\n",
+    "        keeps:\n",
+    "            str or list of str labels of columns to keep.\n",
+    "            \n",
+    "            \n",
+    "            \n",
+    "#### 2. Execute the .run method:\n",
+    "\n",
+    "Slice the given dataframe with the dimensions setted.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        raw_df\n",
+    "            A pandas DataFrame containing the raw data to slice\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        list[pd.DataFrame]\n",
+    "            DataFrame containing the sliced dataframes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use case\n",
+    "Imagine we want to generate a unique `DataFrame` for each dimension value with each metric. \n",
+    "\n",
+    "In our case, this means 4 dataframes since we have `Apple` and `Tesla` from the `Symbol` dimension and two metrics: `avg_num_trades` and `avg_price`. This will result on the following combinations:\n",
+    "1. Apple's average amount of trades per day.\n",
+    "2. Apple's average price per day.\n",
+    "3. Tesla's average amount of trades per day.\n",
+    "4. Tesla's average price per day.\n",
+    "\n",
+    "*We will be using the same database as before but retrieving all the data from the table to generate our `DataFrame`.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from soam.workflow.slicer import Slicer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_num_trades</th>\n",
+       "      <th>avg_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id        date symbol  avg_num_trades  avg_price\n",
+       "0   1  2021-03-01   AAPL         80000.0      125.0\n",
+       "1   2  2021-03-02   AAPL         70000.0      126.0\n",
+       "2   3  2021-03-03   AAPL         80000.0      123.0\n",
+       "3   4  2021-03-04   AAPL         70000.0      121.0\n",
+       "4   5  2021-03-05   AAPL         80000.0      119.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query={\n",
+    "    'columns': '*'\n",
+    "}\n",
+    "\n",
+    "df = extractor.run(build_query_kwargs = query)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Instantiate the class:\n",
+    "\n",
+    "> - dimensions: `symbol` \n",
+    "> - metrics: `avg_num_trades` and `avg_price`\n",
+    "> - ds_col: `date`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slicer = Slicer(metrics=[\"avg_num_trades\", \"avg_price\"], ds_col=\"date\", dimensions=[\"symbol\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Execute the .run method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "apple_trades, apple_price, tesla_trades, tesla_price = slicer.run(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Apple's average amount of trades per day:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_num_trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>70000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>80000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  avg_num_trades\n",
+       "0  2021-03-01   AAPL         80000.0\n",
+       "1  2021-03-02   AAPL         70000.0\n",
+       "2  2021-03-03   AAPL         80000.0\n",
+       "3  2021-03-04   AAPL         70000.0\n",
+       "4  2021-03-05   AAPL         80000.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apple_trades.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Apple's average price per day:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  avg_price\n",
+       "0  2021-03-01   AAPL      125.0\n",
+       "1  2021-03-02   AAPL      126.0\n",
+       "2  2021-03-03   AAPL      123.0\n",
+       "3  2021-03-04   AAPL      121.0\n",
+       "4  2021-03-05   AAPL      119.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "apple_price.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tesla's average amount of trades per day:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_num_trades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>60000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>62000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>64000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>69000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>80000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date symbol  avg_num_trades\n",
+       "22  2021-03-01   TSLA         60000.0\n",
+       "23  2021-03-02   TSLA         62000.0\n",
+       "24  2021-03-03   TSLA         64000.0\n",
+       "25  2021-03-04   TSLA         69000.0\n",
+       "26  2021-03-05   TSLA         80000.0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tesla_trades.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tesla's average price per day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>101.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>108.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>TSLA</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          date symbol  avg_price\n",
+       "22  2021-03-01   TSLA      105.0\n",
+       "23  2021-03-02   TSLA      104.0\n",
+       "24  2021-03-03   TSLA      101.0\n",
+       "25  2021-03-04   TSLA      108.0\n",
+       "26  2021-03-05   TSLA      115.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tesla_price.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Store\n",
+    "A class to store results in a table.\n",
+    "\n",
+    "#### 1. Instantiate the class:\n",
+    "\n",
+    "Store given data into a DataBase\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        db_cli:\n",
+    "            BaseClient client.\n",
+    "        table:\n",
+    "            str of table to store in.\n",
+    "        extra_insert_args:\n",
+    "            dict extra arguments to insert data.\n",
+    "                   \n",
+    "            \n",
+    "#### 2. Execute the .run method:\n",
+    "\n",
+    "Store given DataFrame.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        df\n",
+    "            A pandas DataFrame to store.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use case\n",
+    "Imagine we want to persist the `DataFrame` created of Apple's average price per day on a specific table named `apple_stock_price` in the same `DataBase` we are working on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from soam.workflow.store import Store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Instantiate the class:\n",
+    "\n",
+    "> - db_cli: `sqlite_client` \n",
+    "> - table: `apple_stock_price`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saver = Store(db_cli=sqlite_client, table='apple_stock_price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Execute the .run method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saver.run(apple_price)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can extract it with the `TimeSeriesExtractor` object to verify it was persisted successfully."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>date</th>\n",
+       "      <th>symbol</th>\n",
+       "      <th>avg_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2021-03-01</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>125.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2021-03-02</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2021-03-03</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2021-03-04</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>121.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021-03-05</td>\n",
+       "      <td>AAPL</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date symbol  avg_price\n",
+       "0  2021-03-01   AAPL      125.0\n",
+       "1  2021-03-02   AAPL      126.0\n",
+       "2  2021-03-03   AAPL      123.0\n",
+       "3  2021-03-04   AAPL      121.0\n",
+       "4  2021-03-05   AAPL      119.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "extractor = TimeSeriesExtractor(db=sqlite_client, table_name='apple_stock_price')\n",
+    "query={\n",
+    "    'columns': '*'\n",
+    "}\n",
+    "\n",
+    "persisted_df = extractor.run(build_query_kwargs = query)\n",
+    "\n",
+    "persisted_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voila!"
+   ]
+  }
+ ],
+ "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/examples/soam_quickstart.db b/notebook/examples/soam_quickstart.db
index f65e4b0..a80567c 100644
Binary files a/notebook/examples/soam_quickstart.db and b/notebook/examples/soam_quickstart.db differ
diff --git a/soam/__init__.py b/soam/__init__.py
index b9f4c5a..3ef43f9 100644
--- a/soam/__init__.py
+++ b/soam/__init__.py
@@ -1,3 +1,3 @@
 """Version."""
 
-__version__ = '0.8.0'
+__version__ = '0.8.1'