diff --git a/README.md b/README.md
index 5a0318e04..d0601bb19 100644
--- a/README.md
+++ b/README.md
@@ -74,8 +74,10 @@ df = wr.athena.read_sql_query("SELECT * FROM my_table", database="my_db")
   - [06 - Amazon Athena](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/06%20-%20Amazon%20Athena.ipynb)
   - [07 - Databases (Redshift, MySQL and PostgreSQL)](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/07%20-%20Redshift%2C%20MySQL%2C%20PostgreSQL.ipynb)
   - [08 - Redshift - Copy & Unload.ipynb](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/08%20-%20Redshift%20-%20Copy%20%26%20Unload.ipynb)
-  - [09 - Redshift - Append, Overwrite and Upsert.ipynb](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/09%20-%20Redshift%20-%20Append%2C%20Overwrite%2C%20Upsert.ipynb)
-  - [10 - Parquet Crawler.ipynb](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/10%20-%20Parquet%20Crawler.ipynb)
+  - [09 - Redshift - Append, Overwrite and Upsert](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/09%20-%20Redshift%20-%20Append%2C%20Overwrite%2C%20Upsert.ipynb)
+  - [10 - Parquet Crawler](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/10%20-%20Parquet%20Crawler.ipynb)
+  - [11 - CSV Datasets](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/04%20-%20CSV%20Datasets.ipynb)
+  - [12 - CSV Crawler](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/10%20-%CSV%20Crawler.ipynb)
 - [**API Reference**](https://aws-data-wrangler.readthedocs.io/en/latest/api.html)
   - [Amazon S3](https://aws-data-wrangler.readthedocs.io/en/latest/api.html#amazon-s3)
   - [AWS Glue Catalog](https://aws-data-wrangler.readthedocs.io/en/latest/api.html#aws-glue-catalog)
diff --git a/tutorials/11 - CSV Datasets.ipynb b/tutorials/11 - CSV Datasets.ipynb
new file mode 100644
index 000000000..23fd68cf8
--- /dev/null
+++ b/tutorials/11 - CSV Datasets.ipynb	
@@ -0,0 +1,528 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![AWS Data Wrangler](_static/logo.png \"AWS Data Wrangler\")](https://github.com/awslabs/aws-data-wrangler)\n",
+    "\n",
+    "# 4 - CSV Datasets\n",
+    "\n",
+    "Wrangler has 3 different write modes to store CSV Datasets on Amazon S3.\n",
+    "\n",
+    "- **append** (Default)\n",
+    "\n",
+    "    Only adds new files without any delete.\n",
+    "    \n",
+    "- **overwrite**\n",
+    "\n",
+    "    Deletes everything in the target directory and then add new files.\n",
+    "    \n",
+    "- **overwrite_partitions** (Partition Upsert)\n",
+    "\n",
+    "    Only deletes the paths of partitions that should be updated and then writes the new partitions files. It's like a \"partition Upsert\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import date\n",
+    "import awswrangler as wr\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Enter your bucket name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ··········································\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "bucket = getpass.getpass()\n",
+    "path = f\"s3://{bucket}/dataset/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>value</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>boo</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id value        date\n",
+       "0   1   foo  2020-01-01\n",
+       "1   2   boo  2020-01-02"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    \"id\": [1, 2],\n",
+    "    \"value\": [\"foo\", \"boo\"],\n",
+    "    \"date\": [date(2020, 1, 1), date(2020, 1, 2)]\n",
+    "})\n",
+    "\n",
+    "wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    mode=\"overwrite\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_dataset\"\n",
+    ")\n",
+    "\n",
+    "wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appending"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>value</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>boo</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id value        date\n",
+       "0   1   foo  2020-01-01\n",
+       "1   2   boo  2020-01-02\n",
+       "2   3   bar  2020-01-03"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    \"id\": [3],\n",
+    "    \"value\": [\"bar\"],\n",
+    "    \"date\": [date(2020, 1, 3)]\n",
+    "})\n",
+    "\n",
+    "wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    mode=\"append\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_dataset\"\n",
+    ")\n",
+    "\n",
+    "wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overwriting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>value</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id value        date\n",
+       "0   3   bar  2020-01-03"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    mode=\"overwrite\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_dataset\"\n",
+    ")\n",
+    "\n",
+    "wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating a **Partitoned** Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>value</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>boo</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id value        date\n",
+       "0   2   boo  2020-01-02\n",
+       "1   1   foo  2020-01-01"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    \"id\": [1, 2],\n",
+    "    \"value\": [\"foo\", \"boo\"],\n",
+    "    \"date\": [date(2020, 1, 1), date(2020, 1, 2)]\n",
+    "})\n",
+    "\n",
+    "wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    mode=\"overwrite\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_dataset\",\n",
+    "    partition_cols=[\"date\"]\n",
+    ")\n",
+    "\n",
+    "wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Upserting partitions (overwrite_partitions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>value</th>\n",
+       "      <th>date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>xoo</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>2020-01-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id value        date\n",
+       "0   2   xoo  2020-01-02\n",
+       "1   3   bar  2020-01-03\n",
+       "2   1   foo  2020-01-01"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    \"id\": [2, 3],\n",
+    "    \"value\": [\"xoo\", \"bar\"],\n",
+    "    \"date\": [date(2020, 1, 2), date(2020, 1, 3)]\n",
+    "})\n",
+    "\n",
+    "wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    mode=\"overwrite_partitions\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_dataset\",\n",
+    "    partition_cols=[\"date\"]\n",
+    ")\n",
+    "\n",
+    "wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_dataset\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_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": "3.6.5"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/12 - CSV Crawler.ipynb b/tutorials/12 - CSV Crawler.ipynb
new file mode 100644
index 000000000..d3e4bd710
--- /dev/null
+++ b/tutorials/12 - CSV Crawler.ipynb	
@@ -0,0 +1,707 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![AWS Data Wrangler](_static/logo.png \"AWS Data Wrangler\")](https://github.com/awslabs/aws-data-wrangler)\n",
+    "\n",
+    "# 12 - CSV Crawler\n",
+    "\n",
+    "[Wrangler](https://github.com/awslabs/aws-data-wrangler) can extract only the metadata from a Pandas DataFrame and then add it can be added to Glue Catalog as a table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import awswrangler as wr\n",
+    "from datetime import datetime\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Enter your bucket name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ··········································\n"
+     ]
+    }
+   ],
+   "source": [
+    "import getpass\n",
+    "bucket = getpass.getpass()\n",
+    "path = f\"s3://{bucket}/csv_crawler/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a Pandas DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>string</th>\n",
+       "      <th>float</th>\n",
+       "      <th>date</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>bool</th>\n",
+       "      <th>par0</th>\n",
+       "      <th>par1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>2020-01-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>boo</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>2020-01-02 00:00:01</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2</td>\n",
+       "      <td>b</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id string  float        date           timestamp   bool  par0 par1\n",
+       "0   1    foo    1.0  2020-01-01 2020-01-01 00:00:00   True     1    a\n",
+       "1   2   None    NaN        None                 NaT   None     1    b\n",
+       "2   3    boo    2.0  2020-01-02 2020-01-02 00:00:01  False     2    b"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ts = lambda x: datetime.strptime(x, \"%Y-%m-%d %H:%M:%S.%f\")  # noqa\n",
+    "dt = lambda x: datetime.strptime(x, \"%Y-%m-%d\").date()  # noqa\n",
+    "\n",
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"id\": [1, 2, 3],\n",
+    "        \"string\": [\"foo\", None, \"boo\"],\n",
+    "        \"float\": [1.0, None, 2.0],\n",
+    "        \"date\": [dt(\"2020-01-01\"), None, dt(\"2020-01-02\")],\n",
+    "        \"timestamp\": [ts(\"2020-01-01 00:00:00.0\"), None, ts(\"2020-01-02 00:00:01.0\")],\n",
+    "        \"bool\": [True, None, False],\n",
+    "        \"par0\": [1, 1, 2],\n",
+    "        \"par1\": [\"a\", \"b\", \"b\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extracting the metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_types, partitions_types = wr.catalog.extract_athena_types(\n",
+    "    df=df,\n",
+    "    file_format=\"csv\",\n",
+    "    index=False,\n",
+    "    partition_cols=[\"par0\", \"par1\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'id': 'bigint',\n",
+       " 'string': 'string',\n",
+       " 'float': 'double',\n",
+       " 'date': 'date',\n",
+       " 'timestamp': 'timestamp',\n",
+       " 'bool': 'boolean'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "columns_types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'par0': 'bigint', 'par1': 'string'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "partitions_types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wr.catalog.create_csv_table(\n",
+    "    table=\"csv_crawler\",\n",
+    "    database=\"awswrangler_test\",\n",
+    "    path=path,\n",
+    "    partitions_types=partitions_types,\n",
+    "    columns_types=columns_types,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Column Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Partition</th>\n",
+       "      <th>Comment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>id</td>\n",
+       "      <td>bigint</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>string</td>\n",
+       "      <td>string</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>float</td>\n",
+       "      <td>double</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>date</td>\n",
+       "      <td>date</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>timestamp</td>\n",
+       "      <td>timestamp</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>bool</td>\n",
+       "      <td>boolean</td>\n",
+       "      <td>False</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>par0</td>\n",
+       "      <td>bigint</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>par1</td>\n",
+       "      <td>string</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Column Name       Type  Partition Comment\n",
+       "0          id     bigint      False        \n",
+       "1      string     string      False        \n",
+       "2       float     double      False        \n",
+       "3        date       date      False        \n",
+       "4   timestamp  timestamp      False        \n",
+       "5        bool    boolean      False        \n",
+       "6        par0     bigint       True        \n",
+       "7        par1     string       True        "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wr.catalog.table(database=\"awswrangler_test\", table=\"csv_crawler\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## We can still using the extracted metadata to ensure all data types consistence to new data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>string</th>\n",
+       "      <th>float</th>\n",
+       "      <th>date</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>bool</th>\n",
+       "      <th>par0</th>\n",
+       "      <th>par1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id string  float       date   timestamp  bool  par0 par1\n",
+       "0   1      1      1 2020-01-01  2020-01-02     1     1    a"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "    {\n",
+    "        \"id\": [1],\n",
+    "        \"string\": [\"1\"],\n",
+    "        \"float\": [1],\n",
+    "        \"date\": [ts(\"2020-01-01 00:00:00.0\")],\n",
+    "        \"timestamp\": [dt(\"2020-01-02\")],\n",
+    "        \"bool\": [1],\n",
+    "        \"par0\": [1],\n",
+    "        \"par1\": [\"a\"],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_crawler\",\n",
+    "    partition_cols=[\"par0\", \"par1\"],\n",
+    "    dtype=columns_types\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## You can also extract the metadata directly from the Catalog with you want"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dtype = wr.catalog.get_table_types(database=\"awswrangler_test\", table=\"csv_crawler\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = wr.s3.to_csv(\n",
+    "    df=df,\n",
+    "    path=path,\n",
+    "    index=False,\n",
+    "    dataset=True,\n",
+    "    database=\"awswrangler_test\",\n",
+    "    table=\"csv_crawler\",\n",
+    "    partition_cols=[\"par0\", \"par1\"],\n",
+    "    dtype=dtype\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking out"
+   ]
+  },
+  {
+   "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>id</th>\n",
+       "      <th>string</th>\n",
+       "      <th>float</th>\n",
+       "      <th>date</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>bool</th>\n",
+       "      <th>par0</th>\n",
+       "      <th>par1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id string  float        date  timestamp  bool  par0 par1\n",
+       "0   1      1    1.0  2020-01-01 2020-01-02  True     1    a\n",
+       "1   1      1    1.0  2020-01-01 2020-01-02  True     1    a"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = wr.athena.read_sql_table(database=\"awswrangler_test\", table=\"csv_crawler\")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                    Int64\n",
+       "string               string\n",
+       "float               float64\n",
+       "date                 object\n",
+       "timestamp    datetime64[ns]\n",
+       "bool                boolean\n",
+       "par0                  Int64\n",
+       "par1                 string\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning Up S3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wr.s3.delete_objects(path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleaning Up the Database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wr.catalog.delete_table_if_exists(database=\"awswrangler_test\", table=\"csv_crawler\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_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": "3.6.5"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}