diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 00000000..b1b0d7a9
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,26 @@
+# Security
+
+If you believe you have found a new security vulnerability in this repository, please report it to us as follows.
+
+## Reporting Security Issues
+
+* Please do **not** report security vulnerabilities through public GitHub issues.
+
+* Please create a draft security advisory on the Github page: the reporting form is under `> Security > Advisories`. The URL is https://github.com/exasol/ai-lab/security/advisories/new.
+
+* If you prefer to email, please send your report to `infosec@exasol.com`.
+
+## Guidelines 
+
+* When reporting a vulnerability, please include as much information as possible, including the complete steps to reproduce the issue. 
+
+* Avoid sending us executables.
+
+* Feel free to include any script you wrote and used but avoid sending us scripts that download and run binaries. 
+
+* We will prioritise reports that show how the exploits work in realistic environments. 
+
+* We prefer all communications to be in English. 
+
+* We do not offer financial rewards. We are happy to acknowledge your research publicly when possible. 
+
diff --git a/doc/changes/changes_3.4.0.md b/doc/changes/changes_3.4.0.md
index 11a5823a..21d1e80f 100644
--- a/doc/changes/changes_3.4.0.md
+++ b/doc/changes/changes_3.4.0.md
@@ -22,7 +22,8 @@ The release also merges the developer CLI commands `setup-ec2-and-install-depend
 * #386: Simplified developer commands
 * #374: Added GPU option to Jupyter UI
 * #387: Added AI-Lab example for customizing CUDA-enabled template SLC
-* #417: Added TE model mgmt notebook
+* #417: Added Transformers Extension model management notebook
+* #420: Added Notebook "First Steps" to demo basic Exasol features
 
 ## Refactorings
 
diff --git a/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps.ipynb b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps.ipynb
new file mode 100644
index 00000000..adacfbac
--- /dev/null
+++ b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps.ipynb
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "91051dad-8d45-435f-b2c9-7572e4050f58",
+   "metadata": {},
+   "source": [
+    "# First Steps\n",
+    "\n",
+    "This notebook guides you through the first steps using Jupyter notebooks with Exasol.\n",
+    "\n",
+    "The notebook demonstrates connecting to an Exasol database instance and using some of its features. \n",
+    "\n",
+    "## 1. Open Secure Configuration Storage\n",
+    "\n",
+    "First we need to open the Secure Configuration Storage (SCS) containing the connection information such as the database host, user, password, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5970a003-896d-4f06-83ee-be98129c29d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run utils/access_store_ui.ipynb\n",
+    "display(get_access_store_ui())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d15075fa-8c9d-4e60-82d9-28d92d38ee0e",
+   "metadata": {},
+   "source": [
+    "## 2. Using JupySQL with Exasol AI Lab\n",
+    "\n",
+    "Next, we will activate the [JupySQL](https://jupysql.ploomber.io) magics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73122275-3f4a-4981-b8b3-ee6c49734996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%run utils/jupysql_init.ipynb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "342a7800-253d-43a0-9bd7-89932876317d",
+   "metadata": {},
+   "source": [
+    "JupySQL allows you to run SQL and plot large datasets in Jupyter via a %sql, %%sql, and %sqlplot magics. Using these magics, you can enter SQL directly into notebook cells.\n",
+    "In the background JupySQL uses SQLAlchemy, see also the [demo section on SQLAlchemy](#4.-SQLAlchemy) below.\n",
+    "\n",
+    "### 2.1 Create Database Tables\n",
+    "\n",
+    "We will use JupySQL to create 2 database tables but other sections will use the tables, too:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a7f5ec4-10f3-49e8-a6e4-48552a760542",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "CREATE OR REPLACE TABLE US_AIRLINES (\n",
+    "  OP_CARRIER_AIRLINE_ID DECIMAL(10, 0) IDENTITY PRIMARY KEY,\n",
+    "  CARRIER_NAME VARCHAR(1000)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd466ee7-7b61-4b3c-8f13-a056be60c933",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "CREATE OR REPLACE TABLE US_FLIGHTS (\n",
+    "  FL_DATE TIMESTAMP, \n",
+    "  OP_CARRIER_AIRLINE_ID DECIMAL(10, 0),\n",
+    "  ORIGIN_AIRPORT_SEQ_ID DECIMAL(10, 0),\n",
+    "  ORIGIN_STATE_ABR CHAR(2),\n",
+    "  DEST_AIRPORT_SEQ_ID DECIMAL(10, 0),\n",
+    "  DEST_STATE_ABR CHAR(2),\n",
+    "  CRS_DEP_TIME CHAR(4),\n",
+    "  DEP_DELAY DOUBLE, \n",
+    "  CRS_ARR_TIME CHAR(4),\n",
+    "  ARR_DELAY DOUBLE,\n",
+    "  CANCELLED BOOLEAN,\n",
+    "  CANCELLATION_CODE CHAR(1),\n",
+    "  DIVERTED BOOLEAN,\n",
+    "  CRS_ELAPSED_TIME DOUBLE,\n",
+    "  ACTUAL_ELAPSED_TIME DOUBLE, \n",
+    "  DISTANCE DOUBLE,\n",
+    "  CARRIER_DELAY DOUBLE,\n",
+    "  WEATHER_DELAY DOUBLE,\n",
+    "  NAS_DELAY DOUBLE,\n",
+    "  SECURITY_DELAY DOUBLE,\n",
+    "  LATE_AIRCRAFT_DELAY DOUBLE\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f83db93-e196-47ae-8e41-48eb21f50f41",
+   "metadata": {},
+   "source": [
+    "### 2.2 Importing CSV Files from Remote\n",
+    "\n",
+    "This section demonstrates how to import CSV files from a remote source into the database.\n",
+    "\n",
+    "First we will import a list of **US airlines**. The data is publicly accessible at the [Bureau of Transportation Statistics](https://www.transtats.bts.gov/Homepage.asp) of the US Department of Transportation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "821b0486-0871-44ab-a96e-66e875a28842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "IMPORT INTO US_AIRLINES FROM\n",
+    "    CSV AT 'https://dut5tonqye28.cloudfront.net/ai_lab/flight-info/' \n",
+    "    FILE 'US_AIRLINES.csv' \n",
+    "    COLUMN SEPARATOR = ',' \n",
+    "    ROW SEPARATOR = 'CRLF'\n",
+    "    COLUMN DELIMITER = '\"' \n",
+    "    SKIP = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2428c14-dd23-4891-8c2f-533f5c2b8f05",
+   "metadata": {},
+   "source": [
+    "Next, we will import data about **flights** in February 2024:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12b76b4f-167b-4769-b454-49fa2871a3b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "IMPORT INTO US_FLIGHTS \n",
+    "    FROM CSV AT 'https://dut5tonqye28.cloudfront.net/ai_lab/first_steps/' \n",
+    "    FILE 'US_FLIGHTS_FEB_2024-fixed-booleans.csv'\n",
+    "    (1 FORMAT = 'MM/DD/YYYY HH12:MI:SS AM', 2..21)    \n",
+    "    SKIP = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b764fd6-c3d9-49b8-bd0a-b36c6c382146",
+   "metadata": {},
+   "source": [
+    "Let's find out which is the airline with the highest delay per flight:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b31b5053-72c8-4846-941b-57d7dc267f30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "SELECT\n",
+    "  CARRIER_NAME \"Airline\",\n",
+    "  SUM(CARRIER_DELAY) \"Combined Delay\",\n",
+    "  COUNT(CARRIER_DELAY) \"Delayed Flights\",\n",
+    "  COUNT(F.OP_CARRIER_AIRLINE_ID) \"Total flights\",\n",
+    "  ROUND( SUM(CARRIER_DELAY) / COUNT(F.OP_CARRIER_AIRLINE_ID), 1 ) \"Delay per flight\"\n",
+    "FROM US_FLIGHTS F\n",
+    "  JOIN US_AIRLINES A ON A.OP_CARRIER_AIRLINE_ID = F.OP_CARRIER_AIRLINE_ID\n",
+    "WHERE NOT (CANCELLED OR DIVERTED)\n",
+    "GROUP BY CARRIER_NAME\n",
+    "ORDER BY \"Delay per flight\" DESC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c29ff5f1-1eb8-452c-8827-6c675c71dba5",
+   "metadata": {},
+   "source": [
+    "### 2.3 Importing a Parquet File from an AWS S3 Bucket\n",
+    "\n",
+    "This demo uses a file already uploaded to S3 bucket `ai-lab-example-data-s3`.\n",
+    "\n",
+    "First we will define a connection pointing to the S3 bucket:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5de58f6-b561-45c3-bc96-38523fcff58b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "CREATE OR REPLACE CONNECTION AI_LAB_FIRST_STEPS_S3 \n",
+    "    TO 'https://ai-lab-example-data-s3.s3.eu-central-1.amazonaws.com'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23507a42-9459-4446-af7f-7d4f65294242",
+   "metadata": {},
+   "source": [
+    "Alternatively the connection can also use the following URL syntax, see also \"_Load data from Parquet files_\" on [docs.exasol.com](https://docs.exasol.com/db/latest/loading_data/load_data_parquet.htm#Overview):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "945dc2dd-8722-4455-b77e-cf6ad88eea23",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "CREATE OR REPLACE CONNECTION AI_LAB_FIRST_STEPS_S3 \n",
+    "    TO 's3://ai-lab-example-data-s3'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e118dd1a-40e6-412a-8624-314e7de1f893",
+   "metadata": {},
+   "source": [
+    "Then we will remove the data imported before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9ba8cad-8451-4f7a-8dec-3d04950d88b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "TRUNCATE TABLE US_FLIGHTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dc80445-e5c0-43c1-ae34-f16326d6fb94",
+   "metadata": {},
+   "source": [
+    "**Please note**: \n",
+    "* Parquet import requires using **<span style=\"color: #40a\">Exasol version 2025.\\* or higher</span>**, see [docs.exasol.com](https://docs.exasol.com/db/latest/loading_data/load_data_parquet.htm).\n",
+    "* Hence, the import is currently commented out.\n",
+    "\n",
+    "Now we can import the Parquet file from S3 into the database:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8aa97292-b0e5-441b-a961-a3d1fba6d23e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql \n",
+    "SELECT 1 -- avoid error from empty cell \n",
+    "\n",
+    "-- IMPORT INTO US_FLIGHTS FROM PARQUET AT AI_LAB_FIRST_STEPS_S3 \n",
+    "--    FILE 'first_steps/US_FLIGHTS_FEB_2024.parquet'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b074a883-ae21-479c-b4fa-17962afe8149",
+   "metadata": {},
+   "source": [
+    "We will query table `US_FLIGHTS` again to display the imported data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "064158a4-8d37-42ab-905f-e13d6c2af3d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql \n",
+    "SELECT * FROM US_FLIGHTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5582ac55-9c56-47d9-b286-948835d976c4",
+   "metadata": {},
+   "source": [
+    "## 3. Using PyExasol with Exasol AI Lab\n",
+    "\n",
+    "[PyExasol](https://exasol.github.io/pyexasol/master/index.html) is the basic Python connector for interacting with Exasol databases.\n",
+    "\n",
+    "Please note\n",
+    "* Using PyExasol's import or export functions with Exasol database versions `2025.*` and higher requires Pyexasol version ≥ `1.2`.\n",
+    "* AI Lab currently is shipped with pyexasol version `0.27.0`.\n",
+    "* Hence, the Pyexasol examples can only be executed with Exasol database versions < `2025`.\n",
+    "\n",
+    "### 3.1 Importing a CSV File from the Local Filesystem\n",
+    "\n",
+    "This section demonstrates how to import a CSV file from the local file system into the database using Pyexasol.\n",
+    "\n",
+    "The function `open_pyexasol_connection()` opens a connection, using the configuration from the SCS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40546944-dd3e-4330-a9c0-8ef57c4b5deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from exasol.nb_connector.connections import open_pyexasol_connection\n",
+    "\n",
+    "with open_pyexasol_connection(ai_lab_config, compression=True) as conn:\n",
+    "    path = Path(\"first_steps/pyexasol.csv\")\n",
+    "    import_params = {\n",
+    "        \"column_delimiter\": '\"',\n",
+    "        \"column_separator\": \",\",\n",
+    "        \"row_separator\": \"CRLF\",\n",
+    "        \"skip\": 1,\n",
+    "    }\n",
+    "    conn.import_from_file(path, (ai_lab_config.db_schema, \"US_AIRLINES\"), import_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e023d10d-ccdb-4c2d-8fa5-655e227e8c76",
+   "metadata": {},
+   "source": [
+    "We will also verify the success by using a query with separate parameters and [PyExasol SQL formatting](https://exasol.github.io/pyexasol/master/user_guide/exploring_features/formatting_sql.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e333f0ae-ac41-4664-a214-65bdbc4541e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_pyexasol_connection\n",
+    "\n",
+    "query = \"SELECT * FROM {table!q} WHERE CARRIER_NAME LIKE {pattern!s}\"\n",
+    "params = {\n",
+    "    \"table\": (ai_lab_config.db_schema, \"US_AIRLINES\"),\n",
+    "    \"pattern\": \"% local CSV file via pyexasol\",\n",
+    "}\n",
+    "\n",
+    "with open_pyexasol_connection(ai_lab_config, compression=True) as conn:\n",
+    "    result = conn.execute(query, params).fetchone()\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38838085-0ea2-4570-9581-7de2f9e1ba20",
+   "metadata": {},
+   "source": [
+    "### 3.2 Importing a CSV File from Remote\n",
+    "\n",
+    "This section demonstrates how to import a CSV file from a remote source into the database using PyExasol.\n",
+    "\n",
+    "First, we will truncate table `US_FLIGHTS` to be able to import the flight data again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e2a0de0-7b72-4466-8f61-815d40ee0f06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "TRUNCATE TABLE US_FLIGHTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0759b904-22b2-4725-a2ef-cb20cdba4e6a",
+   "metadata": {},
+   "source": [
+    "Now let's run the import&mdash;again using a query with separate parameter and PyExasol SQL formatting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "317449c8-4347-40ae-8c7c-bcbe1eb06656",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_pyexasol_connection\n",
+    "\n",
+    "query = \"\"\"\n",
+    "    IMPORT INTO {flights_table!q} FROM CSV AT {url!s} FILE {file!s}\n",
+    "    (1 FORMAT = {date_format!s}, 2..21) \n",
+    "    SKIP = 1\n",
+    "\"\"\"\n",
+    "\n",
+    "params = {\n",
+    "    \"flights_table\": (ai_lab_config.db_schema, \"US_FLIGHTS\"),\n",
+    "    \"url\": \"https://dut5tonqye28.cloudfront.net/ai_lab/first_steps/\",\n",
+    "    \"file\": \"US_FLIGHTS_FEB_2024-fixed-booleans.csv\",\n",
+    "    \"date_format\": \"MM/DD/YYYY HH12:MI:SS AM\",\n",
+    "}\n",
+    "\n",
+    "with open_pyexasol_connection(ai_lab_config, compression=True) as conn:\n",
+    "    result = conn.execute(query, params)\n",
+    "\n",
+    "print(f\"Imported {result.rowcount()} rows.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e99c7a-e6a2-4497-8a74-054dab3ca131",
+   "metadata": {},
+   "source": [
+    "### 3.2 Importing a Parquet File from an AWS S3 Bucket\n",
+    "\n",
+    "This demo uses \n",
+    "* a file already uploaded to S3 bucket `ai-lab-example-data-s3` \n",
+    "* and the Exasol connection object `AI_LAB_FIRST_STEPS_S3` created before.\n",
+    "\n",
+    "First we will truncate table `US_FLIGHTS`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce8ed658-07af-433b-8d77-0aedc3e51310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "TRUNCATE TABLE US_FLIGHTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dfdd7ec-5cd5-4082-a565-ee17166fd691",
+   "metadata": {},
+   "source": [
+    "**Please note**: \n",
+    "* Parquet import requires using **<span style=\"color: #40a\">Exasol version 2025.\\* or higher</span>**, see [docs.exasol.com](https://docs.exasol.com/db/latest/loading_data/load_data_parquet.htm).\n",
+    "* Hence, the import is currently commented out.\n",
+    "\n",
+    "Now we can import the Parquet file from S3 into the database:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70b4826c-12df-4b0f-a5f9-868779bc1905",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_pyexasol_connection\n",
+    "\n",
+    "query = \"IMPORT INTO {table!q} FROM PARQUET AT {connection} FILE {file!s}\"\n",
+    "\n",
+    "params = {\n",
+    "    \"table\": (ai_lab_config.db_schema, \"US_FLIGHTS\"),\n",
+    "    \"connection\": \"AI_LAB_FIRST_STEPS_S3\",\n",
+    "    \"file\": \"first_steps/US_FLIGHTS_FEB_2024.parquet\",\n",
+    "}\n",
+    "\n",
+    "#with open_pyexasol_connection(ai_lab_config, compression=True) as conn:\n",
+    "#    result = conn.execute(query, params)\n",
+    "#print(f\"Imported {result.rowcount()} rows.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b8024f-69c1-4247-ab5a-946d5e98fe2b",
+   "metadata": {},
+   "source": [
+    "## 4. Using SQLAlchemy with Exasol AI Lab\n",
+    "\n",
+    "[SQLAlchemy](https://www.sqlalchemy.org/) is a Python SQL Toolkit and Object Relational Mapper for application developers.\n",
+    "                                                               \n",
+    "It provides a full suite of well known enterprise-level persistence patterns, designed for efficient and high-performing database access, adapted into a simple and Pythonic domain language.\n",
+    "\n",
+    "In Exasol AI Lab, the function `open_sqlalchemy_connection()` returns a SQLAlchemy engine, again using the configuration from the SCS.\n",
+    "\n",
+    "This engine will be used by the examples based on SQLAlchemy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "632a9139-f8e1-45bd-8ea8-43571d2ee3f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_sqlalchemy_connection\n",
+    "engine = open_sqlalchemy_connection(ai_lab_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c7e497-cf0a-4158-b656-221a80086a90",
+   "metadata": {},
+   "source": [
+    "### 3.1 Importing a CSV File from Remote\n",
+    "\n",
+    "This section demonstrates how to import a CSV file from a remote source into the database using SQLAlchemy.\n",
+    "\n",
+    "We will once again truncate table `US_FLIGHTS` and import the flight data, now using SQLAlchemy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f00e6c4-4f44-47d7-a85f-5c1bb70708a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import text\n",
+    "\n",
+    "t = (\n",
+    "    text(\n",
+    "        f\"\"\"\n",
+    "        IMPORT INTO \"{ai_lab_config.db_schema}\".\"US_FLIGHTS\" \n",
+    "        FROM CSV AT 'https://dut5tonqye28.cloudfront.net/ai_lab/first_steps/' \n",
+    "        FILE 'US_FLIGHTS_FEB_2024-fixed-booleans.csv'\n",
+    "        (1 FORMAT = 'MM/DD/YYYY HH12:MI:SS AM', 2..21) \n",
+    "        SKIP = 1\n",
+    "        \"\"\"\n",
+    "    )\n",
+    ")\n",
+    "with engine.connect() as conn:\n",
+    "    conn.execute(\"TRUNCATE TABLE US_FLIGHTS\")\n",
+    "    result = conn.execute(t)\n",
+    "\n",
+    "print(f\"Imported {result.rowcount} rows.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2228c1a-b6ce-432f-b869-ad18abf04b93",
+   "metadata": {},
+   "source": [
+    "### 3.3 Importing a Parquet File from an AWS S3 Bucket\n",
+    "\n",
+    "This section demonstrates how to import a CSV file from an AWS S3 Bucket into the database using SQLAlchemy.\n",
+    "\n",
+    "First we will truncate table `US_FLIGHTS` again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75a19429-89b7-4ed2-b401-e1c2563c2940",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "TRUNCATE TABLE US_FLIGHTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d11dcb6d-d3e5-4e1e-8d61-ba59897c56e9",
+   "metadata": {},
+   "source": [
+    "**Please note**: \n",
+    "* Parquet import requires using **<span style=\"color: #40a\">Exasol version 2025.\\* or higher</span>**, see [docs.exasol.com](https://docs.exasol.com/db/latest/loading_data/load_data_parquet.htm).\n",
+    "* Hence, the import is currently commented out.\n",
+    "\n",
+    "Now we can import the Parquet file from S3 into the database:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "651b41c6-3cd1-4311-a5f3-ababdc9a4273",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import text\n",
+    "\n",
+    "t = (\n",
+    "    text(\n",
+    "        f\"\"\"\n",
+    "        IMPORT INTO \"{ai_lab_config.db_schema}\".\"US_FLIGHTS\" \n",
+    "        FROM PARQUET AT AI_LAB_FIRST_STEPS_S3\n",
+    "        FILE 'first_steps/US_FLIGHTS_FEB_2024.parquet'\n",
+    "        \"\"\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# with engine.connect() as conn:\n",
+    "#     result = conn.execute(t)\n",
+    "# print(f\"Imported {result.rowcount} rows.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35335a66-dace-4082-ba35-a3bcc1ed3d54",
+   "metadata": {},
+   "source": [
+    "### 3.4 Querying using SQLAlchemy’s Metadata Concept\n",
+    "\n",
+    "This section demonstrates using some SQLAlchemy features \n",
+    "* Instantiating class [sqlalchemy.MetaData](https://docs.sqlalchemy.org/en/20/core/metadata.html#sqlalchemy.schema.MetaData)\n",
+    "* Querying existing SQL tables with [.reflect()](https://docs.sqlalchemy.org/en/20/core/metadata.html#sqlalchemy.schema.MetaData)\n",
+    "* Retrieving a specific table \n",
+    "* Using its [select](https://docs.sqlalchemy.org/en/20/core/metadata.html#sqlalchemy.schema.Table.select) statement and\n",
+    "* Executing it in a SQLAlchemy [Session](https://docs.sqlalchemy.org/en/20/orm/session_api.html#sqlalchemy.orm.Session) object\n",
+    "* Iterating the result set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "676b4ae9-19de-4099-92df-d990d242d1c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import MetaData\n",
+    "from sqlalchemy.orm import Session\n",
+    "\n",
+    "meta = MetaData()\n",
+    "db_schema = ai_lab_config.db_schema\n",
+    "meta.reflect(engine, schema=db_schema)\n",
+    "\n",
+    "flights = meta.tables[f\"{db_schema}.us_flights\"]\n",
+    "stmt = (\n",
+    "    flights.select()\n",
+    "    .where(flights.c.op_carrier_airline_id == 20452)\n",
+    "    .where(flights.c.origin_state_abr == \"TX\")\n",
+    "    .where(flights.c.dest_state_abr == \"NJ\")\n",
+    ")\n",
+    "with Session(engine) as session:\n",
+    "    for r in session.execute(stmt):\n",
+    "        print(f'Departure: {r[0].date()} {r[6]}')    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "768ca230-817c-4226-a1b6-ac5c32d4b1c7",
+   "metadata": {},
+   "source": [
+    "## 5. Using the Exasol Bucket File System\n",
+    "\n",
+    "The [Exasol Bucket File System](https://docs.exasol.com/db/latest/database_concepts/bucketfs/bucketfs.htm) (BucketFS) is a powerful feature for exchanging non-relational data with the database nodes in an Exasol cluster.\n",
+    "\n",
+    "Such data can be arbitrary files including \n",
+    "* Data to be processed by [User Defined Scripts](https://docs.exasol.com/db/latest/database_concepts/udf_scripts.htm) (UDFs)\n",
+    "* [Script-Languages Containers](https://github.com/exasol/script-languages-release) (SLCs), see also the dedicated [Tutorial on Script-Languages Containers](script_languages_container/using_the_script_languages_container_tool.ipynb)\n",
+    "* AI Models\n",
+    "\n",
+    "### 5.1 Uploading a File to the BucketFS\n",
+    "\n",
+    "First we will create a sample file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30da84a6-c1bd-4159-830e-88a2fa94ec46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile first_steps/text_file.txt\n",
+    "Hello World!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "644c5311-27e4-463c-b228-c44ce810b50e",
+   "metadata": {},
+   "source": [
+    "And now, let's upload the file into the BucketFS.\n",
+    "\n",
+    "The function `open_bucketfs_location()` returns a path into Exasols BucketFS, also using the configuration in the SCS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f6ab083-d7db-4e76-8e1d-7cf7ff369c87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_bucketfs_location\n",
+    "from pathlib import Path \n",
+    "\n",
+    "file = Path(\"first_steps/text_file.txt\")\n",
+    "bfs = open_bucketfs_location(ai_lab_config)\n",
+    "file_in_bfs = bfs / file.name\n",
+    "file_in_bfs.write(file.read_bytes())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82dc65f7-4d27-41b2-835a-100aa8b79d64",
+   "metadata": {},
+   "source": [
+    "### 5.2 Listing the Files in the BucketFS\n",
+    "\n",
+    "We can also list all the files currently available in the BucketFS:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7597aa88-5c37-4d2d-ae96-be90bf1a5baf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from exasol.nb_connector.connections import open_bucketfs_location\n",
+    "\n",
+    "bfs = open_bucketfs_location(ai_lab_config)\n",
+    "for p in bfs.iterdir():\n",
+    "    print(f'- {p.name}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50b22225-34de-435b-b29d-6712e960b926",
+   "metadata": {},
+   "source": [
+    "### 5.3 Reading the File in the BucketFS Using a User Defined Function\n",
+    "\n",
+    "This sections demonstrates reading the contents of a file in the BucketFS using a [User Defined Function](https://docs.exasol.com/db/latest/database_concepts/udf_scripts.htm) (UDF). \n",
+    "\n",
+    "UDFs can be used in Exasol SQL statements and can also access objects in the BucketFS.\n",
+    "\n",
+    "First we will define the UDF:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dfc7b96-2be8-4bf9-9140-00a1097cec91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "--/\n",
+    "CREATE OR REPLACE PYTHON3 SCALAR SCRIPT \n",
+    "    bfs_file_content(\"path\" VARCHAR(256)) \n",
+    "    EMITS (\"FILE\" VARCHAR(256), CONTENT VARCHAR(2000000)) AS\n",
+    "from pathlib import Path \n",
+    "def run(ctx):  \n",
+    "    ctx.emit(ctx.path, Path(ctx.path).read_text())\n",
+    "/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18cb2e3b-c019-4266-ae0e-4e9cbd5f3778",
+   "metadata": {},
+   "source": [
+    "And now, let's read the contents of the file in the BucketFS we just created before. \n",
+    "\n",
+    "The file's BucketFS location is represented by an instance of class [exasol.bucketfs.PathLike](https://exasol.github.io/bucketfs-python/main/api.html#exasol.bucketfs._path.PathLike) and the method `as_udf_path()` returns the file's path as seen by a UDF:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba88c64b-e3e2-4366-af0c-86c77db35132",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%sql\n",
+    "SELECT bfs_file_content('{{file_in_bfs.as_udf_path()}}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/pyexasol.csv b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/pyexasol.csv
new file mode 100644
index 00000000..02e6c11d
--- /dev/null
+++ b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/pyexasol.csv
@@ -0,0 +1,2 @@
+Code,Description
+"99991","From local CSV file via pyexasol"
diff --git a/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/sqlalchemy.csv b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/sqlalchemy.csv
new file mode 100644
index 00000000..674060c0
--- /dev/null
+++ b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook/first_steps/sqlalchemy.csv
@@ -0,0 +1,2 @@
+Code,Description
+"99993","From local CSV file via SQL Alchemy"
diff --git a/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook_requirements.txt b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook_requirements.txt
index 45004d29..0fee2963 100644
--- a/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook_requirements.txt
+++ b/exasol/ds/sandbox/runtime/ansible/roles/jupyter/files/notebook_requirements.txt
@@ -9,5 +9,5 @@ wordcloud==1.9.4 # required for Text AI notebooks
 pickleshare==0.7.5 # See https://github.com/exasol/ai-lab/issues/291 for details.
 ipyfilechooser==0.6.0 # required for SLC notebooks
 ipywidgets==8.1.5 # enable interactive Javascript widgets in the notebooks
-exasol-notebook-connector==2.0.0
+exasol-notebook-connector [sqlalchemy, pyexasol, bucketfs, docker-db, slc, ibis, transformers, text-ai, sagemaker] ==2.2.0
 pydantic==2.11.4 # See https://github.com/exasol/ai-lab/issues/428
diff --git a/nb_tests.yaml b/nb_tests.yaml
index 218ad2cb..a52c8507 100644
--- a/nb_tests.yaml
+++ b/nb_tests.yaml
@@ -3,6 +3,10 @@ normal:
   additional_pytest_parameters:
   stable:
     tests:
+      - name: First Steps notebook
+        test_file: nbtest_first_steps.py
+        test_backend: onprem
+        wip: no-wip
       - name: CSE notebook
         test_file: nbtest_cloud.py
         test_backend: onprem
diff --git a/test/notebooks/nbtest_first_steps.py b/test/notebooks/nbtest_first_steps.py
new file mode 100644
index 00000000..60cc21a6
--- /dev/null
+++ b/test/notebooks/nbtest_first_steps.py
@@ -0,0 +1,14 @@
+# We need to manually import all fixtures that we use, directly or indirectly,
+# since the pytest won't do this for us.
+from notebook_test_utils import (
+    backend_setup,
+    notebook_runner,
+    set_log_level_for_libraries,
+)
+
+set_log_level_for_libraries()
+
+
+def test_first_steps_notebook(notebook_runner) -> None:
+    notebook_runner('main_config.ipynb')
+    notebook_runner('first_steps.ipynb')