diff --git a/00_Introduction.qmd b/00_Introduction.qmd
index 39bebacea7e4e158aad13e98070b87dee237eb14..34bd408e8c71528e05a7f9dcd6fe809214d97b94 100644
--- a/00_Introduction.qmd
+++ b/00_Introduction.qmd
@@ -173,7 +173,7 @@ business value
 
 ::: {.column width="47.5%"}
 
-- The examination form of this lecture will a sma   ll project be to answer a couple of questions as a team for a given dataset using the methodolog learnt over the course of this lecture
+- The examination form of this lecture will a small project be to answer a couple of questions as a team for a given dataset using the methodolog learnt over the course of this lecture
 - More details will follow
 
 :::
@@ -271,7 +271,7 @@ plot(teams_code_new)#, col = c("white", "grey60"))
 
 ::: {.column width="40%"}
 
-![](https://docs.oracle.com/de-de/iaas/Content/bigdata/images/jupyterhub-launch.png)
+![The user interface of JupyterHub](https://docs.oracle.com/de-de/iaas/Content/bigdata/images/jupyterhub-launch.png){#fig-jupyter}
 :::
 :::
 
@@ -317,7 +317,7 @@ sum_xy(5,6)
 
 ::: columns
 ::: {.column width="47.5%"}
-![](img/jupyter1.png)
+![The login screen for the JupyterHub at FH Münster](img/jupyter1.png){#fig-jupyter1}
 :::
 
 ::: {.column width="5%"}
@@ -335,7 +335,7 @@ sum_xy(5,6)
 
 ::: columns
 ::: {.column width="47.5%"}
-![](img/jupyter2.png)
+![Selection of the server for this class](img/jupyter2.png){#fig-jupyter2}
 :::
 
 ::: {.column width="5%"}
@@ -354,7 +354,7 @@ sum_xy(5,6)
 
 ::: columns
 ::: {.column width="47.5%"}
-![](img/jupyter3.png)
+![Spinning up the server](img/jupyter3.png){#fig-jupyter3}
 :::
 
 ::: {.column width="5%"}
@@ -374,7 +374,7 @@ sum_xy(5,6)
 
 ::: columns
 ::: {.column width="47.5%"}
-![](img/jupyter4.png)
+![The user interface after the login](img/jupyter4.png){#fig-jupyter4}
 :::
 
 ::: {.column width="5%"}
diff --git a/01_data.qmd b/01_data.qmd
index 0a65d605b7ab55123da8f0f50a6fe725c6552b6b..241d9a29b285b9fbba57e06333ad77cc431c1acb 100644
--- a/01_data.qmd
+++ b/01_data.qmd
@@ -2,7 +2,7 @@
 
 ---
 title: "Data Literacy"
-subtitle: "Chapter 1: Data"
+subtitle: "Chapter 1: Data and Data Bases"
 author: Prof. Dr. Michael Bücker
 number-offset: [1,0]
 bibliography: references.bib
@@ -35,8 +35,12 @@ bibliography: references.bib
 - Most important aspect of analog data: stepless
 - Digitization of analog data: analog information is measured in short time intervals and digital information is determined for each measured value
 - The quality of this conversion depends on the short time interval between two measurements and on the accuracy of the measurement
+
 ![An example for quantization of an analog signal](https://upload.wikimedia.org/wikipedia/commons/7/70/Quantized.signal.svg){#fig-quantized}
 
+
+
+
 :::
 ::::
 
@@ -69,9 +73,9 @@ Please watch the following video:
 
 ## Data types
 
-![](https://i.imgur.com/6cg2E9Q.png)
+![Python data types](https://i.imgur.com/6cg2E9Q.png){#fig-pythondatatypes}
 
-# Data types in Python
+## Data types in Python
 
 :::: {.columns}
 
@@ -128,27 +132,27 @@ type(s)
 
 ## From data to wisdom (1/4) {#sec-datawisdom}
 
-![](img/pyramid1.png)
+![The data pyramid (part 1)](img/pyramid1.png){#fig-datapyramid1}
 
 
 ## [-@sec-datawisdom] From data to wisdom (2/4) {.unnumbered}
 
-![](img/pyramid2.png)
+![The data pyramid (part 2)](img/pyramid2.png){#fig-datapyramid2}
  
 ## [-@sec-datawisdom] From data to wisdom (3/4) {.unnumbered}
 
-![](img/pyramid3.png)
+![The data pyramid (part 3)](img/pyramid3.png){#fig-datapyramid3}
 
 
 ## [-@sec-datawisdom] From data to wisdom (4/4)  {.unnumbered}
 
-![](img/pyramid4.png)
+![The data pyramid (part 4)](img/pyramid4.png){#fig-datapyramid4}
 
 
 
 ## Data characteristics
 
-![](img/datacharacteristics.png)
+![Types of data characteristics](img/datacharacteristics.png){#fig-datchar}
 
 ## Data types
 
@@ -161,16 +165,166 @@ type(s)
 
 ## Motivation
 
-## Relational data
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+- **Structured Storage**: Organizes data in a defined manner, allowing for relationship establishment between data types.
+- **Data Integrity and Accuracy**: Ensures data remains accurate and consistent through integrity constraints and validation mechanisms.
+- **Ease of Data Retrieval**: Facilitates data extraction through sophisticated querying and reporting capabilities.
+- **Data Security**: Provides robust protection features to safeguard sensitive data through access controls.
+- **Concurrency Control**: Supports simultaneous data access by multiple users while maintaining data consistency.
+
+
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+- **Data Backup and Recovery**: Offers built-in features to protect against data loss and enables data restoration.
+- **Scalability and Performance**: Efficiently handles growing data and transactions, ensuring application responsiveness.
+- **Compliance and Auditing**: Supports regulatory compliance and provides auditing tools for tracking data access.
+- **Cost Efficiency**: Reduces total ownership cost through consolidated data management and automation.
+- **Data Analysis and Decision-Making**: Enables data mining and analysis for informed decision-making and insights.
+
+:::
+::::
+
 
 ## Relational data models
 
+- **Definition**: A relational data model organizes data into tables (or relations) where each table represents a different entity, and each row in a table represents a unique instance of that entity. Columns within the tables represent attributes of the entities.
+
+- **Normalization**: A technique used to minimize data redundancy and avoid undesirable characteristics like insertion, update, and deletion anomalies by organizing data in a way that eliminates repeating groups and ensures data dependencies make sense.
+
+- **ACID Properties**:
+  - **Atomicity**: Ensures that all parts of a transaction are completed successfully or not at all.
+  - **Consistency**: Ensures that the database remains in a consistent state before and after the transaction.
+  - **Isolation**: Ensures that transactions are securely and independently processed at the same time without interference.
+  - **Durability**: Ensures that the effects of a transaction are permanent and can withstand system failures.
+
+
+- **Schema**: Defines the structure of the relational database including tables, fields, and the relationships between them. The schema acts as a blueprint for how data is organized and how relationships between data are handled.
+
+
+
+
+## Relational data schemas
+
+A Relational Model is a type of database model based on the concept of relations, which are akin to tables of data. In a relation, data is organized in tuples (rows) and attributes (columns).
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+
+#### 1. Relations (Tables)
+- A **Relation** is a set of tuples.
+- Each **Tuple** represents a single item.
+- Each **Attribute** in a tuple has a specific data type.
+
+
+#### 2. Relationship cardinalities
+- **One-to-One (1:1):** Each item in one relation is linked to exactly one item in another relation.
+- **One-to-Many (1:M):** One item in a relation can be linked to many items in another relation.
+- **Many-to-Many (M:M):** Items in one relation can be linked to multiple items in another relation.
+
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+
+
+#### 3. Keys
+- **Primary Key:** A unique identifier for each tuple within a relation.
+- **Foreign Key:** A field in one relation that refers to the primary key in another relation.
+
+#### 4. Integrity Constraints
+- **Entity Integrity:** E.g. no primary key value can be null.
+- **Referential Integrity:** Ensures that relationships between relations are maintained.
+- ...
+:::
+::::
+
+
+
+
+## Visualization of relational data models
+
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+
+![Exmaple for the visualization of a relational data model](https://dev.mysql.com/doc/employee/en/images/employees-schema.png){#fig-relmod}
+
+
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+
+- In a visualization of relational data models, each **table** is represented by a box with the table's name on top and the list of **columns/attributes** below
+- Special columns like **primary and foreign keys** are marked
+- **Relationships** are represented by connections between the tables with respective notations for the **cardinalities** (see [@fig-cardinalities])
+
+![Notation of relationship cardinalities](https://d2slcw3kip6qmk.cloudfront.net/marketing/pages/chart/erd-symbols/ERD-Notation.PNG){#fig-cardinalities}
+:::
+::::
+
 ## Accessing data bases
 
 ## Working with data bases - SQL
 
 ## Other types of data bases
 
+Traditional Relational Database Management Systems (RDBMS) have been the standard for data storage and management. However, with the advent of big data and real-time applications, other database models have emerged to address specific needs.
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+#### 1. NoSQL Databases
+- **Key-Value Stores:** Simple and highly scalable, e.g., Redis, DynamoDB.
+- **Document Stores:** Store, retrieve, and manage document-oriented information, e.g., MongoDB, CouchDB.
+- **Column-family Stores:** Ideal for handling large data sets, e.g., Cassandra, HBase.
+- **Graph Databases:** Excellent for managing interconnected data, e.g., Neo4j, Amazon Neptune.
+
+#### 2. NewSQL Databases
+- Aim to provide the scalability of NoSQL databases while maintaining the ACID properties of relational databases, e.g. Google Spanner, CockroachDB.
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+
+
+#### 3. In-Memory Databases (IMDBs)
+- Store data in the main memory (instead of disk) for faster data access, e.g., Redis, SAP HANA.
+
+#### 4. Time Series Databases (TSDBs)
+- Optimized for handling time-series data, e.g., InfluxDB, Prometheus.
+
+#### 5. Multi-model Databases
+- Support multiple data models within a single, integrated backend, e.g., ArangoDB, OrientDB.
+
+:::
+::::
+
+
+
+
+
 # References {.unnumbered .scrollable}
 
 ::: {#refs}
diff --git a/output/00_Introduction.html b/output/00_Introduction.html
index 443639522e7010a891b6baa2d810944461e6bf1b..f887020d8b920acf60629bd8efd64636b94f18a6 100644
--- a/output/00_Introduction.html
+++ b/output/00_Introduction.html
@@ -608,7 +608,7 @@ Chief Economist at Google</p>
 <div class="columns">
 <div class="column" style="width:47.5%;">
 <ul>
-<li>The examination form of this lecture will a sma ll project be to answer a couple of questions as a team for a given dataset using the methodolog learnt over the course of this lecture</li>
+<li>The examination form of this lecture will a small project be to answer a couple of questions as a team for a given dataset using the methodolog learnt over the course of this lecture</li>
 <li>More details will follow</li>
 </ul>
 </div><div class="column" style="width:5%;">
@@ -677,7 +677,12 @@ Chief Economist at Google</p>
 </div><div class="column" style="width:5%;">
 
 </div><div class="column" style="width:40%;">
+<div id="fig-jupyter" class="quarto-figure quarto-figure-center">
+<figure>
 <p><img data-src="https://docs.oracle.com/de-de/iaas/Content/bigdata/images/jupyterhub-launch.png"></p>
+<figcaption>Figure&nbsp;4: The user interface of JupyterHub</figcaption>
+</figure>
+</div>
 </div>
 </div>
 </section>
@@ -711,7 +716,12 @@ Chief Economist at Google</p>
 <h3><span class="header-section-number">0.4.3</span> Accessing the FH Münster Jupyter Hub (1/4)</h3>
 <div class="columns">
 <div class="column" style="width:47.5%;">
+<div id="fig-jupyter1" class="quarto-figure quarto-figure-center">
+<figure>
 <p><img data-src="img/jupyter1.png"></p>
+<figcaption>Figure&nbsp;5: The login screen for the JupyterHub at FH Münster</figcaption>
+</figure>
+</div>
 </div><div class="column" style="width:5%;">
 
 </div><div class="column" style="width:47.5%;">
@@ -726,7 +736,12 @@ Chief Economist at Google</p>
 <h3><a href="#/sec-jupyterhub">0.4.3</a> Accessing the FH Münster Jupyter Hub (2/4)</h3>
 <div class="columns">
 <div class="column" style="width:47.5%;">
+<div id="fig-jupyter2" class="quarto-figure quarto-figure-center">
+<figure>
 <p><img data-src="img/jupyter2.png"></p>
+<figcaption>Figure&nbsp;6: Selection of the server for this class</figcaption>
+</figure>
+</div>
 </div><div class="column" style="width:5%;">
 
 </div><div class="column" style="width:47.5%;">
@@ -742,7 +757,12 @@ Chief Economist at Google</p>
 <h3><a href="#/sec-jupyterhub">0.4.3</a> Accessing the FH Münster Jupyter Hub (3/4)</h3>
 <div class="columns">
 <div class="column" style="width:47.5%;">
+<div id="fig-jupyter3" class="quarto-figure quarto-figure-center">
+<figure>
 <p><img data-src="img/jupyter3.png"></p>
+<figcaption>Figure&nbsp;7: Spinning up the server</figcaption>
+</figure>
+</div>
 </div><div class="column" style="width:5%;">
 
 </div><div class="column" style="width:47.5%;">
@@ -759,7 +779,12 @@ Chief Economist at Google</p>
 <h3><a href="#/sec-jupyterhub">0.4.3</a> Accessing the FH Münster Jupyter Hub (4/4)</h3>
 <div class="columns">
 <div class="column" style="width:47.5%;">
+<div id="fig-jupyter4" class="quarto-figure quarto-figure-center">
+<figure>
 <p><img data-src="img/jupyter4.png"></p>
+<figcaption>Figure&nbsp;8: The user interface after the login</figcaption>
+</figure>
+</div>
 </div><div class="column" style="width:5%;">
 
 </div><div class="column" style="width:47.5%;">
@@ -793,7 +818,7 @@ Chief Economist at Google</p>
 <div id="fig-stackoverflow" class="quarto-figure quarto-figure-center">
 <figure>
 <p><img data-src="img/stackoverflow.png"></p>
-<figcaption>Figure&nbsp;4: Questions and answers on <a href="https://stackoverflow.com/questions/tagged/pandas">Stack Overflow</a> with regards to the Python library <a href="https://pandas.pydata.org/">pandas</a></figcaption>
+<figcaption>Figure&nbsp;9: Questions and answers on <a href="https://stackoverflow.com/questions/tagged/pandas">Stack Overflow</a> with regards to the Python library <a href="https://pandas.pydata.org/">pandas</a></figcaption>
 </figure>
 </div>
 </div>
@@ -813,7 +838,7 @@ Chief Economist at Google</p>
 <div id="fig-youtube" class="quarto-figure quarto-figure-center">
 <figure>
 <p><img data-src="img/youtube.png"></p>
-<figcaption>Figure&nbsp;5: The <a href="https://www.youtube.com/@coreyms">coreyms</a> channel on YouTube with many Python coding instruction videos</figcaption>
+<figcaption>Figure&nbsp;10: The <a href="https://www.youtube.com/@coreyms">coreyms</a> channel on YouTube with many Python coding instruction videos</figcaption>
 </figure>
 </div>
 </div>
@@ -847,7 +872,7 @@ Chief Economist at Google</p>
 <div id="fig-chatgpt" class="quarto-figure quarto-figure-center">
 <figure>
 <p><img data-src="img/chatgpt.png"></p>
-<figcaption>Figure&nbsp;6: ChatGPT writing and testing Python code</figcaption>
+<figcaption>Figure&nbsp;11: ChatGPT writing and testing Python code</figcaption>
 </figure>
 </div>
 </div>
diff --git a/output/01_data.html b/output/01_data.html
index 28cb3abcdd6e089a21ddd05e9d980d55c09509f3..7be3567a5acf715ad571bf9a470493d7607ec7dd 100644
--- a/output/01_data.html
+++ b/output/01_data.html
@@ -412,7 +412,7 @@
 
 <section id="title-slide" data-background-image="img/title.png" data-background-size="cover" class="quarto-title-block center">
   <h1 class="title">Data Literacy</h1>
-  <p class="subtitle">Chapter 1: Data</p>
+  <p class="subtitle">Chapter 1: Data and Data Bases</p>
 
 <div class="quarto-title-authors">
 <div class="quarto-title-author">
@@ -427,8 +427,7 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <h2 id="toc-title">Table of contents</h2>
 <ul>
 <li><a href="#/data" id="/toc-data"><span class="header-section-number">1.1</span> Data</a></li>
-<li><a href="#/data-types-in-python" id="/toc-data-types-in-python"><span class="header-section-number">1.2</span> Data types in Python</a></li>
-<li><a href="#/databases" id="/toc-databases"><span class="header-section-number">1.3</span> Databases</a></li>
+<li><a href="#/databases" id="/toc-databases"><span class="header-section-number">1.2</span> Databases</a></li>
 <li><a href="#/references" id="/toc-references">References</a></li>
 </ul>
 </nav>
@@ -455,8 +454,14 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <ul>
 <li>Most important aspect of analog data: stepless</li>
 <li>Digitization of analog data: analog information is measured in short time intervals and digital information is determined for each measured value</li>
-<li>The quality of this conversion depends on the short time interval between two measurements and on the accuracy of the measurement <img data-src="https://upload.wikimedia.org/wikipedia/commons/7/70/Quantized.signal.svg" id="fig-quantized" alt="An example for quantization of an analog signal"></li>
+<li>The quality of this conversion depends on the short time interval between two measurements and on the accuracy of the measurement</li>
 </ul>
+<div id="fig-quantized" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="https://upload.wikimedia.org/wikipedia/commons/7/70/Quantized.signal.svg"></p>
+<figcaption>Figure&nbsp;1.1: An example for quantization of an analog signal</figcaption>
+</figure>
+</div>
 </div>
 </div>
 </section>
@@ -498,10 +503,9 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <section id="data-types" class="slide level3" data-number="1.1.5">
 <h3><span class="header-section-number">1.1.5</span> Data types</h3>
 
-<img data-src="https://i.imgur.com/6cg2E9Q.png" class="r-stretch"></section></section>
-<section>
-<section id="data-types-in-python" class="title-slide slide level2 center" data-number="1.2">
-<h2><span class="header-section-number">1.2</span> Data types in Python</h2>
+<img data-src="https://i.imgur.com/6cg2E9Q.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.2: Python data types</p></section>
+<section id="data-types-in-python" class="slide level3" data-number="1.1.6">
+<h3><span class="header-section-number">1.1.6</span> Data types in Python</h3>
 <div class="columns">
 <div class="column" style="width:47.5%;">
 <ul>
@@ -515,7 +519,7 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <div class="cell-output cell-output-stdout">
 <pre><code>1</code></pre>
 </div>
-<div class="cell-output cell-output-display" data-execution_count="5">
+<div class="cell-output cell-output-display" data-execution_count="33">
 <pre><code>int</code></pre>
 </div>
 </div>
@@ -530,7 +534,7 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <div class="cell-output cell-output-stdout">
 <pre><code>1.1</code></pre>
 </div>
-<div class="cell-output cell-output-display" data-execution_count="6">
+<div class="cell-output cell-output-display" data-execution_count="34">
 <pre><code>float</code></pre>
 </div>
 </div>
@@ -548,7 +552,7 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <div class="cell-output cell-output-stdout">
 <pre><code>True</code></pre>
 </div>
-<div class="cell-output cell-output-display" data-execution_count="7">
+<div class="cell-output cell-output-display" data-execution_count="35">
 <pre><code>bool</code></pre>
 </div>
 </div>
@@ -563,60 +567,182 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <div class="cell-output cell-output-stdout">
 <pre><code>Text</code></pre>
 </div>
-<div class="cell-output cell-output-display" data-execution_count="8">
+<div class="cell-output cell-output-display" data-execution_count="36">
 <pre><code>str</code></pre>
 </div>
 </div>
 </div>
 </div>
 </section>
-<section id="sec-datawisdom" class="slide level3" data-number="1.2.1">
-<h3><span class="header-section-number">1.2.1</span> From data to wisdom (1/4)</h3>
+<section id="sec-datawisdom" class="slide level3" data-number="1.1.7">
+<h3><span class="header-section-number">1.1.7</span> From data to wisdom (1/4)</h3>
 
-<img data-src="img/pyramid1.png" class="r-stretch"></section>
+<img data-src="img/pyramid1.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.3: The data pyramid (part 1)</p></section>
 <section id="sec-datawisdom-from-data-to-wisdom-24" class="slide level3 unnumbered">
-<h3><a href="#/sec-datawisdom">1.2.1</a> From data to wisdom (2/4)</h3>
+<h3><a href="#/sec-datawisdom">1.1.7</a> From data to wisdom (2/4)</h3>
 
-<img data-src="img/pyramid2.png" class="r-stretch"></section>
+<img data-src="img/pyramid2.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.4: The data pyramid (part 2)</p></section>
 <section id="sec-datawisdom-from-data-to-wisdom-34" class="slide level3 unnumbered">
-<h3><a href="#/sec-datawisdom">1.2.1</a> From data to wisdom (3/4)</h3>
+<h3><a href="#/sec-datawisdom">1.1.7</a> From data to wisdom (3/4)</h3>
 
-<img data-src="img/pyramid3.png" class="r-stretch"></section>
+<img data-src="img/pyramid3.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.5: The data pyramid (part 3)</p></section>
 <section id="sec-datawisdom-from-data-to-wisdom-44" class="slide level3 unnumbered">
-<h3><a href="#/sec-datawisdom">1.2.1</a> From data to wisdom (4/4)</h3>
+<h3><a href="#/sec-datawisdom">1.1.7</a> From data to wisdom (4/4)</h3>
 
-<img data-src="img/pyramid4.png" class="r-stretch"></section>
-<section id="data-characteristics" class="slide level3" data-number="1.2.2">
-<h3><span class="header-section-number">1.2.2</span> Data characteristics</h3>
+<img data-src="img/pyramid4.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.6: The data pyramid (part 4)</p></section>
+<section id="data-characteristics" class="slide level3" data-number="1.1.8">
+<h3><span class="header-section-number">1.1.8</span> Data characteristics</h3>
 
-<img data-src="img/datacharacteristics.png" class="r-stretch"></section>
-<section id="data-types-1" class="slide level3" data-number="1.2.3">
-<h3><span class="header-section-number">1.2.3</span> Data types</h3>
+<img data-src="img/datacharacteristics.png" class="r-stretch quarto-figure-center"><p class="caption">Figure&nbsp;1.7: Types of data characteristics</p></section>
+<section id="data-types-1" class="slide level3" data-number="1.1.9">
+<h3><span class="header-section-number">1.1.9</span> Data types</h3>
 </section></section>
 <section>
-<section id="databases" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="1.3">
-<h2><span class="header-section-number">1.3</span> Databases</h2>
+<section id="databases" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="1.2">
+<h2><span class="header-section-number">1.2</span> Databases</h2>
 <div class="footer">
 
 </div>
 </section>
-<section id="motivation" class="slide level3" data-number="1.3.1">
-<h3><span class="header-section-number">1.3.1</span> Motivation</h3>
+<section id="motivation" class="slide level3" data-number="1.2.1">
+<h3><span class="header-section-number">1.2.1</span> Motivation</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li><strong>Structured Storage</strong>: Organizes data in a defined manner, allowing for relationship establishment between data types.</li>
+<li><strong>Data Integrity and Accuracy</strong>: Ensures data remains accurate and consistent through integrity constraints and validation mechanisms.</li>
+<li><strong>Ease of Data Retrieval</strong>: Facilitates data extraction through sophisticated querying and reporting capabilities.</li>
+<li><strong>Data Security</strong>: Provides robust protection features to safeguard sensitive data through access controls.</li>
+<li><strong>Concurrency Control</strong>: Supports simultaneous data access by multiple users while maintaining data consistency.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<ul>
+<li><strong>Data Backup and Recovery</strong>: Offers built-in features to protect against data loss and enables data restoration.</li>
+<li><strong>Scalability and Performance</strong>: Efficiently handles growing data and transactions, ensuring application responsiveness.</li>
+<li><strong>Compliance and Auditing</strong>: Supports regulatory compliance and provides auditing tools for tracking data access.</li>
+<li><strong>Cost Efficiency</strong>: Reduces total ownership cost through consolidated data management and automation.</li>
+<li><strong>Data Analysis and Decision-Making</strong>: Enables data mining and analysis for informed decision-making and insights.</li>
+</ul>
+</div>
+</div>
+</section>
+<section id="relational-data-models" class="slide level3" data-number="1.2.2">
+<h3><span class="header-section-number">1.2.2</span> Relational data models</h3>
+<ul>
+<li><p><strong>Definition</strong>: A relational data model organizes data into tables (or relations) where each table represents a different entity, and each row in a table represents a unique instance of that entity. Columns within the tables represent attributes of the entities.</p></li>
+<li><p><strong>Normalization</strong>: A technique used to minimize data redundancy and avoid undesirable characteristics like insertion, update, and deletion anomalies by organizing data in a way that eliminates repeating groups and ensures data dependencies make sense.</p></li>
+<li><p><strong>ACID Properties</strong>:</p>
+<ul>
+<li><strong>Atomicity</strong>: Ensures that all parts of a transaction are completed successfully or not at all.</li>
+<li><strong>Consistency</strong>: Ensures that the database remains in a consistent state before and after the transaction.</li>
+<li><strong>Isolation</strong>: Ensures that transactions are securely and independently processed at the same time without interference.</li>
+<li><strong>Durability</strong>: Ensures that the effects of a transaction are permanent and can withstand system failures.</li>
+</ul></li>
+<li><p><strong>Schema</strong>: Defines the structure of the relational database including tables, fields, and the relationships between them. The schema acts as a blueprint for how data is organized and how relationships between data are handled.</p></li>
+</ul>
 </section>
-<section id="relational-data" class="slide level3" data-number="1.3.2">
-<h3><span class="header-section-number">1.3.2</span> Relational data</h3>
+<section id="relational-data-schemas" class="slide level3" data-number="1.2.3">
+<h3><span class="header-section-number">1.2.3</span> Relational data schemas</h3>
+<p>A Relational Model is a type of database model based on the concept of relations, which are akin to tables of data. In a relation, data is organized in tuples (rows) and attributes (columns).</p>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<h5 id="relations-tables">1. Relations (Tables)</h5>
+<ul>
+<li>A <strong>Relation</strong> is a set of tuples.</li>
+<li>Each <strong>Tuple</strong> represents a single item.</li>
+<li>Each <strong>Attribute</strong> in a tuple has a specific data type.</li>
+</ul>
+<h5 id="relationship-cardinalities">2. Relationship cardinalities</h5>
+<ul>
+<li><strong>One-to-One (1:1):</strong> Each item in one relation is linked to exactly one item in another relation.</li>
+<li><strong>One-to-Many (1:M):</strong> One item in a relation can be linked to many items in another relation.</li>
+<li><strong>Many-to-Many (M:M):</strong> Items in one relation can be linked to multiple items in another relation.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<h5 id="keys">3. Keys</h5>
+<ul>
+<li><strong>Primary Key:</strong> A unique identifier for each tuple within a relation.</li>
+<li><strong>Foreign Key:</strong> A field in one relation that refers to the primary key in another relation.</li>
+</ul>
+<h5 id="integrity-constraints">4. Integrity Constraints</h5>
+<ul>
+<li><strong>Entity Integrity:</strong> E.g. no primary key value can be null.</li>
+<li><strong>Referential Integrity:</strong> Ensures that relationships between relations are maintained.</li>
+<li>…</li>
+</ul>
+</div>
+</div>
 </section>
-<section id="relational-data-models" class="slide level3" data-number="1.3.3">
-<h3><span class="header-section-number">1.3.3</span> Relational data models</h3>
+<section id="visualization-of-relational-data-models" class="slide level3" data-number="1.2.4">
+<h3><span class="header-section-number">1.2.4</span> Visualization of relational data models</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<div id="fig-relmod" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="https://dev.mysql.com/doc/employee/en/images/employees-schema.png"></p>
+<figcaption>Figure&nbsp;1.8: Exmaple for the visualization of a relational data model</figcaption>
+</figure>
+</div>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<ul>
+<li>In a visualization of relational data models, each <strong>table</strong> is represented by a box with the table’s name on top and the list of <strong>columns/attributes</strong> below</li>
+<li>Special columns like <strong>primary and foreign keys</strong> are marked</li>
+<li><strong>Relationships</strong> are represented by connections between the tables with respective notations for the <strong>cardinalities</strong> (see <a href="#/visualization-of-relational-data-models">Figure&nbsp;1.9</a>)</li>
+</ul>
+<div id="fig-cardinalities" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="https://d2slcw3kip6qmk.cloudfront.net/marketing/pages/chart/erd-symbols/ERD-Notation.PNG"></p>
+<figcaption>Figure&nbsp;1.9: Notation of relationship cardinalities</figcaption>
+</figure>
+</div>
+</div>
+</div>
 </section>
-<section id="accessing-data-bases" class="slide level3" data-number="1.3.4">
-<h3><span class="header-section-number">1.3.4</span> Accessing data bases</h3>
+<section id="accessing-data-bases" class="slide level3" data-number="1.2.5">
+<h3><span class="header-section-number">1.2.5</span> Accessing data bases</h3>
 </section>
-<section id="working-with-data-bases---sql" class="slide level3" data-number="1.3.5">
-<h3><span class="header-section-number">1.3.5</span> Working with data bases - SQL</h3>
+<section id="working-with-data-bases---sql" class="slide level3" data-number="1.2.6">
+<h3><span class="header-section-number">1.2.6</span> Working with data bases - SQL</h3>
 </section>
-<section id="other-types-of-data-bases" class="slide level3" data-number="1.3.6">
-<h3><span class="header-section-number">1.3.6</span> Other types of data bases</h3>
+<section id="other-types-of-data-bases" class="slide level3" data-number="1.2.7">
+<h3><span class="header-section-number">1.2.7</span> Other types of data bases</h3>
+<p>Traditional Relational Database Management Systems (RDBMS) have been the standard for data storage and management. However, with the advent of big data and real-time applications, other database models have emerged to address specific needs.</p>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<h5 id="nosql-databases">1. NoSQL Databases</h5>
+<ul>
+<li><strong>Key-Value Stores:</strong> Simple and highly scalable, e.g., Redis, DynamoDB.</li>
+<li><strong>Document Stores:</strong> Store, retrieve, and manage document-oriented information, e.g., MongoDB, CouchDB.</li>
+<li><strong>Column-family Stores:</strong> Ideal for handling large data sets, e.g., Cassandra, HBase.</li>
+<li><strong>Graph Databases:</strong> Excellent for managing interconnected data, e.g., Neo4j, Amazon Neptune.</li>
+</ul>
+<h5 id="newsql-databases">2. NewSQL Databases</h5>
+<ul>
+<li>Aim to provide the scalability of NoSQL databases while maintaining the ACID properties of relational databases, e.g.&nbsp;Google Spanner, CockroachDB.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<h5 id="in-memory-databases-imdbs">3. In-Memory Databases (IMDBs)</h5>
+<ul>
+<li>Store data in the main memory (instead of disk) for faster data access, e.g., Redis, SAP HANA.</li>
+</ul>
+<h5 id="time-series-databases-tsdbs">4. Time Series Databases (TSDBs)</h5>
+<ul>
+<li>Optimized for handling time-series data, e.g., InfluxDB, Prometheus.</li>
+</ul>
+<h5 id="multi-model-databases">5. Multi-model Databases</h5>
+<ul>
+<li>Support multiple data models within a single, integrated backend, e.g., ArangoDB, OrientDB.</li>
+</ul>
+</div>
+</div>
 </section></section>
 <section id="references" class="title-slide slide level2 unnumbered scrollable smaller">
 <h2>References</h2>