diff --git a/src/machine-learning-and-data-mining/main.tex b/src/machine-learning-and-data-mining/main.tex
index 6cfcf6a..8afd97d 100644
--- a/src/machine-learning-and-data-mining/main.tex
+++ b/src/machine-learning-and-data-mining/main.tex
@@ -24,6 +24,7 @@
     \newpage
 
     \input{sections/_intro.tex}
-    \input{sections/_bi.tex}
+    \input{sections/_data_warehouse.tex}
+    \input{sections/_data_lake.tex}
 
 \end{document}
\ No newline at end of file
diff --git a/src/machine-learning-and-data-mining/sections/_data_lake.tex b/src/machine-learning-and-data-mining/sections/_data_lake.tex
new file mode 100644
index 0000000..4309164
--- /dev/null
+++ b/src/machine-learning-and-data-mining/sections/_data_lake.tex
@@ -0,0 +1,206 @@
+\chapter{Data lake}
+
+\begin{description}
+    \item[Dark data] \marginnote{Dark data}
+        Acquired and stored data that are never used for decision-making processes.
+
+    \item[Data lake] \marginnote{Data lake}
+        Repository to store raw (unstructured) data.
+        It has the following features:
+        \begin{itemize}
+            \item Does not enforce a schema on write.
+            \item Allows flexible access and applies schemas on read.
+            \item Single source of truth.
+            \item Low cost and scalable.
+        \end{itemize}
+
+    \item[Storage]
+        Stored data can be classified as:
+        \begin{descriptionlist}
+            \item[Hot] \marginnote{Hot storage}
+                A low volume of highly requested data that require low latency.
+                More expensive HW/SW.
+            \item[Cold] \marginnote{Cold storage}
+                A large amount of data that does not have latency requirements.
+                Less expensive.
+        \end{descriptionlist}
+
+        \begin{figure}[ht]
+            \centering
+            \includegraphics[width=0.5\textwidth]{img/_storage.pdf}
+            \caption{Data storage technologies}
+        \end{figure}
+\end{description}
+
+
+\section{Traditional vs insight-driven data systems}
+\begin{tabular}{c | p{0.4\textwidth} | p{0.4\textwidth}}
+    & \textbf{\makecell[c]{Traditional (data warehouse)}} & \textbf{\makecell[c]{Insight-driven (data lake)}} \\
+    \hline
+    \textbf{Sources} & Structured data & Structured, semi-structured and unstructured data \\
+    \hline
+    \textbf{Storage} & Limited ingestion and storage capability & Virtually unlimited ingestion and storage capability \\
+    \hline
+    \textbf{Schema} & Schema designed upfront & Schema not fixed \\
+    \hline
+    \textbf{Transformations} & \ac{etl} upfront & Transformations on query \\
+    \hline
+    \textbf{Analytics} & SQL, \ac{bi} tools, full-text search & Traditional methods, self-service \ac{bi}, big data, machine learning, \dots \\
+    \hline
+    \textbf{Price} & High storage cost & Low storage cost \\
+    \textbf{Performance} & Fast queries & Scalability/speed/cost tradeoffs \\
+    \hline
+    \textbf{Quality} & High data quality & Depends on the use case \\
+\end{tabular}
+
+
+\section{Data architecture evolution}
+\begin{description}
+    \item[Traditional data warehouse] \marginnote{Traditional data warehouse} 
+        (i.e. in-house data warehouse)
+        \begin{itemize}
+            \item Structured data with predefined schemas.
+            \item High setup and maintenance cost. Not scalable.
+            \item Relational high-quality data.
+            \item Slow data ingestion.
+        \end{itemize}
+
+    \item[Modern cloud data warehouse] \marginnote{Modern cloud data warehouse} 
+        \phantom{}
+        \begin{itemize}
+            \item Structured and semi-structured data.
+            \item Low setup and maintenance cost. Scalable and easier disaster recovery.
+            \item Relational high-quality data and mixed data.
+            \item Fast data ingestion if supported.
+        \end{itemize}
+
+    \item[On-premise big data] \marginnote{On-premise big data} 
+        (i.e. in-house data lake)
+        \begin{itemize}
+            \item Any type of data with schemas on read.
+            \item High setup and maintenance cost.
+            \item Fast data ingestion.
+        \end{itemize}
+
+    \item[Cloud data lake] \marginnote{Cloud data lake} 
+        \phantom{}
+        \begin{itemize}
+            \item Any type of data with schemas on read.
+            \item Low setup and maintenance cost. Scalable and easier disaster recovery.
+            \item Fast data ingestion.
+        \end{itemize}
+\end{description}
+
+
+\section{Components}
+
+\subsection{Data ingestion} 
+\marginnote{Data ingestion}
+    \begin{descriptionlist}
+        \item[Workload migration]
+            Inserting all the data from an existing source.
+        \item[Incremental ingestion]
+            Inserting changes since the last ingestion.
+        \item[Streaming ingestion]   
+            Continuously inserting data.
+    \end{descriptionlist}
+
+    \begin{description}
+        \item[\Acl{cdc} (\Acs{cdc})] \marginnote{\Acl{cdc} (\Acs{cdc})}
+            Mechanism to detect changes and insert the new data into the data lake (possibly in real-time).
+    \end{description}
+
+\subsection{Storage}
+\begin{descriptionlist}
+    \item[Raw] \marginnote{Raw storage}
+        Immutable data useful for disaster recovery.
+    \item[Optimized] \marginnote{Optimized storage}
+        Optimized raw data for faster query.
+    \item[Analytics] \marginnote{Analytics storage}
+        Ready to use data.
+\end{descriptionlist}
+
+\begin{description}
+    \item[Columnar storage] \phantom{}
+        \begin{itemize}
+            \item Homogenous data are stores contiguously.
+            \item Speeds up methods that process entire columns (i.e. all the values of a feature).
+            \item Insertion becomes slower.
+        \end{itemize}
+
+    \item[Data catalog]
+        Methods to add descriptive metadata to a data lake.
+        This is useful to prevent an unorganized data lake (data swamp).
+\end{description}
+        
+\subsection{Processing and analytics} 
+\marginnote{Processing and analytics}
+\begin{descriptionlist}
+    \item[Interactive analytics]
+        Interactive queries to large volumes of data.
+        The results are stored back in the data lake.
+    \item[Big data analytics]
+        Data aggregations and transformations.
+    \item[Real-time analytics]   
+        Streaming analysis.
+\end{descriptionlist}
+
+
+\section{Architectures}
+
+\subsection{Lambda lake} 
+\marginnote{Lambda lake}
+\begin{description}
+    \item[Batch layer] Receives and stores the data. Prepares the batch views for the serving layer.
+    \item[Serving layer] Indexes batch views for faster queries.
+    \item[Speed layer] Receives the data and prepares real-time views. The views are also stored in the serving layer.
+\end{description}
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.5\textwidth]{img/lambda_lake.png}
+    \caption{Lambda lake architecture}
+\end{figure}
+
+\subsection{Kappa lake} 
+\marginnote{Kappa lake}
+The data are stored in a long-term store.
+Computations only happen in the speed layer (avoids lambda lake redundancy between batch layer and speed layer).
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.5\textwidth]{img/kappa_lake.png}
+    \caption{Kappa lake architecture}
+\end{figure}
+
+\subsection{Delta lake} 
+\marginnote{Delta lake}
+Framework that adds features on top of an existing data lake.
+\begin{itemize}
+    \item ACID transactions
+    \item Scalable metadata handling
+    \item Data versioning
+    \item Unified batch and streaming
+    \item Schema enforcement
+\end{itemize}
+\begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.7\textwidth]{img/delta_lake.png}
+    \caption{Delta lake architecture}
+\end{figure}
+
+
+\section{Metadata}
+\marginnote{Metadata}
+Metadata are used to organize a data lake.
+Useful metadata are:
+\begin{descriptionlist}
+    \item[Source] Origin of the data.
+    \item[Schema] Structure of the data.
+    \item[Format] File format or encoding.
+    \item[Quality metrics] (e.g. percentage of missing values).
+    \item[Lifecycle] Retention policies and archiving rules.
+    \item[Ownership] 
+    \item[Lineage] History of applied transformations or dependencies.
+    \item[Access control] 
+    \item[Classification] Sensitivity level of the data.
+    \item[Usage information] Record of who accessed the data and how it is used.
+\end{descriptionlist}
\ No newline at end of file
diff --git a/src/machine-learning-and-data-mining/sections/_bi.tex b/src/machine-learning-and-data-mining/sections/_data_warehouse.tex
similarity index 61%
rename from src/machine-learning-and-data-mining/sections/_bi.tex
rename to src/machine-learning-and-data-mining/sections/_data_warehouse.tex
index fd52e63..77a7379 100644
--- a/src/machine-learning-and-data-mining/sections/_bi.tex
+++ b/src/machine-learning-and-data-mining/sections/_data_warehouse.tex
@@ -1,4 +1,4 @@
-\chapter{Business Intelligence}
+\chapter{Data warehouse}
 
 
 \begin{description}
@@ -23,10 +23,10 @@
 
 
 
-\section{\Acl{olap} (\Ac{olap})}
+\section{\Acl{olap} (\Acs{olap})}
 
 \begin{description}
-    \item[\ac{olap} analyses] \marginnote{\Acl{olap} (\Ac{olap})}
+    \item[\ac{olap} analyses] \marginnote{\Acl{olap} (\Acs{olap})}
         Able to interactively navigate the information in a data warehouse.
         Allows to visualize different levels of aggregation.
 
@@ -352,215 +352,4 @@ There are mainly two strategies:
             \includegraphics[width=\textwidth]{img/logical_snowflake_schema.png}
             \caption{Example of snowflake schema}
         \end{figure}
-\end{descriptionlist}
-
-
-
-\section{Data lake}
-\begin{description}
-    \item[Dark data] \marginnote{Dark data}
-        Acquired and stored data that are never used for decision-making processes.
-
-    \item[Data lake] \marginnote{Data lake}
-        Repository to store raw (unstructured) data.
-        It has the following features:
-        \begin{itemize}
-            \item Does not enforce a schema on write.
-            \item Allows flexible access and applies schemas on read.
-            \item Single source of truth.
-            \item Low cost and scalable.
-        \end{itemize}
-
-    \item[Storage]
-        Stored data can be classified as:
-        \begin{descriptionlist}
-            \item[Hot] \marginnote{Hot storage}
-                A low volume of highly requested data that require low latency.
-                More expensive HW/SW.
-            \item[Cold] \marginnote{Cold storage}
-                A large amount of data that does not have latency requirements.
-                Less expensive.
-        \end{descriptionlist}
-
-        \begin{figure}[ht]
-            \centering
-            \includegraphics[width=0.5\textwidth]{img/_storage.pdf}
-            \caption{Data storage technologies}
-        \end{figure}
-\end{description}
-
-
-\subsection{Traditional vs insight-driven data systems}
-\begin{tabular}{c | p{0.4\textwidth} | p{0.4\textwidth}}
-    & \textbf{\makecell[c]{Traditional (data warehouse)}} & \textbf{\makecell[c]{Insight-driven (data lake)}} \\
-    \hline
-    \textbf{Sources} & Structured data & Structured, semi-structured and unstructured data \\
-    \hline
-    \textbf{Storage} & Limited ingestion and storage capability & Virtually unlimited ingestion and storage capability \\
-    \hline
-    \textbf{Schema} & Schema designed upfront & Schema not fixed \\
-    \hline
-    \textbf{Transformations} & \ac{etl} upfront & Transformations on query \\
-    \hline
-    \textbf{Analytics} & SQL, \ac{bi} tools, full-text search & Traditional methods, self-service \ac{bi}, big data, machine learning, \dots \\
-    \hline
-    \textbf{Price} & High storage cost & Low storage cost \\
-    \textbf{Performance} & Fast queries & Scalability/speed/cost tradeoffs \\
-    \hline
-    \textbf{Quality} & High data quality & Depends on the use case \\
-\end{tabular}
-
-
-\subsection{Data architecture evolution}
-\begin{description}
-    \item[Traditional data warehouse] \marginnote{Traditional data warehouse} 
-        (i.e. in-house data warehouse)
-        \begin{itemize}
-            \item Structured data with predefined schemas.
-            \item High setup and maintenance cost. Not scalable.
-            \item Relational high-quality data.
-            \item Slow data ingestion.
-        \end{itemize}
-
-    \item[Modern cloud data warehouse] \marginnote{Modern cloud data warehouse} 
-        \phantom{}
-        \begin{itemize}
-            \item Structured and semi-structured data.
-            \item Low setup and maintenance cost. Scalable and easier disaster recovery.
-            \item Relational high-quality data and mixed data.
-            \item Fast data ingestion if supported.
-        \end{itemize}
-
-    \item[On-premise big data] \marginnote{On-premise big data} 
-        (i.e. in-house data lake)
-        \begin{itemize}
-            \item Any type of data with schemas on read.
-            \item High setup and maintenance cost.
-            \item Fast data ingestion.
-        \end{itemize}
-
-    \item[Cloud data lake] \marginnote{Cloud data lake} 
-        \phantom{}
-        \begin{itemize}
-            \item Any type of data with schemas on read.
-            \item Low setup and maintenance cost. Scalable and easier disaster recovery.
-            \item Fast data ingestion.
-        \end{itemize}
-\end{description}
-
-
-\subsection{Components}
-\begin{description}
-    \item[Data ingestion] \marginnote{Data ingestion}
-        \phantom{}
-        \begin{descriptionlist}
-            \item[Workload migration]
-                Inserting all the data from an existing source.
-            \item[Incremental ingestion]
-                Inserting changes since the last ingestion.
-            \item[Streaming ingestion]   
-                Continuously inserting data.
-        \end{descriptionlist}
-
-        \begin{description}
-            \item[\Acl{cdc} (\Acs{cdc})] \marginnote{\Acl{cdc} (\Acs{cdc})}
-                Mechanism to detect changes and insert the new data into the data lake (possibly in real-time).
-        \end{description}
-
-    \item[Storage]
-        \phantom{}
-        \begin{descriptionlist}
-            \item[Raw] \marginnote{Raw storage}
-                Immutable data useful for disaster recovery.
-            \item[Optimized] \marginnote{Optimized storage}
-                Optimized raw data for faster query.
-            \item[Analytics] \marginnote{Analytics storage}
-                Ready to use data.
-        \end{descriptionlist}
-
-        \begin{description}
-            \item[Columnar storage] \phantom{}
-                \begin{itemize}
-                    \item Homogenous data are stores contiguously.
-                    \item Speeds up methods that process entire columns (i.e. all the values of a feature).
-                    \item Insertion becomes slower.
-                \end{itemize}
-
-            \item[Data catalog]
-                Methods to add descriptive metadata to a data lake.
-                This is useful to prevent an unorganized data lake (data swamp).
-        \end{description}
-        
-    
-    \item[Processing and analytics] \marginnote{Processing and analytics}
-        \phantom{}
-        \begin{descriptionlist}
-            \item[Interactive analytics]
-                Interactive queries to large volumes of data.
-                The results are stored back in the data lake.
-            \item[Big data analytics]
-                Data aggregations and transformations.
-            \item[Real-time analytics]   
-                Streaming analysis.
-        \end{descriptionlist}
-\end{description}
-
-
-\subsection{Architectures}
-
-\begin{description}
-    \item[Lambda lake] \marginnote{Lambda lake}
-        \phantom{}
-        \begin{description}
-            \item[Batch layer] Receives and stores the data. Prepares the batch views for the serving layer.
-            \item[Serving layer] Indexes batch views for faster queries.
-            \item[Speed layer] Receives the data and prepares real-time views. The views are also stored in the serving layer.
-        \end{description}
-        \begin{figure}[ht]
-            \centering
-            \includegraphics[width=0.5\textwidth]{img/lambda_lake.png}
-            \caption{Lambda lake architecture}
-        \end{figure}
-
-    \item[Kappa lake] \marginnote{Kappa lake}
-        The data are stored in a long-term store.
-        Computations only happen in the speed layer (avoids lambda lake redundancy between batch layer and speed layer).
-        \begin{figure}[ht]
-            \centering
-            \includegraphics[width=0.5\textwidth]{img/kappa_lake.png}
-            \caption{Kappa lake architecture}
-        \end{figure}
-
-    \item[Delta lake] \marginnote{Delta lake}
-        Framework that adds features on top of an existing data lake.
-        \begin{itemize}
-            \item ACID transactions
-            \item Scalable metadata handling
-            \item Data versioning
-            \item Unified batch and streaming
-            \item Schema enforcement
-        \end{itemize}
-        \begin{figure}[ht]
-            \centering
-            \includegraphics[width=0.7\textwidth]{img/delta_lake.png}
-            \caption{Delta lake architecture}
-        \end{figure}
-\end{description}
-
-
-\subsection{Metadata}
-\marginnote{Metadata}
-Metadata are used to organize a data lake.
-Useful metadata are:
-\begin{descriptionlist}
-    \item[Source] Origin of the data.
-    \item[Schema] Structure of the data.
-    \item[Format] File format or encoding.
-    \item[Quality metrics] (e.g. percentage of missing values).
-    \item[Lifecycle] Retention policies and archiving rules.
-    \item[Ownership] 
-    \item[Lineage] History of applied transformations or dependencies.
-    \item[Access control] 
-    \item[Classification] Sensitivity level of the data.
-    \item[Usage information] Record of who accessed the data and how it is used.
 \end{descriptionlist}
\ No newline at end of file