Add ML/DM data lake

src/machine-learning-and-data-mining/img/storage.drawio

@@ -0,0 +1,31 @@
+<mxfile host="app.diagrams.net" modified="2023-10-13T17:44:38.951Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/118.0" etag="0k6DN-mG6fDlB8POdY3R" version="22.0.4" type="device">
+  <diagram name="Pagina-1" id="Obl2eNAEIfPRNowj_f7H">
+    <mxGraphModel dx="1195" dy="622" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-1" value="" style="endArrow=classic;html=1;rounded=0;strokeWidth=2;startArrow=classic;startFill=1;fontSize=20;" edge="1" parent="1">
+          <mxGeometry width="50" height="50" relative="1" as="geometry">
+            <mxPoint x="180" y="400" as="sourcePoint" />
+            <mxPoint x="680" y="400" as="targetPoint" />
+          </mxGeometry>
+        </mxCell>
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-2" value="Data warehouse" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=20;" vertex="1" parent="1">
+          <mxGeometry x="180" y="360" width="150" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-3" value="&lt;div align=&quot;right&quot; style=&quot;font-size: 20px;&quot;&gt;Data lake&lt;br style=&quot;font-size: 20px;&quot;&gt;&lt;/div&gt;" style="text;html=1;strokeColor=none;fillColor=none;align=right;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=20;" vertex="1" parent="1">
+          <mxGeometry x="530" y="360" width="150" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-4" value="Data hub" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=20;" vertex="1" parent="1">
+          <mxGeometry x="360" y="360" width="150" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-5" value="Hot" style="text;html=1;strokeColor=none;fillColor=none;align=left;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=20;" vertex="1" parent="1">
+          <mxGeometry x="180" y="410" width="60" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="j0uoPLtJFFh1yWsyPPyp-6" value="Cold" style="text;html=1;strokeColor=none;fillColor=none;align=right;verticalAlign=middle;whiteSpace=wrap;rounded=0;fontSize=20;" vertex="1" parent="1">
+          <mxGeometry x="620" y="410" width="60" height="30" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+</mxfile>

src/machine-learning-and-data-mining/main.tex

@@ -14,6 +14,7 @@
 \DeclareAcronym{dm}{short=DM, long=Data Mart}
 \DeclareAcronym{etl}{short=ETL, long=Extraction{,} Transformation{,} Loading}
 \DeclareAcronym{dfm}{short=DFM, long=Dimensional Fact Model}
+\DeclareAcronym{cdc}{short=CDC, long=Change Data Capture}
 
 
 \begin{document}

src/machine-learning-and-data-mining/sections/_bi.tex

@@ -107,8 +107,8 @@
 
 
 
-\section{\Acl{etl} (\Ac{etl})}
-\marginnote{\Acl{etl} (\Ac{etl})}
+\section{\Acl{etl} (\Acs{etl})}
+\marginnote{\Acl{etl} (\Acs{etl})}
 The \Ac{etl} process extracts, integrates and cleans operational data that will be loaded into a data warehouse.
 
 
@@ -353,3 +353,214 @@ There are mainly two strategies:
             \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}