Add practical examples to multiple files
- LF9-03 Virtualisierung: Docker Compose + Volume examples - LF6-02 Frontend: To-Do list practical example - LF8-04 ETL: Complete ETL pipeline example - LF6-04 Sicherheit: Express.js security headers - LF2-04 Nutzwertanalyse: Cloud provider selection example - LF9-04 Monitoring: Prometheus alerts + Python logging
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@@ -80,6 +80,73 @@ Laptop B:
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---
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## Praktisches Beispiel: Cloud-Anbieter Auswahl
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### Szenario
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Ein Unternehmen muss sich zwischen AWS, Azure und Google Cloud entscheiden.
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### 1. Kriterien definieren
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| Kriterium | Bedeutung |
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|-----------|-----------|
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| Preis | Kosten pro Monat |
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| Leistung | Rechenleistung, Geschwindigkeit |
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| Sicherheit | Zertifizierungen, Features |
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| Support | Deutsche Ansprechpartner |
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| Skalierbarkeit | Einfache Erweiterung |
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### 2. Gewichtung
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| Kriterium | Gewichtung |
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|-----------|------------|
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| Preis | 25% |
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| Leistung | 30% |
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| Sicherheit | 25% |
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| Support | 10% |
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| Skalierbarkeit | 10% |
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| **Summe** | **100%** |
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### 3. Bewertung (1-10)
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| Kriterium | Gewichtung | AWS | Azure | Google Cloud |
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|-----------|------------|-----|-------|--------------|
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| Preis | 25% | 7 | 8 | 9 |
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| Leistung | 30% | 9 | 8 | 9 |
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| Sicherheit | 25% | 9 | 8 | 8 |
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| Support | 10% | 7 | 8 | 6 |
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| Skalierbarkeit | 10% | 9 | 8 | 9 |
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### 4. Berechnung
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```
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AWS:
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= 0,25×7 + 0,30×9 + 0,25×9 + 0,10×7 + 0,10×9
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= 1,75 + 2,70 + 2,25 + 0,70 + 0,90
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= 8,30
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Azure:
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= 0,25×8 + 0,30×8 + 0,25×8 + 0,10×8 + 0,10×8
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= 2,00 + 2,40 + 2,00 + 0,80 + 0,80
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= 8,00
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Google Cloud:
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= 0,25×9 + 0,30×9 + 0,25×8 + 0,10×6 + 0,10×9
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= 2,25 + 2,70 + 2,00 + 0,60 + 0,90
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= 8,45
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```
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### 5. Ergebnis
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| Anbieter | Nutzwert | Rang |
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|----------|----------|------|
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| Google Cloud | 8,45 | 1 |
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| AWS | 8,30 | 2 |
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| Azure | 8,00 | 3 |
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**Empfehlung:** Google Cloud (aufgrund des besten Preis-Leistungs-Verhältnisses)
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---
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## Vorlage
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### Nutzwertanalyse - Vorlage
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@@ -6,13 +6,13 @@ Dieses Lernfeld behandelt die Entwicklung und Verwaltung von Datenbanken.
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## Themen
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| Nr. | Thema | Beschreibung |
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|-----|-------|-------------|
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| 3.1 | [[LF3-01-Datenbankgrundlagen|Datenbankgrundlagen]] | Grundbegriffe, DBMS, Datenmodelle |
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| 3.2 | [[LF3-02-Datenmodellierung|Datenmodellierung]] | ER-Modell, Normalisierung |
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| 3.3 | [[LF3-03-SQL-Grundlagen|SQL-Grundlagen]] | SELECT, INSERT, UPDATE, DELETE |
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| 3.4 | [[LF3-04-SQL-Abfragen|SQL-Abfragen]] | JOINs, Aggregatfunktionen, Unterabfragen |
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| 3.5 | [[LF3-05-Datenbankmanagement|Datenbankmanagement]] | Rechte, Sicherheit, Backup |
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| Nr. | Thema | Beschreibung | |
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| --- | ---------------------------- | --------------------- | ---------------------------------------- |
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| 3.1 | [[LF3-01-Datenbankgrundlagen | Datenbankgrundlagen]] | Grundbegriffe, DBMS, Datenmodelle |
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| 3.2 | [[LF3-02-Datenmodellierung | Datenmodellierung]] | ER-Modell, Normalisierung |
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| 3.3 | [[LF3-03-SQL-Grundlagen | SQL-Grundlagen]] | SELECT, INSERT, UPDATE, DELETE |
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| 3.4 | [[LF3-04-SQL-Abfragen | SQL-Abfragen]] | JOINs, Aggregatfunktionen, Unterabfragen |
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| 3.5 | [[LF3-05-Datenbankmanagement | Datenbankmanagement]] | Rechte, Sicherheit, Backup |
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## Lernziele
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@@ -99,6 +99,45 @@ element.setAttribute("class", "neu");
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element.getAttribute("href");
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```
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### Praktisches Beispiel: To-Do-Liste
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```html
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<!-- HTML -->
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<input type="text" id="todo-input" placeholder="Neue Aufgabe">
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<button id="add-btn">Hinzufügen</button>
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<ul id="todo-list"></ul>
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```
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```javascript
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// JavaScript
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const input = document.getElementById('todo-input');
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const button = document.getElementById('add-btn');
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const list = document.getElementById('todo-list');
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button.addEventListener('click', () => {
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const text = input.value.trim();
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if (text) {
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// Neues Element erstellen
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const li = document.createElement('li');
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li.textContent = text;
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// Löschen-Button
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const deleteBtn = document.createElement('button');
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deleteBtn.textContent = 'X';
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deleteBtn.onclick = () => li.remove();
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li.appendChild(deleteBtn);
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list.appendChild(li);
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input.value = '';
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}
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});
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// Enter-Taste Support
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input.addEventListener('keypress', (e) => {
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if (e.key === 'Enter') button.click();
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});
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```
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### Events
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```javascript
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@@ -80,6 +80,29 @@ function escapeHtml(text) {
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Content-Security-Policy: default-src 'self'; script-src 'self'
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```
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### Praktisches Beispiel: Express.js Sicherheits-Header
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```javascript
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const helmet = require('helmet');
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const cors = require('cors');
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app.use(helmet());
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// CORS konfigurieren
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app.use(cors({
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origin: 'https://meine-app.de',
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credentials: true
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}));
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// Rate Limiting
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const rateLimit = require('express-rate-limit');
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app.use('/api/', rateLimit({
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windowMs: 15 * 60 * 1000, // 15 Minuten
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max: 100, // Max 100 Anfragen
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message: 'Zu viele Anfragen, bitte später versuchen'
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}));
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```
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---
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## CSRF (Cross-Site Request Forgery)
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@@ -207,6 +207,81 @@ ETL - Fehlerstrategien
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---
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## Praktisches Beispiel: Vollständiger ETL-Pipeline
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```python
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import pandas as pd
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import requests
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from sqlalchemy import create_engine
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import logging
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# Logging konfigurieren
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def etl_pipeline():
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"""
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Vollständiger ETL-Pipeline für Verkaufsdaten
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"""
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# === EXTRACT ===
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logger.info("Starte Extraktion...")
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# Aus CSV
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kunden_df = pd.read_csv('daten/kunden.csv')
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bestellungen_df = pd.read_csv('daten/bestellungen.csv')
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# Aus API
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try:
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response = requests.get('https://api.shop.de/produkte', timeout=30)
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produkte_df = pd.DataFrame(response.json())
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except Exception as e:
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logger.error(f"API Fehler: {e}")
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produkte_df = pd.DataFrame()
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logger.info(f"Extrahiert: {len(kunden_df)} Kunden, {len(bestellungen_df)} Bestellungen")
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# === TRANSFORM ===
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logger.info("Starte Transformation...")
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# Daten bereinigen
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kunden_df = kunden_df.drop_duplicates()
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kunden_df['email'] = kunden_df['email'].str.lower().str.strip()
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kunden_df['erstellt_am'] = pd.to_datetime(kunden_df['erstellt_am'])
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# Berechnungen
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bestellungen_df['umsatz_mit_mwst'] = bestellungen_df['umsatz_netto'] * 1.19
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# JOIN: Bestellungen mit Kunden verbinden
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merged_df = bestellungen_df.merge(
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kunden_df[['kunden_id', 'name', 'stadt']],
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on='kunden_id',
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how='left'
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)
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# Aggregation: Umsatz pro Stadt
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umsatz_pro_stadt = merged_df.groupby('stadt')['umsatz_netto'].sum().reset_index()
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logger.info(f"Transformation abgeschlossen: {len(merged_df)} Datensätze")
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# === LOAD ===
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logger.info("Starte Laden...")
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# Datenbank-Verbindung
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engine = create_engine('postgresql://user:pass@localhost:5432/warehouse')
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# In Datenbank laden
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merged_df.to_sql('fact_bestellungen', engine, if_exists='replace', index=False)
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umsatz_pro_stadt.to_sql('dim_umsatz_stadt', engine, if_exists='replace', index=False)
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logger.info("ETL Pipeline erfolgreich abgeschlossen!")
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if __name__ == '__main__':
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etl_pipeline()
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```
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---
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## Querverweise
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- [[LF8-03-Datenformate|Zurück: Datenformate]]
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@@ -108,6 +108,59 @@ EXPOSE 3000
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CMD ["node", "server.js"]
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```
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### Praktisches Beispiel: Docker Compose
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```yaml
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# docker-compose.yml
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version: '3.8'
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services:
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# Webanwendung
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app:
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build: .
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ports:
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- "3000:3000"
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environment:
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- DATABASE_URL=postgres://db:5432/webapp
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depends_on:
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- db
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- redis
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# Datenbank
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db:
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image: postgres:15-alpine
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volumes:
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- db_data:/var/lib/postgresql/data
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environment:
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- POSTGRES_PASSWORD=geheim
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- POSTGRES_DB=webapp
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# Cache
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redis:
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image: redis:7-alpine
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ports:
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- "6379:6379"
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volumes:
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db_data:
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```
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### Praktisches Beispiel: Docker Volume
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```bash
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# Volume erstellen
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docker volume create mydata
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# Volume einhängen
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docker run -v mydata:/data ubuntu
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# Volumes auflisten
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docker volume ls
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# Unbenutzte Volumes löschen
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docker volume prune
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```
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---
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## Kubernetes
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@@ -61,6 +61,73 @@ scrape_configs:
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- targets: ['localhost:9100']
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```
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### Praktisches Beispiel: Alert-Regeln
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```yaml
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# alerts.yml
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groups:
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- name: server_alerts
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rules:
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# Hohe CPU-Auslastung
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- alert: HighCPU
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expr: 100 - (avg by (instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
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for: 5m
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labels:
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severity: warning
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annotations:
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summary: "Hohe CPU-Auslastung auf {{ $labels.instance }}"
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description: "CPU Auslastung ist seit 5 Minuten über 80%"
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# Wenig Speicherplatz
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- alert: DiskSpaceLow
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expr: (node_filesystem_avail_bytes{mountpoint="/"} / node_filesystem_size_bytes{mountpoint="/"}) * 100 < 10
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for: 2m
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labels:
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severity: critical
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annotations:
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summary: "Wenig Speicherplatz auf {{ $labels.instance }}"
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# Service ausgefallen
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- alert: ServiceDown
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expr: up == 0
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for: 1m
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labels:
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severity: critical
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annotations:
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summary: "{{ $labels.job }} Service ausgefallen"
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```
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### Praktisches Beispiel: Python Logging
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```python
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import logging
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import logging.handlers
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import sys
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# Logger konfigurieren
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
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handlers=[
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logging.StreamHandler(sys.stdout),
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logging.handlers.RotatingFileHandler(
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'app.log',
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maxBytes=10_000_000, # 10 MB
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backupCount=5
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)
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]
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)
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logger = logging.getLogger(__name__)
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# Log-Ebenen nutzen
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logger.debug("Detaillierte Debug-Info")
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logger.info("Anwendung gestartet")
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logger.warning("Warnung: Konfiguration fehlt")
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logger.error("Fehler: Datenbank nicht erreichbar")
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logger.critical("Kritisch: System muss heruntergefahren werden")
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```
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### Grafana Dashboard
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```
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Reference in New Issue
Block a user