chore: remove placeholder Azure workflows (no Infomaniak VM yet)

Co-authored-by: Cursor <cursoragent@cursor.com>

.github/workflows/main_poweron-preprocessing.yml

@@ -1,63 +0,0 @@
-name: Build and deploy Python app to Azure Web App - poweron-preprocessing
-
-on:
-  push:
-    branches: [main]
-  workflow_dispatch:
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      # ---------- BACKEND / PYTHON ----------
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.13"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v6
-
-      # ---------- ARTIFACT ----------
-      - name: Upload artifact for deployment jobs
-        uses: actions/upload-artifact@v4
-        with:
-          name: python-app
-          path: |
-            .
-            !venv/
-            !.venv/
-            !.git/
-            !.cache/
-
-  deploy:
-    runs-on: ubuntu-latest
-    needs: build
-    permissions:
-      id-token: write
-      contents: read
-
-    steps:
-      - name: Download artifact from build job
-        uses: actions/download-artifact@v4
-        with:
-          name: python-app
-
-      - name: Login to Azure (OIDC)
-        uses: azure/login@v2
-        with:
-          client-id: ${{ secrets.AZUREAPPSERVICE_CLIENTID_AA4B9998A69E4C5C8FDF357E3FEAADD5 }}
-          tenant-id: ${{ secrets.AZUREAPPSERVICE_TENANTID_CC57AD1F29D44DDA960AE3EAC6D2C27A }}
-          subscription-id: ${{ secrets.AZUREAPPSERVICE_SUBSCRIPTIONID_4CD1D97C506D403E8E284466DB4E7898 }}
-
-      - name: Deploy to Azure Web App
-        uses: azure/webapps-deploy@v3
-        with:
-          app-name: "poweron-preprocessing"
-          slot-name: "Production"
-          package: . # reuse the downloaded artifact folder

README.md

@@ -0,0 +1,667 @@
+# Gateway Preprocessing - Datenschnittstellen (Datenmodell)
+
+## Übersicht
+
+Dieses Projekt bietet zwei Hauptservices mit klar definierten Datenschnittstellen:
+
+1. **Data Processor Service** (`/dataprocessor`) - Lädt Daten aus Power BI, verarbeitet sie und speichert sie in SQLite
+2. **Data Query Service** (`/dataquery`) - Ermöglicht SQL-Abfragen auf die gespeicherten Daten
+
+```mermaid
+graph LR
+    A[Power BI Dataset] -->|Data Processor| B[SQLite Database]
+    B -->|Data Query| C[Client/API Consumer]
+    
+    style A fill:#f9f,stroke:#333,stroke-width:2px
+    style B fill:#bbf,stroke:#333,stroke-width:2px
+    style C fill:#bfb,stroke:#333,stroke-width:2px
+```
+
+---
+
+## Data Processor Service
+
+Der Data Processor Service verarbeitet Daten aus Power BI und speichert sie in einer lokalen SQLite-Datenbank. Die Schemas befinden sich in `src/dataprocessor/schemas.py`.
+
+### Datenmodell-Hierarchie
+
+```mermaid
+classDiagram
+    class PreprocessingConfigSchema {
+        +List~TableConfigSchema~ tables
+    }
+    
+    class TableConfigSchema {
+        +str name
+        +str powerbi_table_name
+        +List~str~ measures
+        +List~str~ group_by_columns
+        +List~Dict~ steps
+    }
+    
+    class UpdateDbWithConfigResponse {
+        +bool success
+        +int tables_processed
+        +List~str~ warnings
+    }
+    
+    class UpdateDbResponse {
+        +bool success
+    }
+    
+    PreprocessingConfigSchema "1" *-- "1..*" TableConfigSchema : enthält
+```
+
+### 1. TableConfigSchema
+
+Definiert die Konfiguration für eine einzelne Tabelle, die aus Power BI gelesen und verarbeitet werden soll.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung | Beispiel |
+|------|-----|--------------|----------|
+| `name` | `str` | Name der Tabelle in der lokalen SQLite-Datenbank | `"ProductData"` |
+| `powerbi_table_name` | `str` | Name der Quelltabelle im Power BI Dataset | `"products_raw"` |
+| `measures` | `List[str]` | Liste von Power BI Measures, die abgerufen werden sollen (optional) | `["EP in CHF", "Gesamtbetrag"]` |
+| `group_by_columns` | `List[str]` | Spalten für Gruppierung bei Measures (triggert SUMMARIZECOLUMNS) | `["m_Artikel", "Kategorie"]` |
+| `steps` | `List[Dict[str, Any]]` | Liste von Preprocessing-Schritten | siehe unten |
+
+**Unterstützte Preprocessing-Schritte:**
+
+```mermaid
+graph TD
+    A[Preprocessing Steps] --> B[keep]
+    A --> C[fillna]
+    A --> D[to_numeric]
+    A --> E[dropna]
+    
+    B --> B1["columns: List[str]<br/>Behält nur angegebene Spalten"]
+    C --> C1["column: str, value: Any<br/>Füllt fehlende Werte"]
+    D --> D1["column: str, errors: str<br/>Konvertiert zu numerisch"]
+    E --> E1["subset: List[str]<br/>Entfernt Zeilen mit NaN"]
+    
+    style A fill:#f96,stroke:#333,stroke-width:3px
+    style B fill:#9cf,stroke:#333,stroke-width:2px
+    style C fill:#9cf,stroke:#333,stroke-width:2px
+    style D fill:#9cf,stroke:#333,stroke-width:2px
+    style E fill:#9cf,stroke:#333,stroke-width:2px
+```
+
+**Beispiel:**
+
+```json
+{
+  "name": "ProductData",
+  "powerbi_table_name": "products_raw",
+  "measures": ["Total Sales", "Average Price"],
+  "group_by_columns": ["Category", "Supplier"],
+  "steps": [
+    {
+      "keep": {
+        "columns": ["ProductID", "ProductName", "Supplier", "Stock", "Unit", "Price"]
+      }
+    },
+    {
+      "fillna": {
+        "column": "Supplier",
+        "value": "Unknown"
+      }
+    },
+    {
+      "to_numeric": {
+        "column": "Price",
+        "errors": "coerce"
+      }
+    },
+    {
+      "dropna": {
+        "subset": ["ProductID", "ProductName", "Price"]
+      }
+    }
+  ]
+}
+```
+
+### 2. PreprocessingConfigSchema
+
+Komplette Konfiguration für einen Preprocessing-Request. Kann mehrere Tabellen gleichzeitig verarbeiten.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung | Validierung |
+|------|-----|--------------|-------------|
+| `tables` | `List[TableConfigSchema]` | Liste von Tabellenkonfigurationen | min. 1 Tabelle |
+
+**Vollständiges Request-Beispiel:**
+
+```json
+{
+  "tables": [
+    {
+      "name": "ProductData",
+      "powerbi_table_name": "products_raw",
+      "steps": [
+        {
+          "keep": {
+            "columns": ["ProductID", "ProductName", "Supplier", "Stock", "Unit", "Price"]
+          }
+        },
+        {
+          "fillna": {
+            "column": "Supplier",
+            "value": "Unknown"
+          }
+        },
+        {
+          "to_numeric": {
+            "column": "Price",
+            "errors": "coerce"
+          }
+        },
+        {
+          "dropna": {
+            "subset": ["ProductID", "ProductName", "Stock", "Unit", "Price"]
+          }
+        }
+      ]
+    },
+    {
+      "name": "CustomerData",
+      "powerbi_table_name": "customers_raw",
+      "steps": [
+        {
+          "keep": {
+            "columns": ["CustomerID", "Name", "Email", "Region"]
+          }
+        },
+        {
+          "fillna": {
+            "column": "Region",
+            "value": "Unknown"
+          }
+        }
+      ]
+    }
+  ]
+}
+```
+
+### 3. UpdateDbResponse
+
+Einfache Response für Standard-DB-Updates (YAML-basiert).
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `success` | `bool` | Gibt an, ob das Update erfolgreich war |
+
+**Beispiel:**
+
+```json
+{
+  "success": true
+}
+```
+
+### 4. UpdateDbWithConfigResponse
+
+Detaillierte Response für JSON-basierte DB-Updates mit Konfiguration.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `success` | `bool` | Gibt an, ob das Update erfolgreich war |
+| `tables_processed` | `int` | Anzahl der erfolgreich verarbeiteten Tabellen |
+| `warnings` | `List[str]` | Liste von Warnungen während der Verarbeitung |
+
+**Beispiel:**
+
+```json
+{
+  "success": true,
+  "tables_processed": 2,
+  "warnings": [
+    "Table 'ProductData': 3 rows dropped due to missing values",
+    "Table 'ProductData': Column 'Price' had 5 non-numeric values coerced to NaN"
+  ]
+}
+```
+
+### Preprocessing-Workflow
+
+```mermaid
+sequenceDiagram
+    participant Client
+    participant API
+    participant PowerBI
+    participant Preprocessor
+    participant SQLite
+
+    Client->>API: POST /dataprocessor/update-db-with-config
+    Note over Client,API: PreprocessingConfigSchema
+    
+    loop Für jede Tabelle
+        API->>PowerBI: Daten abrufen (powerbi_table_name)
+        PowerBI-->>API: Raw DataFrame
+        API->>Preprocessor: Preprocessing-Steps anwenden
+        Preprocessor->>Preprocessor: keep → fillna → to_numeric → dropna
+        Preprocessor-->>API: Verarbeiteter DataFrame
+        API->>SQLite: Tabelle speichern (name)
+    end
+    
+    API-->>Client: UpdateDbWithConfigResponse
+    Note over Client,API: success, tables_processed, warnings
+```
+
+---
+
+## Data Query Service
+
+Der Data Query Service ermöglicht SQL-Abfragen auf die gespeicherte SQLite-Datenbank. Die Schemas befinden sich in `src/dataquery/schemas.py`.
+
+### Datenmodell-Hierarchie
+
+```mermaid
+classDiagram
+    class SqlQueryRequest {
+        +str query
+    }
+    
+    class SqlQueryResponse {
+        +bool success
+        +List~Dict~ data
+        +List~str~ columns
+        +int row_count
+        +Optional~str~ message
+    }
+    
+    class DatabaseSchemaResponse {
+        +bool success
+        +List~TableInfo~ tables
+        +int table_count
+    }
+    
+    class TableInfo {
+        +str name
+        +int row_count
+    }
+    
+    class TableSchemaResponse {
+        +bool success
+        +str table_name
+        +List~ColumnInfo~ columns
+        +int row_count
+        +List~Dict~ sample_data
+    }
+    
+    class ColumnInfo {
+        +str name
+        +str type
+        +bool nullable
+        +bool primary_key
+    }
+    
+    DatabaseSchemaResponse "1" *-- "*" TableInfo : enthält
+    TableSchemaResponse "1" *-- "*" ColumnInfo : enthält
+```
+
+### 1. SqlQueryRequest
+
+Request-Schema für SQL-Abfragen.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung | Validierung |
+|------|-----|--------------|-------------|
+| `query` | `str` | SQL-Query (nur SELECT erlaubt) | min. 1 Zeichen |
+
+**Beispiel:**
+
+```json
+{
+  "query": "SELECT ProductID, ProductName, Price FROM ProductData WHERE Price > 10 ORDER BY Price DESC LIMIT 100"
+}
+```
+
+### 2. SqlQueryResponse
+
+Response-Schema für SQL-Query-Ergebnisse.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `success` | `bool` | Gibt an, ob die Query erfolgreich ausgeführt wurde |
+| `data` | `List[Dict[str, Any]]` | Query-Ergebnisse als Liste von Dictionaries |
+| `columns` | `List[str]` | Spaltennamen im Ergebnis |
+| `row_count` | `int` | Anzahl der zurückgegebenen Zeilen |
+| `message` | `Optional[str]` | Zusätzliche Informationen oder Fehlermeldung |
+
+**Beispiel:**
+
+```json
+{
+  "success": true,
+  "columns": ["ProductID", "ProductName", "Price"],
+  "row_count": 3,
+  "data": [
+    {
+      "ProductID": 42,
+      "ProductName": "Premium Widget",
+      "Price": 99.99
+    },
+    {
+      "ProductID": 15,
+      "ProductName": "Deluxe Gadget",
+      "Price": 79.50
+    },
+    {
+      "ProductID": 8,
+      "ProductName": "Standard Tool",
+      "Price": 45.00
+    }
+  ],
+  "message": null
+}
+```
+
+### 3. TableInfo
+
+Informationen über eine Datenbanktabelle.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `name` | `str` | Tabellenname |
+| `row_count` | `int` | Anzahl der Zeilen in der Tabelle |
+
+### 4. DatabaseSchemaResponse
+
+Response-Schema für Datenbank-Schema-Informationen.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `success` | `bool` | Gibt an, ob die Schema-Abfrage erfolgreich war |
+| `tables` | `List[TableInfo]` | Liste aller Tabellen in der Datenbank |
+| `table_count` | `int` | Gesamtanzahl der Tabellen |
+
+**Beispiel:**
+
+```json
+{
+  "success": true,
+  "tables": [
+    {
+      "name": "ProductData",
+      "row_count": 1523
+    },
+    {
+      "name": "CustomerData",
+      "row_count": 847
+    },
+    {
+      "name": "OrderData",
+      "row_count": 3421
+    }
+  ],
+  "table_count": 3
+}
+```
+
+### 5. ColumnInfo
+
+Informationen über eine Tabellenspalte.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `name` | `str` | Spaltenname |
+| `type` | `str` | Datentyp der Spalte |
+| `nullable` | `bool` | Ob die Spalte NULL-Werte enthalten kann |
+| `primary_key` | `bool` | Ob die Spalte ein Primary Key ist |
+
+### 6. TableSchemaResponse
+
+Response-Schema für detaillierte Tabellenstruktur-Informationen.
+
+**Felder:**
+
+| Feld | Typ | Beschreibung |
+|------|-----|--------------|
+| `success` | `bool` | Gibt an, ob die Schema-Abfrage erfolgreich war |
+| `table_name` | `str` | Name der Tabelle |
+| `columns` | `List[ColumnInfo]` | Liste aller Spalten in der Tabelle |
+| `row_count` | `int` | Anzahl der Zeilen in der Tabelle |
+| `sample_data` | `List[Dict[str, Any]]` | Beispieldaten (bis zu 5 Zeilen) |
+
+**Beispiel:**
+
+```json
+{
+  "success": true,
+  "table_name": "ProductData",
+  "columns": [
+    {
+      "name": "ProductID",
+      "type": "INTEGER",
+      "nullable": false,
+      "primary_key": true
+    },
+    {
+      "name": "ProductName",
+      "type": "TEXT",
+      "nullable": false,
+      "primary_key": false
+    },
+    {
+      "name": "Supplier",
+      "type": "TEXT",
+      "nullable": true,
+      "primary_key": false
+    },
+    {
+      "name": "Price",
+      "type": "REAL",
+      "nullable": true,
+      "primary_key": false
+    }
+  ],
+  "row_count": 1523,
+  "sample_data": [
+    {
+      "ProductID": 1,
+      "ProductName": "Widget A",
+      "Supplier": "Acme Corp",
+      "Price": 12.50
+    },
+    {
+      "ProductID": 2,
+      "ProductName": "Gadget B",
+      "Supplier": "TechCo",
+      "Price": 24.99
+    },
+    {
+      "ProductID": 3,
+      "ProductName": "Tool C",
+      "Supplier": "Unknown",
+      "Price": 8.75
+    }
+  ]
+}
+```
+
+### Query-Workflow
+
+```mermaid
+sequenceDiagram
+    participant Client
+    participant API
+    participant SQLite
+
+    rect rgb(200, 220, 250)
+        Note over Client,SQLite: Szenario 1: SQL Query ausführen
+        Client->>API: POST /dataquery/query
+        Note over Client,API: SqlQueryRequest
+        API->>SQLite: SELECT ausführen
+        SQLite-->>API: Ergebnisse
+        API-->>Client: SqlQueryResponse
+        Note over Client,API: data, columns, row_count
+    end
+
+    rect rgb(220, 250, 220)
+        Note over Client,SQLite: Szenario 2: Datenbank-Schema abrufen
+        Client->>API: GET /dataquery/schema
+        API->>SQLite: Tabellen auflisten
+        SQLite-->>API: Tabellenliste
+        API-->>Client: DatabaseSchemaResponse
+        Note over Client,API: tables, table_count
+    end
+
+    rect rgb(250, 220, 220)
+        Note over Client,SQLite: Szenario 3: Tabellenstruktur abrufen
+        Client->>API: GET /dataquery/table/{table_name}
+        API->>SQLite: Spalten + Sample Data abrufen
+        SQLite-->>API: Struktur + Daten
+        API-->>Client: TableSchemaResponse
+        Note over Client,API: columns, sample_data
+    end
+```
+
+---
+
+## Gesamtarchitektur
+
+```mermaid
+graph TB
+    subgraph "External Data Source"
+        PBI[Power BI Dataset]
+    end
+    
+    subgraph "Gateway Preprocessing API"
+        subgraph "Data Processor Service"
+            DP_Router[Router]
+            DP_Service[Service]
+            DP_Schemas[Schemas]
+            DP_Domain[Domain Layer]
+        end
+        
+        subgraph "Data Query Service"
+            DQ_Router[Router]
+            DQ_Service[Service]
+            DQ_Schemas[Schemas]
+        end
+    end
+    
+    subgraph "Local Storage"
+        DB[(SQLite Database)]
+    end
+    
+    subgraph "Clients"
+        Client1[Web App]
+        Client2[BI Tool]
+        Client3[API Consumer]
+    end
+    
+    PBI -->|Power BI Reader| DP_Domain
+    DP_Router --> DP_Service
+    DP_Service --> DP_Domain
+    DP_Domain -->|Save| DB
+    
+    DQ_Router --> DQ_Service
+    DQ_Service -->|Query| DB
+    
+    Client1 -->|POST /dataprocessor/update-db-with-config| DP_Router
+    Client2 -->|POST /dataquery/query| DQ_Router
+    Client3 -->|GET /dataquery/schema| DQ_Router
+    
+    style PBI fill:#f9f,stroke:#333,stroke-width:2px
+    style DB fill:#bbf,stroke:#333,stroke-width:3px
+    style DP_Schemas fill:#ffa,stroke:#333,stroke-width:2px
+    style DQ_Schemas fill:#ffa,stroke:#333,stroke-width:2px
+```
+
+---
+
+## API-Endpunkte Übersicht
+
+### Data Processor Endpoints
+
+| Methode | Endpoint | Request Schema | Response Schema | Beschreibung |
+|---------|----------|----------------|-----------------|--------------|
+| POST | `/dataprocessor/update-db` | - | `UpdateDbResponse` | Aktualisiert DB mit YAML-Config |
+| POST | `/dataprocessor/update-db-with-config` | `PreprocessingConfigSchema` | `UpdateDbWithConfigResponse` | Aktualisiert DB mit JSON-Config |
+
+### Data Query Endpoints
+
+| Methode | Endpoint | Request Schema | Response Schema | Beschreibung |
+|---------|----------|----------------|-----------------|--------------|
+| POST | `/dataquery/query` | `SqlQueryRequest` | `SqlQueryResponse` | Führt SQL-Query aus |
+| GET | `/dataquery/schema` | - | `DatabaseSchemaResponse` | Gibt DB-Schema zurück |
+| GET | `/dataquery/table/{table_name}` | - | `TableSchemaResponse` | Gibt Tabellenstruktur zurück |
+
+---
+
+## Typische Use Cases
+
+### Use Case 1: Daten aus Power BI laden und verarbeiten
+
+```mermaid
+flowchart LR
+    A[Client] -->|1. POST PreprocessingConfigSchema| B[API]
+    B -->|2. Daten abrufen| C[Power BI]
+    C -->|3. Raw Data| B
+    B -->|4. Preprocessing| B
+    B -->|5. Speichern| D[(SQLite)]
+    B -->|6. UpdateDbWithConfigResponse| A
+    
+    style A fill:#bfb,stroke:#333,stroke-width:2px
+    style C fill:#f9f,stroke:#333,stroke-width:2px
+    style D fill:#bbf,stroke:#333,stroke-width:2px
+```
+
+### Use Case 2: Daten abfragen
+
+```mermaid
+flowchart LR
+    A[Client] -->|1. POST SqlQueryRequest| B[API]
+    B -->|2. SELECT Query| C[(SQLite)]
+    C -->|3. Ergebnisse| B
+    B -->|4. SqlQueryResponse| A
+    
+    style A fill:#bfb,stroke:#333,stroke-width:2px
+    style C fill:#bbf,stroke:#333,stroke-width:2px
+```
+
+### Use Case 3: Datenbank-Struktur erkunden
+
+```mermaid
+flowchart TD
+    A[Client] -->|1. GET /schema| B[API]
+    B -->|2. DatabaseSchemaResponse| A
+    A -->|3. GET /table/ProductData| B
+    B -->|4. TableSchemaResponse| A
+    A -->|5. POST SqlQueryRequest| B
+    B -->|6. SqlQueryResponse| A
+    
+    style A fill:#bfb,stroke:#333,stroke-width:2px
+```
+
+---
+
+## Validierung und Fehlerbehandlung
+
+Alle Schemas verwenden Pydantic für automatische Validierung:
+
+- **Typsicherheit**: Alle Felder werden auf korrekte Typen geprüft
+- **Required Fields**: Pflichtfelder müssen vorhanden sein
+- **Min/Max Validierung**: z.B. `min_items=1` für `tables`
+- **String Validierung**: z.B. `min_length=1` für SQL-Queries
+- **Custom Validation**: Zusätzliche Business-Logik in den Services
+
+Bei Validierungsfehlern gibt die API einen HTTP 422 (Unprocessable Entity) mit detaillierten Fehlermeldungen zurück.

src/dataprocessor/domain/powerbi_reader.py

@@ -4,6 +4,7 @@ from dataclasses import dataclass
 from src.settings import settings
 import pandas as pd
 import httpx
+import re
 
 
 @dataclass
@@ -13,31 +14,169 @@ class PowerBIReader:
     table_name: str
     base_url: str = settings.POWERBI_BASE_URL
     include_nulls: bool = True
+    measures: list[str] = None
+    group_by_columns: list[str] = None
+    batch_size: int = 10000
+    order_by_column: str | None = None
+    max_batches: int = 100
 
     @classmethod
     async def create(
-        cls, dataset_id: str, access_token: str, table_name: str, **kwargs
+        cls,
-    ):
+        *,
+        dataset_id: str,
+        access_token: str,
+        table_name: str,
+        measures: list[str] = None,
+        group_by_columns: list[str] = None,
+        batch_size: int = 10000,
+        order_by_column: str | None = None,
+        max_batches: int = 100,
+        **kwargs,
+    ) -> "PowerBIReader":
         return cls(
             dataset_id=dataset_id,
             access_token=access_token,
             table_name=table_name,
+            measures=measures or [],
+            group_by_columns=group_by_columns or [],
+            batch_size=batch_size,
+            order_by_column=order_by_column,
+            max_batches=max_batches,
             **kwargs,
         )
 
     def _dax_query(self) -> str:
-        # Escape single quotes in table names per DAX rules
+        """Generate DAX query based on configuration.
-        safe = self.table_name.replace("'", "''")
-        return f"EVALUATE '{safe}'"
 
-    async def read_data(self) -> pd.DataFrame:
+        Generates different DAX queries depending on whether measures and/or
+        group_by_columns are specified:
+
+        1. No measures: EVALUATE 'TableName'
+           Returns all physical/calculated columns from the table.
+
+        2. Measures only: EVALUATE ADDCOLUMNS('TableName', "Measure1", [Measure1], ...)
+           Returns all columns plus the specified measures.
+
+        3. Measures + group_by_columns: EVALUATE SUMMARIZECOLUMNS('Table'[Col1], ..., "Measure1", [Measure1], ...)
+           Returns aggregated measures grouped by the specified columns.
+
+        Returns:
+            DAX query string to execute against Power BI.
         """
-        Calls Power BI REST API: POST /datasets/{datasetId}/executeQueries
+        # Remove XML/HTML tags from table name (e.g., '<oii>Artikel</oii>' -> 'Artikel')
-        with DAX: EVALUATE 'TableName' and returns a DataFrame.
+        cleaned_table_name = re.sub(r'<[^>]+>', '', self.table_name).strip()
+        # Escape single quotes in table names per DAX rules
+        safe_table = cleaned_table_name.replace("'", "''")
+
+        # Case 1: No measures - simple table evaluation
+        if not self.measures:
+            return f"EVALUATE '{safe_table}'"
+
+        # Case 2: Measures without grouping - use ADDCOLUMNS
+        if not self.group_by_columns:
+            measure_clauses = ", ".join(
+                [f'"{measure}", [{measure}]' for measure in self.measures]
+            )
+            return f"EVALUATE ADDCOLUMNS('{safe_table}', {measure_clauses})"
+
+        # Case 3: Measures with grouping - use SUMMARIZECOLUMNS
+        group_cols = ", ".join(
+            [f"'{safe_table}'[{col}]" for col in self.group_by_columns]
+        )
+        measure_clauses = ", ".join(
+            [f'"{measure}", [{measure}]' for measure in self.measures]
+        )
+        return f"EVALUATE SUMMARIZECOLUMNS({group_cols}, {measure_clauses})"
+
+    @staticmethod
+    def _is_numeric_value(value: str | int | float | None) -> bool:
+        """Check if a value is numeric (including numeric strings).
+
+        Args:
+            value: The value to check.
+
+        Returns:
+            True if the value is numeric or a string that represents a number.
         """
+        if value is None:
+            return False
+        if isinstance(value, (int, float)):
+            return True
+        if isinstance(value, str):
+            try:
+                float(value)
+                return True
+            except ValueError:
+                return False
+        return False
+
+    def _dax_query_batch(self, last_value: str | int | float | None = None) -> str:
+        """Generate a batched DAX query using TOPN and keyset pagination.
+
+        Uses ORDER BY with the order_by_column for deterministic ordering,
+        and FILTER to skip already-fetched rows based on the last seen value.
+
+        Args:
+            last_value: The last value of order_by_column from the previous batch.
+                        None for the first batch.
+
+        Returns:
+            DAX query string for fetching the next batch.
+        """
+        # Remove XML/HTML tags from table name (e.g., '<oii>Artikel</oii>' -> 'Artikel')
+        cleaned_table_name = re.sub(r'<[^>]+>', '', self.table_name).strip()
+        # Escape single quotes in table names per DAX rules
+        safe_table = cleaned_table_name.replace("'", "''")
+        order_col = self.order_by_column
+
+        if last_value is None:
+            # First batch: just use TOPN with ORDER BY
+            return (
+                f"EVALUATE TOPN({self.batch_size}, '{safe_table}', "
+                f"'{safe_table}'[{order_col}], ASC)"
+            )
+
+        # Subsequent batches: filter rows where order_col > last_value
+        # IMPORTANT: Handle type conversion to avoid DAX type mismatch errors
+        # Use a type-safe approach: convert column using IF/ISNUMBER to handle both text and numeric columns
+        if self._is_numeric_value(last_value):
+            numeric_val = float(last_value) if '.' in str(last_value) else int(last_value)
+            # Use IF to check if column is numeric, if so use it directly, otherwise convert with VALUE()
+            # This handles both text columns (VALUE converts) and numeric columns (use directly)
+            return (
+                f"EVALUATE TOPN({self.batch_size}, "
+                f"FILTER('{safe_table}', "
+                f"NOT(ISBLANK('{safe_table}'[{order_col}])) && "
+                f"IF(ISNUMBER('{safe_table}'[{order_col}]), '{safe_table}'[{order_col}], VALUE('{safe_table}'[{order_col}])) > {numeric_val}), "
+                f"IF(ISNUMBER('{safe_table}'[{order_col}]), '{safe_table}'[{order_col}], VALUE('{safe_table}'[{order_col}])), ASC)"
+            )
+        else:
+            # String comparison - escape quotes in the value
+            escaped_value = str(last_value).replace('"', '""')
+            return (
+                f"EVALUATE TOPN({self.batch_size}, "
+                f"FILTER('{safe_table}', '{safe_table}'[{order_col}] > \"{escaped_value}\"), "
+                f"'{safe_table}'[{order_col}], ASC)"
+            )
+
+    async def _execute_query(self, dax_query: str) -> pd.DataFrame:
+        """Execute a DAX query and return the results as a DataFrame.
+
+        Args:
+            dax_query: The DAX query string to execute.
+
+        Returns:
+            DataFrame containing the query results.
+        """
+        # Log the DAX query for debugging
+        import logging
+        logger = logging.getLogger(__name__)
+        logger.info(f"Executing DAX query: {dax_query}")
+        
         url = f"{self.base_url}/datasets/{self.dataset_id}/executeQueries"
         body = {
-            "queries": [{"query": self._dax_query()}],
+            "queries": [{"query": dax_query}],
             "serializerSettings": {"includeNulls": self.include_nulls},
         }
 
@@ -50,6 +189,7 @@ class PowerBIReader:
             resp = await client.post(url, headers=headers, json=body)
 
         if resp.status_code != 200:
+            logger.error(f"DAX query failed: {dax_query}")
             raise RuntimeError(
                 f"Power BI executeQueries failed: {resp.status_code} - {resp.text}"
             )
@@ -74,6 +214,116 @@ class PowerBIReader:
         df.columns = [_strip_qual(c) for c in df.columns]
         return df
 
+    async def read_data(self) -> pd.DataFrame:
+        """Fetch data from Power BI, using batching if order_by_column is set.
+
+        If order_by_column is configured, fetches data in batches using
+        keyset pagination to avoid the Power BI API's 1M value limit.
+        Otherwise, fetches all data in a single query (legacy behavior).
+
+        Returns:
+            DataFrame containing all fetched data.
+        """
+        # Legacy behavior: no batching if order_by_column not set
+        if not self.order_by_column:
+            return await self._execute_query(self._dax_query())
+
+        # Batch fetching with keyset pagination
+        import logging
+        logger = logging.getLogger(__name__)
+        
+        all_dfs: list[pd.DataFrame] = []
+        last_value: str | int | None = None
+        batch_num = 0
+        total_rows = 0
+
+        while True:
+            batch_num += 1
+
+            # Safety limit to prevent runaway requests
+            if batch_num > self.max_batches:
+                logger.warning(
+                    f"Reached max_batches limit ({self.max_batches}) for table "
+                    f"'{self.table_name}'. Stopping batch fetch. "
+                    f"Total rows fetched so far: {total_rows}"
+                )
+                break
+
+            dax_query = self._dax_query_batch(last_value)
+            df = await self._execute_query(dax_query)
+
+            if df.empty:
+                logger.info(f"Batch {batch_num}: Empty result, stopping fetch")
+                break
+
+            batch_rows = len(df)
+            total_rows += batch_rows
+            
+            # Log batch info
+            if self.order_by_column in df.columns:
+                min_val = df[self.order_by_column].min()
+                max_val = df[self.order_by_column].max()
+                logger.info(
+                    f"Batch {batch_num}: Fetched {batch_rows} rows, "
+                    f"{self.order_by_column} range: {min_val} to {max_val}, "
+                    f"Total so far: {total_rows}"
+                )
+            else:
+                logger.info(f"Batch {batch_num}: Fetched {batch_rows} rows, Total so far: {total_rows}")
+
+            all_dfs.append(df)
+
+            # Get the maximum value for the next batch (not just the last row)
+            # This ensures we don't skip rows when there are multiple rows per ID
+            # Using max() instead of iloc[-1] because rows are ordered by I_ID,
+            # but if there are multiple rows with the same I_ID, the last row might
+            # have a smaller I_ID than the maximum in the batch
+            max_value = df[self.order_by_column].max()
+            
+            # Ensure numeric values are preserved as numeric (not converted to string)
+            # Check if the column is numeric and convert the value accordingly
+            if pd.api.types.is_numeric_dtype(df[self.order_by_column].dtype):
+                # Column is numeric - ensure value is numeric
+                if pd.isna(max_value):
+                    logger.info(f"Batch {batch_num}: Max value is NaN, stopping")
+                    break  # Can't continue with NaN
+                # Convert to appropriate numeric type
+                if pd.api.types.is_integer_dtype(df[self.order_by_column].dtype):
+                    new_last_value = int(max_value)
+                else:
+                    new_last_value = float(max_value)
+            else:
+                new_last_value = max_value
+
+            # Safety check: if last_value didn't change, we're stuck in a loop
+            if new_last_value == last_value:
+                logger.warning(
+                    f"Batch {batch_num}: last_value didn't change ({last_value}), "
+                    f"stopping to avoid infinite loop"
+                )
+                break
+
+            last_value = new_last_value
+        
+        logger.info(f"Finished fetching batches for table '{self.table_name}': {batch_num} batches, {total_rows} total rows")
+
+        if not all_dfs:
+            return pd.DataFrame()
+
+        result = pd.concat(all_dfs, ignore_index=True)
+        
+        # Log statistics
+        if self.order_by_column and self.order_by_column in result.columns:
+            unique_ids = result[self.order_by_column].nunique()
+            total_rows_final = len(result)
+            logger.info(
+                f"Table '{self.table_name}': {total_rows_final} total rows, "
+                f"{unique_ids} unique {self.order_by_column} values, "
+                f"average {total_rows_final / unique_ids:.2f} rows per {self.order_by_column}"
+            )
+        
+        return result
+
     @staticmethod
     def _get_access_token_sync(
         tenant_id: str,

src/dataprocessor/domain/preprocessor.py

@@ -147,6 +147,11 @@ class TableConfig:
 
     name: str
     powerbi_table_name: str
+    measures: List[str] = field(default_factory=list)
+    group_by_columns: List[str] = field(default_factory=list)
+    batch_size: int = 10000
+    order_by_column: str | None = None
+    max_batches: int = 100
     steps: List[Dict[str, Any]] = field(default_factory=list)
 
 
@@ -184,6 +189,8 @@ class Preprocessor:
             table_config = TableConfig(
                 name=table_data.get("name", ""),
                 powerbi_table_name=table_data.get("powerbi_table_name", ""),
+                measures=table_data.get("measures", []),
+                group_by_columns=table_data.get("group_by_columns", []),
                 steps=table_data.get("steps", []),
             )
             table_configs.append(table_config)
@@ -253,6 +260,11 @@ class Preprocessor:
             table_config = TableConfig(
                 name=table_data.get("name", ""),
                 powerbi_table_name=table_data.get("powerbi_table_name", ""),
+                measures=table_data.get("measures", []),
+                group_by_columns=table_data.get("group_by_columns", []),
+                batch_size=table_data.get("batch_size", 10000),
+                order_by_column=table_data.get("order_by_column"),
+                max_batches=table_data.get("max_batches", 100),
                 steps=table_data.get("steps", []),
             )
             table_configs.append(table_config)

src/dataprocessor/router.py

@@ -50,6 +50,53 @@ async def update_db_with_config(
     3. **Saves to local database**: The processed data is saved to the local SQLite
        database with the specified table name.
 
+    ## Power BI Measures Support
+
+    In addition to retrieving physical/calculated columns from Power BI tables, you can
+    now retrieve Power BI measures using the optional `measures` and `group_by_columns`
+    fields in your table configuration.
+
+    ### Retrieving Measures
+
+    Power BI measures are calculated values that live only in the model and are computed
+    at query time. To retrieve them alongside your table data, add a `measures` array
+    to your table configuration:
+
+    ```json
+    {
+        "name": "Einkaufspreis",
+        "powerbi_table_name": "Einkaufspreis",
+        "measures": ["EP in CHF", "Gesamtbetrag in CHF"],
+        "steps": [...]
+    }
+    ```
+
+    This uses the DAX ADDCOLUMNS pattern: `EVALUATE ADDCOLUMNS('TableName', "MeasureName", [MeasureName], ...)`
+
+    ### Grouping Measures
+
+    If your measures need to be aggregated by specific columns, add the `group_by_columns`
+    field. This is useful when measures are defined with aggregation functions:
+
+    ```json
+    {
+        "name": "Einkaufspreis_Aggregated",
+        "powerbi_table_name": "Einkaufspreis",
+        "measures": ["EP in CHF", "Gesamtbetrag in CHF"],
+        "group_by_columns": ["m_Artikel"],
+        "steps": [...]
+    }
+    ```
+
+    This uses the DAX SUMMARIZECOLUMNS pattern: `EVALUATE SUMMARIZECOLUMNS('Table'[Column], "MeasureName", [MeasureName], ...)`
+
+    ### Measure Name Formatting
+
+    - Measure names with spaces are automatically handled (e.g., "EP in CHF" becomes `[EP in CHF]` in DAX)
+    - If `measures` is empty or not provided, the standard table evaluation is used
+    - If `measures` is provided without `group_by_columns`, ADDCOLUMNS is used
+    - If both `measures` and `group_by_columns` are provided, SUMMARIZECOLUMNS is used
+
     ## Available Preprocessing Steps
 
     The following preprocessing steps are supported. Each step is specified as a

src/dataprocessor/schemas.py

@@ -18,6 +18,8 @@ class TableConfigSchema(BaseModel):
     Attributes:
         name: The name to use for the table in the local SQLite database
         powerbi_table_name: The name of the source table in Power BI dataset
+        measures: Optional list of Power BI measures to retrieve
+        group_by_columns: Optional list of columns to group by when retrieving measures
         steps: List of preprocessing steps to apply to the table data
     """
 
@@ -29,6 +31,31 @@ class TableConfigSchema(BaseModel):
         description="Name of the table in the Power BI dataset",
         example="data_full",
     )
+    measures: List[str] = Field(
+        default_factory=list,
+        description="List of Power BI measure names to retrieve",
+        example=["EP in CHF", "Gesamtbetrag in CHF"],
+    )
+    group_by_columns: List[str] = Field(
+        default_factory=list,
+        description="Columns to group by when retrieving measures (triggers SUMMARIZECOLUMNS)",
+        example=["m_Artikel"],
+    )
+    batch_size: int = Field(
+        default=10000,
+        description="Number of rows to fetch per batch (for large tables)",
+        example=10000,
+    )
+    order_by_column: str | None = Field(
+        default=None,
+        description="Column to order by for batch fetching. Required for batching to work.",
+        example="I_ID",
+    )
+    max_batches: int = Field(
+        default=100,
+        description="Maximum number of batches to fetch. Safety limit to prevent runaway requests.",
+        example=100,
+    )
     steps: List[Dict[str, Any]] = Field(
         default_factory=list,
         description="List of preprocessing steps to apply",

src/dataprocessor/service.py

@@ -54,6 +54,10 @@ class DataProcessorService:
                 dataset_id=settings.POWERBI_DATASET_ID,
                 access_token=self.access_token,
                 table_name=table_config.powerbi_table_name,
+                measures=table_config.measures,
+                group_by_columns=table_config.group_by_columns,
+                batch_size=getattr(table_config, 'batch_size', 10000),
+                order_by_column=getattr(table_config, 'order_by_column', None),
             )
 
             # Step 2: Read data from Power BI
@@ -147,6 +151,11 @@ class DataProcessorService:
                 dataset_id=settings.POWERBI_DATASET_ID,
                 access_token=self.access_token,
                 table_name=table_config.powerbi_table_name,
+                measures=table_config.measures,
+                group_by_columns=table_config.group_by_columns,
+                batch_size=table_config.batch_size,
+                order_by_column=table_config.order_by_column,
+                max_batches=table_config.max_batches,
             )
 
             # Step 2: Read data from Power BI

src/dataquery/service.py

@@ -71,6 +71,53 @@ class DataQueryService:
 
         return True
 
+    def _enforce_query_limit(self, *, query: str) -> str:
+        """Enforce maximum row limit on query.
+
+        If query has LIMIT > SQL_ROW_LIMIT, replace with SQL_ROW_LIMIT.
+        If query has no LIMIT, append LIMIT SQL_ROW_LIMIT.
+        If query has LIMIT <= SQL_ROW_LIMIT, keep as is.
+
+        Args:
+            query: The SQL query to enforce limit on.
+
+        Returns:
+            Query with enforced limit.
+        """
+        max_limit = settings.SQL_ROW_LIMIT
+
+        # Strip trailing semicolons and whitespace to prevent multi-statement errors
+        query = query.rstrip("; \t\n\r")
+
+        # Remove comments and normalize whitespace for parsing
+        cleaned_query = re.sub(r"--.*$", "", query, flags=re.MULTILINE)
+        cleaned_query = re.sub(r"/\*.*?\*/", "", cleaned_query, flags=re.DOTALL)
+
+        # Look for LIMIT clause (case insensitive)
+        # Pattern matches: LIMIT <number> or LIMIT <number> OFFSET <number>
+        limit_pattern = r"\bLIMIT\s+(\d+)(\s+OFFSET\s+\d+)?\s*$"
+        match = re.search(limit_pattern, cleaned_query, re.IGNORECASE)
+
+        if match:
+            # Extract the current limit value
+            current_limit = int(match.group(1))
+
+            if current_limit > max_limit:
+                # Replace with max_limit while preserving OFFSET if present
+                offset_clause = match.group(2) or ""
+                # Find the position in the original query to replace
+                # Use the original query to preserve formatting
+                original_match = re.search(limit_pattern, query, re.IGNORECASE)
+                if original_match:
+                    new_limit_clause = f"LIMIT {max_limit}{offset_clause}"
+                    query = query[: original_match.start()] + new_limit_clause
+            # If current_limit <= max_limit, keep query as is
+        else:
+            # No LIMIT clause found, append one
+            query = f"{query.rstrip()} LIMIT {max_limit}"
+
+        return query
+
     async def execute_query(self, *, query: str) -> SqlQueryResponse:
         """Execute a SELECT query and return the results.
 
@@ -90,6 +137,9 @@ class DataQueryService:
                 message="Only SELECT queries are allowed",
             )
 
+        # Enforce row limit on the query
+        query = self._enforce_query_limit(query=query)
+
         try:
             async with self.engine.begin() as conn:
                 result = await conn.execute(text(query))

src/pp-config.yaml

@@ -30,3 +30,30 @@ tables:
               "Einheit",
               "EP in CHF",
             ]
+
+  # Example: Retrieving Power BI measures with ADDCOLUMNS
+  # Uncomment to retrieve measures alongside all table columns
+  # - name: "Einkaufspreis_With_Measures"
+  #   powerbi_table_name: "Einkaufspreis"
+  #   measures:
+  #     - "EP in CHF"
+  #     - "Gesamtbetrag in CHF"
+  #   steps:
+  #     - to_numeric:
+  #         column: "EP_CHF"
+  #         errors: "coerce"
+  #     - dropna:
+  #         subset: ["EP_CHF"]
+
+  # Example: Retrieving aggregated measures with SUMMARIZECOLUMNS
+  # Uncomment to retrieve measures grouped by specific columns
+  # - name: "Einkaufspreis_Aggregated"
+  #   powerbi_table_name: "Einkaufspreis"
+  #   measures:
+  #     - "EP in CHF"
+  #     - "Gesamtbetrag in CHF"
+  #   group_by_columns:
+  #     - "m_Artikel"
+  #   steps:
+  #     - dropna:
+  #         subset: ["m_Artikel"]

src/settings.py

@@ -30,6 +30,14 @@ class Settings(BaseSettings):
         description="Path to the SQLite database.",
     )
 
+    # --- Database Query Settings ---
+
+    # Maximum number of rows to return from SQL queries.
+    SQL_ROW_LIMIT: int = Field(
+        default=50,
+        description="Maximum number of rows to return from SQL queries. Defaults to 50.",
+    )
+
     # --- API Keys ---
 
     # Preprocessor API key to access this app.

tests/dataquery/__init__.py

@@ -0,0 +1 @@
+"""Data query tests."""