diff --git a/data_analysis/src/data_analysis/cli.py b/data_analysis/src/data_analysis/cli.py
new file mode 100644
index 0000000..65a576c
--- /dev/null
+++ b/data_analysis/src/data_analysis/cli.py
@@ -0,0 +1,95 @@
+import argparse
+import json
+import sys
+from pathlib import Path
+import logging
+from typing import List
+
+import pandas as pd  # third party dependency
+
+from data_analysis.core import analyze_data, AnalysisResult
+from data_analysis.io_utils import LogEntry
+
+logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s')
+logger = logging.getLogger(__name__)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Analyse der Donau-Rover Sensordaten für Muster und Anomalien."
+    )
+    parser.add_argument(
+        "--input",
+        required=True,
+        help="Pfad zur JSON-Datei mit Rover-Logdaten."
+    )
+    parser.add_argument(
+        "--output",
+        required=False,
+        help="Pfad zur Zielausgabedatei für die Analyseergebnisse."
+    )
+    return parser.parse_args()
+
+
+def load_log_entries(input_path: Path) -> List[LogEntry]:
+    if not input_path.exists():
+        raise FileNotFoundError(f"Eingabedatei nicht gefunden: {input_path}")
+
+    try:
+        df = pd.read_json(input_path)
+    except ValueError as e:
+        raise ValueError(f"Fehler beim Einlesen von JSON: {e}")
+
+    required_columns = {"t", "Lx", "dB", "Temp", "Inference"}
+    if not required_columns.issubset(df.columns):
+        missing = required_columns - set(df.columns)
+        raise ValueError(f"Fehlende Felder in Eingabedatei: {missing}")
+
+    entries: List[LogEntry] = []
+    for _, row in df.iterrows():
+        try:
+            entry = LogEntry(
+                timestamp=row["t"],
+                luminosity=int(row["Lx"]),
+                sound_level=float(row["dB"]),
+                temperature=float(row["Temp"]),
+                inference=float(row["Inference"])
+            )
+            entries.append(entry)
+        except (TypeError, ValueError) as e:
+            logger.warning(f"Überspringe ungültigen Datensatz: {e}")
+    if not entries:
+        raise ValueError("Keine gültigen LogEinträge geladen.")
+    return entries
+
+
+def save_analysis_result(result: AnalysisResult, output_path: Path) -> None:
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    result_dict = {
+        "significant_patterns": result.significant_patterns,
+        "anomaly_events": result.anomaly_events,
+    }
+    with output_path.open("w", encoding="utf-8") as f:
+        json.dump(result_dict, f, indent=2, ensure_ascii=False)
+    logger.info(f"Analyseergebnisse gespeichert unter: {output_path}")
+
+
+def main() -> None:
+    args = parse_args()
+    input_path = Path(args.input)
+    output_path = Path(args.output) if args.output else Path("output/analysis_result.json")
+
+    try:
+        log_entries = load_log_entries(input_path)
+        result = analyze_data(log_entries)
+
+        assert isinstance(result, AnalysisResult), "Analyseergebnis ist ungültig."
+        save_analysis_result(result, output_path)
+
+    except Exception as e:
+        logger.error(f"Fehler während der Analyse: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
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