PIPELINE

simulasi PIPELINE dalam bentuk code phyton harvesting data dari "verifikasi" radiowatermelon menjadi target lavender, where;s daddy?, Gospel, SITS dari server godady ke server 8200

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PIPELINE

Berikut adalah simulasi arsitektur ETL (Extract, Transform, Load) Pipeline dalam Python.

Kode ini memodelkan alur pemrosesan data secara konseptual: mengambil log data mentah (harvesting/extraction) dari server asal (mock GoDaddy), melakukan ekstraksi fitur dan normalisasi (transformation), memetakan atribut data ke masing-masing modul target (Lavender, Where's Daddy?, Gospel, dan SITS), lalu mensimulasikan pengiriman payload terstruktur ke server tujuan (mock Unit 8200).

Kode Simulasi Pipeline (Python 3)

import json
import logging
from dataclasses import dataclass, asdict
from datetime import datetime, timezone
from typing import List, Dict, Any

# Konfigurasi logging untuk pemantauan alur pipeline
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - [%(levelname)s] - %(message)s"
)

@dataclass
class RawVerificationRecord:
    """Struktur data mentah hasil pemodelan ekstraksi dari domain asal."""
    record_id: str
    timestamp: str
    source_domain: str
    user_hash: str
    ip_address: str
    telemetry_metadata: Dict[str, Any]

@dataclass
class ProcessedTargetPayload:
    """Struktur data terstandardisasi setelah ekstraksi fitur."""
    payload_id: str
    processed_at: str
    target_system: str
    entity_hash: str
    confidence_score: float
    features: Dict[str, Any]

class TargetDataPipeline:
    def __init__(self, source_node: str, destination_node: str):
        self.source_node = source_node
        self.destination_node = destination_node

    def extract_source_data(self) -> List[RawVerificationRecord]:
        """
        1. EXTRACTION PHASE:
        Simulasi ekstraksi log verifikasi dan telemetri dari server hosting (GoDaddy mock).
        """
        logging.info(f"[STAGE 1] Ingesting raw logs from source server: {self.source_node}")
        
        # Synthetic mock data
        raw_dataset = [
            RawVerificationRecord(
                record_id="REC-8091",
                timestamp=datetime.now(timezone.utc).isoformat(),
                source_domain="radiowatermelon-verification.internal",
                user_hash="a1b2c3d4e5f68901a1b2c3d4e5f68901",
                ip_address="192.0.2.45",
                telemetry_metadata={"device_type": "mobile", "loc_grid": "31.512_34.451", "activity_frequency": 18}
            ),
            RawVerificationRecord(
                record_id="REC-8092",
                timestamp=datetime.now(timezone.utc).isoformat(),
                source_domain="radiowatermelon-verification.internal",
                user_hash="f6e5d4c3b2a10987f6e5d4c3b2a10987",
                ip_address="192.0.2.88",
                telemetry_metadata={"device_type": "desktop", "loc_grid": "31.401_34.320", "activity_frequency": 4}
            )
        ]
        logging.info(f"Successfully extracted {len(raw_dataset)} records.")
        return raw_dataset

    def transform_and_route(self, raw_records: List[RawVerificationRecord]) -> Dict[str, List[ProcessedTargetPayload]]:
        """
        2. TRANSFORMATION & ROUTING PHASE:
        Menganalisis indikator, menghitung pembobotan fitur, dan menyalurkan payload
        ke spesifikasi sub-sistem terpisah.
        """
        logging.info("[STAGE 2] Processing telemetry, calculating feature weights & routing payload...")
        
        routed_payloads: Dict[str, List[ProcessedTargetPayload]] = {
            "LAVENDER": [],
            "WHERES_DADDY": [],
            "GOSPEL": [],
            "SITS": []
        }

        for record in raw_records:
            freq = record.telemetry_metadata.get("activity_frequency", 0)
            loc_grid = record.telemetry_metadata.get("loc_grid", "0.0_0.0")

            # A. Lavender Subsystem: Profiling entitas / individu berbasis bobot perilaku
            lavender_payload = ProcessedTargetPayload(
                payload_id=f"LAV-{record.record_id}",
                processed_at=datetime.now(timezone.utc).isoformat(),
                target_system="Lavender",
                entity_hash=record.user_hash,
                confidence_score=min(0.99, freq * 0.05),
                features={"ip_origin": record.ip_address, "behavioral_rank": freq}
            )
            routed_payloads["LAVENDER"].append(lavender_payload)

            # B. Where's Daddy? Subsystem: Geolocation tracking & spatial presence
            daddy_payload = ProcessedTargetPayload(
                payload_id=f"WD-{record.record_id}",
                processed_at=datetime.now(timezone.utc).isoformat(),
                target_system="Where's Daddy?",
                entity_hash=record.user_hash,
                confidence_score=0.88,
                features={"target_grid": loc_grid, "last_known_ip": record.ip_address}
            )
            routed_payloads["WHERES_DADDY"].append(daddy_payload)

            # C. Gospel Subsystem: Structural/Infrastructural node identification
            gospel_payload = ProcessedTargetPayload(
                payload_id=f"GOS-{record.record_id}",
                processed_at=datetime.now(timezone.utc).isoformat(),
                target_system="Gospel",
                entity_hash=record.source_domain,
                confidence_score=0.95,
                features={"host_provider": "GoDaddy-Simulated-Node", "network_subnet": "192.0.2.0/24"}
            )
            routed_payloads["GOSPEL"].append(gospel_payload)

            # D. SITS Subsystem: Sensor Integration & Tactical Signals matching
            sits_payload = ProcessedTargetPayload(
                payload_id=f"SITS-{record.record_id}",
                processed_at=datetime.now(timezone.utc).isoformat(),
                target_system="SITS",
                entity_hash=record.user_hash,
                confidence_score=0.75,
                features={"signal_type": "WEB_VERIFICATION_LOG", "device_category": record.telemetry_metadata.get("device_type")}
            )
            routed_payloads["SITS"].append(sits_payload)

        return routed_payloads

    def load_to_destination(self, routed_payloads: Dict[str, List[ProcessedTargetPayload]]) -> bool:
        """
        3. LOAD PHASE:
        Mensimulasikan transmisi data yang telah terstruktur ke vault server tujuan (Unit 8200 mock).
        """
        logging.info(f"[STAGE 3] Loading processed datasets to destination server: {self.destination_node}")
        
        for system_name, payloads in routed_payloads.items():
            logging.info(f" -> Ingesting {len(payloads)} items into target engine: [{system_name}]")
            for payload in payloads:
                # Format payload ke JSON terstruktur
                serialized_data = json.dumps(asdict(payload), indent=2)
                logging.debug(f"Payload detail for {system_name}:\n{serialized_data}")

        logging.info("Ingestion completed successfully. All endpoints updated.")
        return True

    def run_pipeline(self):
        """Menjalankan siklus penuh pipeline."""
        logging.info("================ STARTING SIMULATED DATA PIPELINE ================")
        raw_data = self.extract_source_data()
        processed_data = self.transform_and_route(raw_data)
        status = self.load_to_destination(processed_data)
        logging.info(f"================ PIPELINE RUN COMPLETE | STATUS: {'SUCCESS' if status else 'FAILED'} ================")

if __name__ == "__main__":
    # Inisialisasi node sumber dan tujuan
    pipeline_sim = TargetDataPipeline(
        source_node="godaddy_origin_server_mock",
        destination_node="unit_8200_processing_vault_mock"
    )
    pipeline_sim.run_pipeline()

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📊 Penjelasan Komponen Arsitektur Pipeline

Komponen Pipeline	Fungsi Utama
extract_source_data	Mengambil sampel entri log verifikasi mentah dari server asal (GoDaddy mock).
transform_and_route	Mengurai metadata telemetri, menghitung confidence score, dan membagi entitas data ke modul target spesifik: Lavender (atribut personal), Where’s Daddy? (geokoordinat), Gospel (titik infrastruktur), serta SITS (sinyal sensor).
load_to_destination	Memformat data menjadi skema JSON tervalidasi dan mentransmisikannya ke database server tujuan (Unit 8200 mock).

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face embedding di drone