Monte Carlo Observability Shift Left Security

Monte Carlo Shift Left

Monte Carlo Data Observability Shift Left Security Data Quality Freshness Volume Schema Lineage Anomaly Detection dbt Great Expectations

Data Observability Architecture

# === Data Observability Pipeline ===

from dataclasses import dataclass

@dataclass
class ObservabilityLayer:
    layer: str
    tool: str
    checks: str
    when: str
    action_on_fail: str

layers = [
    ObservabilityLayer("Source (Shift Left)",
        "Great Expectations + Custom Validators",
        "Schema Validation, PII Detection, Null Check, Type Check",
        "ก่อน Ingestion (ที่ Source)",
        "Reject Bad Data, Alert Data Owner"),
    ObservabilityLayer("Ingestion",
        "Airflow Sensors + Monte Carlo",
        "Freshness, Volume, File Format, Encoding",
        "ระหว่าง Ingestion",
        "Retry, Dead Letter Queue, Alert"),
    ObservabilityLayer("Transformation",
        "dbt Tests + Monte Carlo",
        "unique, not_null, accepted_values, relationships, custom SQL",
        "หลัง dbt run (CI/CD)",
        "Block Deploy, Alert Engineer"),
    ObservabilityLayer("Warehouse",
        "Monte Carlo (Auto ML Monitors)",
        "Freshness, Volume, Schema, Distribution, Lineage",
        "ต่อเนื่อง (24/7 Monitoring)",
        "Alert SOC, Impact Analysis, Incident"),
    ObservabilityLayer("Consumption",
        "Monte Carlo + Dashboard Monitoring",
        "Dashboard Load Time, Query Performance, User Reports",
        "เมื่อ User Report ปัญหา",
        "Root Cause Analysis via Lineage"),
]

print("=== Observability Layers ===")
for l in layers:
    print(f"\n  [{l.layer}] Tool: {l.tool}")
    print(f"    Checks: {l.checks}")
    print(f"    When: {l.when}")
    print(f"    On Fail: {l.action_on_fail}")

Shift Left Implementation

# === Shift Left Data Quality in CI/CD ===

# dbt test examples (schema.yml)
# models:
#   - name: orders
#     columns:
#       - name: order_id
#         tests:
#           - unique
#           - not_null
#       - name: status
#         tests:
#           - accepted_values:
#               values: ['pending', 'shipped', 'delivered', 'cancelled']
#       - name: total_amount
#         tests:
#           - not_null
#           - dbt_utils.expression_is_true:
#               expression: ">= 0"

# Great Expectations example
# import great_expectations as gx
# context = gx.get_context()
# validator = context.sources.pandas_default.read_csv("orders.csv")
# validator.expect_column_values_to_not_be_null("order_id")
# validator.expect_column_values_to_be_between("total_amount", min_value=0)
# validator.expect_column_values_to_be_in_set("status",
#     ["pending", "shipped", "delivered", "cancelled"])
# result = validator.validate()

@dataclass
class ShiftLeftCheck:
    check: str
    tool: str
    stage: str
    example: str
    impact: str

checks = [
    ShiftLeftCheck("Schema Validation",
        "Monte Carlo / dbt / Custom",
        "PR Review (CI)",
        "ตรวจ Schema Change ก่อน Merge dbt Model",
        "ป้องกัน Breaking Change ไป Production"),
    ShiftLeftCheck("Data Contract",
        "dbt Tests / Great Expectations",
        "Ingestion (Pre-load)",
        "ตรวจ Type Null Range ก่อน Load เข้า Warehouse",
        "ป้องกัน Bad Data เข้า Warehouse"),
    ShiftLeftCheck("PII Detection",
        "Monte Carlo / Custom Regex",
        "Pre-Ingestion + Post-Transform",
        "หา Email Phone ID Number ใน Column ที่ไม่ควรมี",
        "ป้องกัน Data Leak Compliance Violation"),
    ShiftLeftCheck("Business Rule",
        "dbt Tests / Custom SQL Monitor",
        "Post-Transform (dbt run)",
        "Revenue >= 0, User Count > Previous Day × 0.9",
        "ป้องกัน Wrong Data ไป Dashboard Report"),
    ShiftLeftCheck("Lineage Impact",
        "Monte Carlo Lineage",
        "Pre-Deploy (CI)",
        "ดูว่า Model Change กระทบ Dashboard/Report ไหน",
        "รู้ Impact ก่อน Deploy ลด Surprise"),
]

print("=== Shift Left Checks ===")
for c in checks:
    print(f"\n  [{c.check}] Tool: {c.tool}")
    print(f"    Stage: {c.stage}")
    print(f"    Example: {c.example}")
    print(f"    Impact: {c.impact}")

Incident Management

# === Data Incident Response ===

@dataclass
class IncidentStep:
    step: str
    action: str
    tool: str
    time: str

incident_flow = [
    IncidentStep("1. Detection",
        "Monte Carlo ML ตรวจจับ Anomaly อัตโนมัติ",
        "Monte Carlo Auto-monitors",
        "0-5 นาที (อัตโนมัติ)"),
    IncidentStep("2. Alert",
        "ส่ง Alert ไป Slack PagerDuty + สร้าง Incident",
        "Monte Carlo → Slack/PagerDuty/Jira",
        "ทันที"),
    IncidentStep("3. Triage",
        "ดู Impact Analysis ผ่าน Lineage ว่ากระทบอะไร",
        "Monte Carlo Lineage + Impact Dashboard",
        "5-10 นาที"),
    IncidentStep("4. Root Cause",
        "ใช้ Lineage Trace กลับไปหา Source ที่มีปัญหา",
        "Monte Carlo Lineage + Query Logs",
        "10-30 นาที"),
    IncidentStep("5. Fix",
        "แก้ไขที่ Source Fix dbt Model Re-run Pipeline",
        "dbt + Airflow + Git",
        "30-60 นาที"),
    IncidentStep("6. Verify",
        "ตรวจสอบ Data กลับมาปกติ Monitor ต่อเนื่อง",
        "Monte Carlo + dbt Tests",
        "ทันทีหลัง Fix"),
]

print("=== Incident Response ===")
for s in incident_flow:
    print(f"  [{s.step}] {s.action}")
    print(f"    Tool: {s.tool}")
    print(f"    Time: {s.time}")

เคล็ดลับ

• Shift Left: ตรวจ Data Quality ที่ Source ไม่ใช่รอ Dashboard พัง
• dbt Tests: เขียน Test ทุก Model unique not_null accepted_values
• Lineage: ใช้ Lineage หา Impact ก่อน Deploy ลด Surprise
• PII: ตรวจ PII ทุก Table อัตโนมัติ ป้องกัน Compliance Violation
• Baseline: ให้ ML สร้าง Baseline อัตโนมัติ ไม่ต้องตั้ง Threshold เอง

Monte Carlo Data Observability คืออะไร

Data Observability Platform Freshness Volume Schema Distribution Lineage ML Anomaly Snowflake BigQuery Databricks dbt Airflow Alert

สรุป

Monte Carlo Data Observability Shift Left Freshness Volume Schema Lineage dbt Tests Great Expectations PII CI/CD Anomaly Detection Incident