Airbyte ETL Post-mortem Analysis

Airbyte ETL Post-mortem

Airbyte ELT ETL Post-mortem Pipeline Sync Failure Root Cause Data Quality Schema CDC Connector Alert Prevention Production

Post-mortem Template

# === ETL Post-mortem Template ===

from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class PostMortem:
    title: str
    severity: str
    date: str
    impact: str
    timeline: list
    root_cause: str
    contributing_factors: list
    resolution: str
    action_items: list
    lessons_learned: list

example = PostMortem(
    title="Stripe Sync Failure - 6 hours data gap",
    severity="P2 - High",
    date="2025-01-15",
    impact="Dashboard ไม่มีข้อมูล Payment 6 ชั่วโมง "
           "Finance Team ไม่เห็น Revenue Real-time "
           "ไม่มี Data Loss (Incremental Sync ดึงย้อนหลังได้)",
    timeline=[
        "02:00 - Stripe API Token Expired",
        "02:05 - Airbyte Sync Failed (Auto-retry 3x)",
        "02:20 - All Retries Failed",
        "08:00 - Data Engineer เริ่มงาน เห็น Alert",
        "08:15 - Investigate → Found Token Expired",
        "08:20 - Rotate Token → Trigger Manual Sync",
        "08:45 - Sync Complete → Data Backfilled",
    ],
    root_cause="Stripe API Token หมดอายุ 90 วัน "
               "ไม่มี Alert แจ้งก่อน Token Expire "
               "ไม่มี Auto-rotation",
    contributing_factors=[
        "ไม่มี Token Expiry Monitoring",
        "Alert ส่งเฉพาะ Email ไม่มี Slack/PagerDuty",
        "On-call ไม่ได้ดู Email ตอนกลางคืน",
    ],
    resolution="Rotate Stripe API Token "
              "Trigger Manual Sync Backfill 6 hours "
              "Verify Row Count ตรงกับ Stripe Dashboard",
    action_items=[
        "ตั้ง Token Expiry Alert 14 วันก่อนหมดอายุ",
        "ตั้ง Auto-rotation Script ทุก 60 วัน",
        "เพิ่ม Slack + PagerDuty Alert สำหรับ Sync Failure",
        "ตั้ง On-call Schedule สำหรับ Data Team",
    ],
    lessons_learned=[
        "Credential Management ต้องมี Expiry Tracking",
        "Alert ต้องไปถึงคนที่แก้ได้ ไม่ใช่แค่ Email",
        "Incremental Sync ช่วยให้ Backfill ได้ ไม่สูญเสียข้อมูล",
    ],
)

print(f"=== Post-mortem: {example.title} ===")
print(f"Severity: {example.severity} | Date: {example.date}")
print(f"\nImpact: {example.impact}")
print(f"\nTimeline:")
for t in example.timeline:
    print(f"  {t}")
print(f"\nRoot Cause: {example.root_cause}")
print(f"\nAction Items:")
for a in example.action_items:
    print(f"  - {a}")

Monitoring & Alert

# === Pipeline Monitoring ===

# Airbyte API - Check Sync Status
# curl -X POST https://api.airbyte.com/v1/jobs/list \
#   -H "Authorization: Bearer $AIRBYTE_TOKEN" \
#   -d '{"connectionId": "xxx", "limit": 5}'

@dataclass
class MonitorCheck:
    check: str
    metric: str
    threshold: str
    alert_channel: str
    runbook: str

checks = [
    MonitorCheck("Sync Status",
        "airbyte_sync_status (succeeded/failed)",
        "Any Failure → Alert Immediately",
        "Slack #data-alerts + PagerDuty",
        "1.Check Airbyte Log 2.Check Source API 3.Retry"),
    MonitorCheck("Sync Duration",
        "airbyte_sync_duration_seconds",
        "> 2x Average Duration",
        "Slack #data-alerts",
        "1.Check Data Volume 2.Check Worker Resources"),
    MonitorCheck("Row Count",
        "rows_synced per run (compare with previous)",
        "Δ > 50% (มากขึ้นหรือน้อยลงผิดปกติ)",
        "Slack #data-quality",
        "1.Check Source Data 2.Check Filter/Query"),
    MonitorCheck("Freshness",
        "time since last successful sync",
        "> 2x Schedule Interval",
        "PagerDuty (P2)",
        "1.Check Sync Queue 2.Check Worker Status"),
    MonitorCheck("Schema Change",
        "schema_change_detected event",
        "Any Schema Change → Review Required",
        "Slack #data-schema",
        "1.Review Change 2.Update dbt Model 3.Approve"),
    MonitorCheck("Credential Expiry",
        "days_until_token_expiry",
        "< 14 days → Warning < 3 days → Critical",
        "Slack + Email to Owner",
        "1.Rotate Token 2.Update Airbyte Connection"),
]

print("=== Monitoring Checks ===")
for c in checks:
    print(f"  [{c.check}] Metric: {c.metric}")
    print(f"    Threshold: {c.threshold}")
    print(f"    Alert: {c.alert_channel}")
    print(f"    Runbook: {c.runbook}")

Prevention Checklist

# === Prevention Best Practices ===

@dataclass
class Prevention:
    category: str
    practice: str
    tool: str
    frequency: str

practices = [
    Prevention("Credential Management",
        "Track Expiry Date Auto-rotate Vault Storage",
        "HashiCorp Vault AWS Secrets Manager",
        "Auto-rotate ทุก 60 วัน"),
    Prevention("Data Quality Testing",
        "Row Count Not Null Unique Freshness Range",
        "dbt Test Great Expectations Soda",
        "ทุก Sync Run"),
    Prevention("Schema Management",
        "Auto-detect Change Review Before Apply",
        "Airbyte Schema Change + dbt Schema Test",
        "ทุก Sync Run"),
    Prevention("Capacity Planning",
        "Monitor Storage Compute Worker Trend",
        "Prometheus Grafana CloudWatch",
        "Review ทุกสัปดาห์"),
    Prevention("Disaster Recovery",
        "Backup Config Connector Settings Schedule",
        "Airbyte API Export Terraform IaC",
        "Backup ทุกวัน Test Restore ทุกเดือน"),
    Prevention("Post-mortem Review",
        "Review Action Items Track Completion",
        "Jira Confluence Notion",
        "ทุก Incident + Monthly Review"),
]

print("=== Prevention Practices ===")
for p in practices:
    print(f"  [{p.category}]")
    print(f"    Practice: {p.practice}")
    print(f"    Tool: {p.tool}")
    print(f"    Frequency: {p.frequency}")

เคล็ดลับ

• Incremental: ใช้ Incremental Sync เสมอ ลด Cost + Backfill ได้
• dbt Test: ตรวจ Data Quality ทุก Sync Run
• Credential: Auto-rotate Token ทุก 60 วัน
• Blameless: Post-mortem ไม่โทษคน เน้นปรับปรุง Process
• Action Items: ติดตาม Action Items ทุกข้อให้เสร็จ

Airbyte คืออะไร

Open Source ELT ETL 300+ Connectors API Database SaaS CDC Incremental dbt Docker Kubernetes Cloud Self-hosted Schedule Monitor

สรุป

Airbyte ETL Post-mortem Pipeline Sync Failure Root Cause Data Quality dbt Test Credential Monitor Alert Prevention Blameless Production