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    # 傳統如何翻新,用Serverless新架構改造傳統資料分析ETL:以利用 AWS Lambda Batch 為例 - 楊立偉 (Bryan Yang) {%hackmd j86FNbMoRoeN4Xn0pg5ofw %} 講者: * 資料工程協會 > 時間不夠不會講太多技術細節,以思路為主 老闆:我保證這個需求很簡單 ## 思路 > 1. 釐清需求 > 2. 盤點現狀 > 3. 用戶旅程 > 4. 盤點資源 > 5. 選擇方案 ## 情境:從 AWS 資料庫轉移到 GCP BigQurey ### 釐清需求 給不會寫程式的使用者使用 ### 盤點現況 * 資料在AWS EMR * parquet 格式 * big query 只支援 CSV ### 用戶旅程 使用 [FeatureMap](https://www.featuremap.co/) 製作使用者旅程地圖 轉檔 1. 計算檔案大小 2. 計算所有資源 3. 啟動機器 4. 開始轉檔 5. 判定轉檔成功與否 6. 關閉資源 監控 1. 監控機器狀態 2. 監控運行結果 3. 根據結果決定通知內容 送至 GCP * 將檔案搬移 GCP 通知使用者 * 根據結果決定通知內容 ### 盤點資源 最重要的資源:人力 > 一名開發工程師 > 一名測試工程師 > 一名維護工程師 現況:人力一名... ### 選擇方案 #### 傳統ETL: 前端工程師 後端工程師 ---責任之牆--- 資料工程師 ![](https://i.imgur.com/DyGvdkL.jpg) * 前端 1. API Gateway 2. AWS Lambda * 後端 * 轉檔 3. AWS Batch 4. ECS 5. EC2 * 監控 6. Cloud Watch * 丟到 GCS 7. AWS Lambda 8. Google Storage * GCS 到 BigQuery 9. Google Cloud Function * 通知 Lambda SNS Lambda Post to Slack ![](https://i.imgur.com/vNUpBfz.jpg) #### 優缺點 * 優點 * 一鍵驅動 * 省錢 (AWS -> GCP最花錢) * 好維護 * 缺點 * 難開發 * 元件真的很多 * Python + Node.js * 分成好幾階段開發 * 難測試 * 難部署 * 過程出問題難Debug * GCP的監控還沒串好 講師 Blog: https://medium.com/bryanyang0528 ###### tags: `MW18`

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