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[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n

\u65b9\u9762<\/th>\n	ETL<\/th>\n	ELT<\/th>\n<\/tr>\n
\u5904\u7406\u987a\u5e8f<\/td>\n	\u52a0\u8f7d\u524d\u8f6c\u6362<\/td>\n	\u52a0\u8f7d\u540e\u8f6c\u6362<\/td>\n<\/tr>\n
\u53ef\u6269\u5c55\u6027<\/td>\n	\u53d7\u5904\u7406\u80fd\u529b\u9650\u5236<\/td>\n	\u9ad8\u5ea6\u53ef\u6269\u5c55<\/td>\n<\/tr>\n
\u6570\u636e\u6e56\u53cb\u597d\u5ea6<\/td>\n	\u4e0d\u592a\u7406\u60f3<\/td>\n	\u5b8c\u7f8e\u5339\u914d<\/td>\n<\/tr>\n
\u5904\u7406\u901f\u5ea6<\/td>\n	\u5bf9\u5927\u578b\u6570\u636e\u96c6\u53ef\u80fd\u8f83\u6162<\/td>\n	\u5bf9\u5927\u6570\u636e\u66f4\u5feb<\/td>\n<\/tr>\n
\u6570\u636e\u8d28\u91cf\u63a7\u5236<\/td>\n	\u66f4\u5bb9\u6613\u524d\u671f\u5b9e\u65bd<\/td>\n	\u9700\u8981\u52a0\u8f7d\u540e\u989d\u5916\u6b65\u9aa4<\/td>\n<\/tr>\n
\u7075\u6d3b\u6027<\/td>\n	\u65b0\u8f6c\u6362\u8f83\u4e0d\u7075\u6d3b<\/td>\n	\u66f4\u7075\u6d3b\uff0c\u53ef\u968f\u65f6\u8f6c\u6362<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n ETL\uff1a\u7ecf\u53d7\u65f6\u95f4\u8003\u9a8c\u7684\u8f6c\u6362\u5668<\/strong><\/h2>\n ETL\u51e0\u5341\u5e74\u6765\u4e00\u76f4\u662f\u9996\u9009\u65b9\u6cd5\uff0c\u8fd9\u662f\u6709\u539f\u56e0\u7684\u3002\u5b83\u5c31\u50cf\u6570\u636e\u5904\u7406\u754c\u7684\u8d44\u6df1\u529f\u592b\u5927\u5e08\u2014\u2014\u4e25\u8c28\u3001\u7cbe\u786e\u4e14\u9ad8\u6548\u3002\u5728\u9999\u6e2f\u5feb\u8282\u594f\u7684\u5546\u4e1a\u73af\u5883\u4e2d\uff0c\u5f53\u9700\u8981\u5728\u6570\u636e\u8fdb\u5165\u751f\u4ea7\u7cfb\u7edf\u4e4b\u524d\u8fdb\u884c\u590d\u6742\u8f6c\u6362\u65f6\uff0cETL\u8868\u73b0\u51fa\u8272\u3002<\/p>\n \u8fd9\u91cc\u6709\u4e00\u4e2a\u7b80\u5355\u7684Python\u4ee3\u7801\u7247\u6bb5\u6765\u8bf4\u660eETL\u8fc7\u7a0b\uff1a<\/p>\n import pandas as pd\r\n\r\n# Extract\r\ndata = pd.read_csv('hong_kong_sales.csv')\r\n\r\n# Transform\r\ndata['total_revenue'] = data['quantity'] * data['price']\r\ndata['date'] = pd.to_datetime(data['date'])\r\n\r\n# Load\r\ndata.to_sql('processed_sales', engine, if_exists='replace')<\/code><\/pre>\n\u8fd9\u6bb5\u4ee3\u7801\u5c55\u793a\u4e86\u57fa\u672c\u7684ETL\u5de5\u4f5c\u6d41\u7a0b\uff0c\u975e\u5e38\u9002\u5408\u9700\u8981\u5728\u6570\u636e\u8fdb\u5165\u9999\u6e2f\u6700\u5148\u8fdb\u6570\u636e\u4e2d\u5fc3\u6258\u7ba1\u7684\u5206\u6790\u7cfb\u7edf\u4e4b\u524d\u51c6\u5907\u6570\u636e\u7684\u573a\u666f\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n ELT\uff1a\u65b0\u5174\u7684\u540e\u8d77\u4e4b\u79c0<\/strong><\/h2>\nELT\u5c31\u50cf\u5728\u9999\u6e2f\u521b\u4e1a\u5708\u6380\u8d77\u98ce\u66b4\u7684\u5e74\u8f7b\u79d1\u6280\u5929\u624d\u3002\u5b83\u5168\u9762\u5173\u6ce8\u901f\u5ea6\u3001\u53ef\u6269\u5c55\u6027\u548c\u7075\u6d3b\u6027\u3002\u5229\u7528\u9999\u6e2f\u5f3a\u5927\u7684\u670d\u52a1\u5668\u79df\u7528\u57fa\u7840\u8bbe\u65bd\uff0cELT\u5229\u7528\u73b0\u4ee3\u6570\u636e\u4ed3\u5e93\u7684\u529b\u91cf\u5b9e\u73b0\u5373\u65f6\u6570\u636e\u8f6c\u6362\u3002<\/p>\n \u4ee5\u4e0b\u662f\u5728\u6570\u636e\u4ed3\u5e93\u4e2d\u4f7f\u7528SQL\u8fdb\u884cELT\u5904\u7406\u7684\u793a\u4f8b\uff1a<\/p>\n -- Extract and Load (already done)\r\n-- Assume data is loaded into 'raw_sales' table\r\n\r\n-- Transform\r\nCREATE TABLE processed_sales AS\r\nSELECT \r\n ,\r\n quantity price AS total_revenue,\r\n TO_DATE(date_string, 'YYYY-MM-DD') AS formatted_date\r\nFROM raw_sales; <\/code><\/pre>\n\u8fd9\u6bb5SQL\u4ee3\u7801\u5c55\u793a\u4e86ELT\u7684\u7075\u6d3b\u6027\uff0c\u8f6c\u6362\u76f4\u63a5\u5728\u6570\u636e\u4ed3\u5e93\u5185\u8fdb\u884c\uff0c\u5145\u5206\u5229\u7528\u4e86\u9999\u6e2f\u670d\u52a1\u5668\u6258\u7ba1\u8bbe\u65bd\u4e2d\u53ef\u7528\u7684\u5904\u7406\u80fd\u529b\u3002<\/p>\n [\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text]<\/p>\n \u9009\u62e9\u4f60\u7684\u6700\u4f73\uff1aETL\u8fd8\u662fELT\uff1f<\/strong><\/h2>\n\u5728ETL\u548cELT\u4e4b\u95f4\u505a\u51fa\u9009\u62e9\u4e0d\u4ec5\u4ec5\u662f\u8ddf\u968f\u8d8b\u52bf\u3002\u5b83\u5173\u4e4e\u4e8e\u5728\u9999\u6e2f\u52a8\u6001\u7684\u79d1\u6280\u751f\u6001\u7cfb\u7edf\u4e2d\u4e0e\u4f60\u7684\u7279\u5b9a\u9700\u6c42\u4fdd\u6301\u4e00\u81f4\u3002\u8003\u8651\u4ee5\u4e0b\u56e0\u7d20\uff1a<\/p>\n 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[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”blog-detail-section”][vc_column][vc_column_text] \u57fa\u7840\u77e5\u8bc6\uff1aETL\u4e0eELT\u89e3\u6790 \u5728\u6211\u4eec\u8ba9\u8fd9\u4e24\u4e2a\u6570\u636e\u5904\u7406\u91cd\u91cf\u7ea7\u9009\u624b\u5bf9\u51b3\u4e4b\u524d\uff0c\u8ba9\u6211\u4eec\u5148\u660e\u786e\u5b83\u4eec\u7684\u5b9e\u9645\u542b\u4e49\uff1a ETL\uff08\u63d0\u53d6\u3001\u8f6c\u6362\u3001\u52a0\u8f7d\uff09\uff1a\u4f20\u7edf\u65b9\u6cd5\uff0c\u6570\u636e\u4ece\u6e90\u7cfb\u7edf\u63d0\u53d6\uff0c\u8f6c\u6362\u4ee5\u9002\u5e94\u64cd\u4f5c\u9700\u6c42\uff0c\u7136\u540e\u52a0\u8f7d\u5230\u76ee\u6807\u6570\u636e\u5e93\u3002 ELT\uff08\u63d0\u53d6\u3001\u52a0\u8f7d\u3001\u8f6c\u6362\uff09\uff1a\u73b0\u4ee3\u65b9\u6cd5\uff0c\u6570\u636e\u88ab\u63d0\u53d6\u540e\u7acb\u5373\u52a0\u8f7d\u5230\u76ee\u6807\u7cfb\u7edf\uff0c\u7136\u540e\u5728\u76ee\u6807\u6570\u636e\u5e93\u5185\u90e8\u8fdb\u884c\u8f6c\u6362\u3002 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ETL\u51e0\u5341\u5e74\u6765\u4e00\u76f4\u662f\u9996\u9009\u65b9\u6cd5\uff0c\u8fd9\u662f\u6709\u539f\u56e0\u7684\u3002\u5b83\u5c31\u50cf\u6570\u636e\u5904\u7406\u754c\u7684\u8d44\u6df1\u529f\u592b\u5927\u5e08\u2014\u2014\u4e25\u8c28\u3001\u7cbe\u786e\u4e14\u9ad8\u6548\u3002\u5728\u9999\u6e2f\u5feb\u8282\u594f\u7684\u5546\u4e1a\u73af\u5883\u4e2d\uff0c\u5f53\u9700\u8981\u5728\u6570\u636e\u8fdb\u5165\u751f\u4ea7\u7cfb\u7edf\u4e4b\u524d\u8fdb\u884c\u590d\u6742\u8f6c\u6362\u65f6\uff0cETL\u8868\u73b0\u51fa\u8272\u3002 \u8fd9\u91cc\u6709\u4e00\u4e2a\u7b80\u5355\u7684Python\u4ee3\u7801\u7247\u6bb5\u6765\u8bf4\u660eETL\u8fc7\u7a0b\uff1a import pandas as pd # Extract data = pd.read_csv(‘hong_kong_sales.csv’) # Transform data[‘total_revenue’] = data[‘quantity’] * data[‘price’] data[‘date’] = pd.to_datetime(data[‘date’]) […]<\/p>\n Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":15156,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[62],"tags":[3258,3257,3259,3260,1882],"class_list":["post-15161","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hong-kong-dedicated-server-sc","tag-elt-sc","tag-etl-sc","tag-data-processing-sc","tag-big-data-sc","tag-hong-kong-hosting-sc"],"acf":[],"yoast_head":"\nETL vs ELT\uff1a\u76f8\u4f3c\u70b9\u548c\u5dee\u5f02<\/title>\n<meta name=\"description\" content=\"\u6df1\u5165\u63a2\u8ba8ETL\u4e0eELT\u7684\u4e89\u8bba\uff1a\u53d1\u73b0\u5173\u952e\u5dee\u5f02\u3001\u4f7f\u7528\u573a\u666f\uff0c\u4ee5\u53ca\u8fd9\u4e9b\u6570\u636e\u5904\u7406\u65b9\u6cd5\u5982\u4f55\u5728\u9999\u6e2f\u7684\u670d\u52a1\u5668\u57fa\u7840\u8bbe\u65bd\u4e2d\u5927\u653e\u5f02\u5f69\u3002\" \/>\n<meta name=\"robots\" content=\"index, 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