{"id":47000,"date":"2023-09-14T07:08:46","date_gmt":"2024-01-15T22:05:44","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/uplift%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8databricks-delta-live-tables%e6%9d%a5%e6%9e%84%e5%bb%bacdc%e5%92%8c%e5%a4%9a%e8%b7%af%e6%95%b0%e6%8d%ae%e6%b5%81%ef%bc%9f\/"},"modified":"2024-05-03T23:40:58","modified_gmt":"2024-05-03T15:40:58","slug":"uplift%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8databricks-delta-live-tables%e6%9d%a5%e6%9e%84%e5%bb%bacdc%e5%92%8c%e5%a4%9a%e8%b7%af%e6%95%b0%e6%8d%ae%e6%b5%81%ef%bc%9f","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/uplift%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8databricks-delta-live-tables%e6%9d%a5%e6%9e%84%e5%bb%bacdc%e5%92%8c%e5%a4%9a%e8%b7%af%e6%95%b0%e6%8d%ae%e6%b5%81%ef%bc%9f\/","title":{"rendered":"Uplift\u5982\u4f55\u4f7f\u7528Databricks Delta Live Tables\u6765\u6784\u5efaCDC\u548c\u591a\u8def\u6570\u636e\u6d41\uff1f"},"content":{"rendered":"

\u5982\u4f55\u901a\u8fc7Databricks Delta\u5b9e\u65f6\u8868\u63d0\u5347 CDC \u6570\u636e\u7ba1\u9053\u7684\u89c4\u6a21 – Databricks\u535a\u5ba2<\/p>\n

\u9019\u672c\u66f8\u662f\u6458\u8981\u7ffb\u8b6f\uff0c\u4e26\u4e0d\u80fd\u4fdd\u8b49\u5167\u5bb9\u7684\u6e96\u78ba\u6027\u3002\u6709\u95dc\u6e96\u78ba\u5167\u5bb9\uff0c\u8acb\u53c3\u8003\u539f\u6587\u3002<\/div>\n

\u8fd9\u7bc7\u6587\u7ae0\u7531Uplift\u7684Ruchira\u548cJoydeep\u5171\u540c\u64b0\u5199\uff0c\u8868\u8fbe\u4e86\u5bf9\u4e8eDatabricks Lakehouse\u5e73\u53f0\u7684\u8d21\u732e\u548c\u9886\u5bfc\u4f5c\u7528\u7684\u611f\u6fc0\u4e4b\u60c5\u3002<\/p><\/blockquote>\n

Uplift\u662f\u9886\u5148\u7684\u201c\u7acb\u5373\u8d2d\u4e70\uff0c\u4ee5\u540e\u4ed8\u6b3e\u201d\u7684\u89e3\u51b3\u65b9\u6848\u63d0\u4f9b\u5546\uff0c\u65e8\u5728\u8ba9\u4eba\u4eec\u4ece\u751f\u6d3b\u4e2d\u83b7\u5f97\u66f4\u591a\uff0c\u5e76\u80fd\u591f\u5728\u9002\u5f53\u7684\u65f6\u95f4\u8fdb\u884c\u8d2d\u4e70\u3002Uplift\u7684\u7075\u6d3b\u4ed8\u6b3e\u9009\u9879\u4e3a\u8d2d\u4e70\u8005\u63d0\u4f9b\u4e86\u7b80\u5355\u4e14\u53ef\u9760\u7684\u9009\u62e9\uff0c\u65e2\u53ef\u4ee5\u7acb\u5373\u8d2d\u4e70\uff0c\u53c8\u53ef\u4ee5\u5728\u540e\u7eed\u671f\u95f4\u8fdb\u884c\u652f\u4ed8\u3002<\/p>\n

Uplift\u7684\u89e3\u51b3\u65b9\u6848\u901a\u8fc7\u6700\u9ad8\u7ea7\u522b\u7684\u5b89\u5168\u6027\u3001\u9690\u79c1\u3001\u6570\u636e\u7ba1\u7406\u548c\u96c6\u6210\uff0c\u4f18\u5316\u4e86\u8d85\u8fc7200\u4e2a\u5408\u4f5c\u4f19\u4f34\u7684\u652f\u4ed8\u6d41\u7a0b\u3002\u8fd9\u6837\u4e00\u6765\uff0c\u5ba2\u6237\u53ef\u4ee5\u5728\u4e0d\u611f\u53d7\u5230\u4efb\u4f55\u6469\u64e6\u7684\u60c5\u51b5\u4e0b\uff0c\u901a\u8fc7\u5728\u7ebf\u3001\u547c\u53eb\u4e2d\u5fc3\u548c\u9762\u5bf9\u9762\u7684\u65b9\u5f0f\u4eab\u53d7\u8d2d\u7269\u4f53\u9a8c\u3002\u8fd9\u4e2a\u5e9e\u5927\u7684\u5408\u4f5c\u4f19\u4f34\u751f\u6001\u7cfb\u7edf\u5bf9\u6211\u4eec\u7684\u5de5\u7a0b\u56e2\u961f\u63d0\u51fa\u4e86\u6570\u636e\u5de5\u7a0b\u548c\u5206\u6790\u65b9\u9762\u7684\u6311\u6218\u3002\u7531\u4e8e\u6570\u636e\u662f\u4f01\u4e1a\u6307\u6570\u7ea7\u589e\u957f\u7684\u4e3b\u8981\u4ef7\u503c\u521b\u9020\u9a71\u52a8\u56e0\u7d20\uff0c\u56e0\u6b64Uplift\u9700\u8981\u4e00\u4e2a\u6781\u5ea6\u53ef\u6269\u5c55\u7684\u89e3\u51b3\u65b9\u6848\u6765\u6700\u5c0f\u5316\u57fa\u7840\u8bbe\u65bd\u548c\u9700\u8981\u7ba1\u7406\u7684”\u7ba1\u7406\u4ee3\u7801\uff08janitor code\uff09”\u3002<\/p>\n

\u901a\u8fc7\u6574\u5408\u6570\u767e\u4e2a\u5408\u4f5c\u4f19\u4f34\u548c\u6570\u636e\u6765\u6e90\uff0cUplift\u5229\u7528\u81ea\u8eab\u7684\u6838\u5fc3\u6570\u636e\u7ba1\u9053\u6765\u63a8\u52a8\u4ee5\u4e0b\u6d1e\u5bdf\u548c\u64cd\u4f5c\u7684\u5b9e\u73b0\u3002<\/p>\n

\u30d5\u30a1\u30f3\u30cd\u30eb\u30e1\u30c8\u30ea\u30af\u30b9 – \u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u7387\u3001\u627f\u8a8d\u7387\u3001\u30c6\u30a4\u30af\u30a2\u30c3\u30d7\u7387\u3001\u30b3\u30f3\u30d0\u30fc\u30b8\u30e7\u30f3\u7387\u3001\u30c8\u30e9\u30f3\u30b6\u30af\u30b7\u30e7\u30f3\u306e\u898f\u6a21<\/p>\n

\u30e6\u30fc\u30b6\u30fc\u30e1\u30c8\u30ea\u30af\u30b9 – \u30ea\u30d4\u30fc\u30c8\u30e6\u30fc\u30b6\u30fc\u7387\u3001\u30c8\u30fc\u30bf\u30eb\u306e\u30a2\u30af\u30c6\u30a3\u30d6\u30e6\u30fc\u30b6\u30fc\u3001\u65b0\u898f\u30e6\u30fc\u30b6\u30fc\u3001\u89e3\u7d04\u7387\u3001\u30c1\u30e3\u30cd\u30eb\u6a2a\u65ad\u30b7\u30e7\u30c3\u30d4\u30f3\u30b0<\/p>\n

\u30d1\u30fc\u30c8\u30ca\u30fc\u30ec\u30dd\u30fc\u30c8 – \u30d1\u30fc\u30c8\u30ca\u30fc\u30ec\u30d9\u30eb\u3067\u306e\u30d5\u30a1\u30f3\u30cd\u30eb\u30e1\u30c8\u30ea\u30af\u30b9\u3001\u53ce\u76ca\u30e1\u30c8\u30ea\u30af\u30b9<\/p>\n

\u30d5\u30a1\u30f3\u30c7\u30a3\u30f3\u30b0 – \u53d7\u7d66\u6761\u4ef6\u306e\u8a55\u4fa1\u6307\u6a19\u3001\u30e1\u30c8\u30ea\u30af\u30b9\u3001\u878d\u8cc7\u3057\u305f\u8cc7\u7523\u306b\u5bfe\u3059\u308b\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0<\/p>\n

\u878d\u8cc7 – \u30ed\u30fc\u30eb\u7387\u3001\u904e\u5931\u306e\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u3001\u56de\u5fa9\u3001\u30af\u30b8\u30ec\u30c3\u30c8\/\u4e0d\u6b63\u306e\u627f\u8a8d\u30d5\u30a1\u30f3\u30cd\u30eb<\/p>\n

\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30dd\u30fc\u30c8 – \u30b3\u30fc\u30eb\u30bb\u30f3\u30bf\u30fc\u306e\u7d71\u8a08\u60c5\u5831\u3001\u30ad\u30e5\u30fc\u306e\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u3001\u652f\u6255\u3044\u30dd\u30fc\u30bf\u30eb\u306e\u30a2\u30af\u30c6\u30a3\u30d3\u30c6\u30a3<\/p>\n

\u4e3a\u4e86\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff0cUplift\u5229\u7528Databricks\u7684Lakehouse\u5e73\u53f0\u8f7b\u677e\u5730\u4eceKafka\u548cS3\u5bf9\u8c61\u5b58\u50a8\u4e2d\u5bfc\u5165\u6570\u767e\u4e2a\u4e3b\u9898\uff0c\u5e76\u6784\u5efa\u4e86\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u96c6\u6210\u7cfb\u7edf\u8fdb\u884c\u534f\u8c03\u3002\u6bcf\u4e2a\u6570\u636e\u6e90\u90fd\u88ab\u72ec\u7acb\u5b58\u50a8\uff0c\u4f46\u7531\u5e94\u7528\u5de5\u7a0b\u56e2\u961f\u81ea\u52a8\u68c0\u6d4b\u548c\u5bfc\u5165\u65b0\u7684\u6570\u636e\u6e90\uff0c\u4f7f\u6bcf\u4e2a\u6570\u636e\u6e90\u7684\u6570\u636e\u80fd\u591f\u72ec\u7acb\u6f14\u5316\uff0c\u4ee5\u4f9b\u540e\u7eed\u7684\u5206\u6790\u56e2\u961f\u4f7f\u7528\u3002<\/p>\n

\u5728\u4f7f\u7528Lakehouse\u5e73\u53f0\u8fdb\u884c\u6807\u51c6\u5316\u4e4b\u524d\uff0c\u6dfb\u52a0\u65b0\u6570\u636e\u6e90\u9700\u8981\u5f00\u53d1\u65b0\u7684\u6570\u636e\u7ba1\u9053\uff0c\u56e0\u6b64\u5728\u6dfb\u52a0\u65b0\u6570\u636e\u6e90\u6216\u56e2\u961f\u4e4b\u95f4\u7684\u66f4\u6539\u65b9\u9762\u7684\u6c9f\u901a\u662f\u624b\u52a8\u7684\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u9519\u8bef\u6df7\u5165\uff0c\u5e76\u6d6a\u8d39\u65f6\u95f4\u3002\u901a\u8fc7\u4f7f\u7528Delta Live Tables\uff0c\u4ed6\u4eec\u7684\u7cfb\u7edf\u53ef\u4ee5\u81ea\u52a8\u9002\u5e94\u53ef\u6269\u5c55\u548c\u53d8\u66f4\uff0c\u5e76\u51cf\u5c11\u4e86\u9700\u8981\u5f00\u53d1\u3001\u7ba1\u7406\u548c\u7f16\u6392\u7684\u7b14\u8bb0\u672c\u6570\u91cf\uff08\u4ece100\u591a\u4e2a\u51cf\u5c11\u5230\u4e24\u4e2a\u7ba1\u9053\uff09\uff0c\u4ece\u800c\u5927\u5927\u52a0\u5feb\u4e86\u83b7\u5f97\u6d1e\u5bdf\u6240\u9700\u7684\u65f6\u95f4\u3002<\/p>\n

\n

\u5728\u8fd9\u4e2a\u6570\u636e\u5bfc\u5165\u6d41\u6c34\u7ebf\u4e2d\uff0c\u63d0\u5347\uff08Uplift\uff09\u6709\u4ee5\u4e0b\u8981\u6c42\u3002<\/p>\n

    \n
  1. \n
      \u901a\u8fc7\u57fa\u4e8eDelta Lake\u7684\u6280\u672f\uff0c\u63d0\u4f9b\u5c06100\u591a\u4e2a\u4e3b\u9898\u4eceKafka\/S3\u53ef\u6269\u5c55\u5730\u5bfc\u5165\u5230Lakehouse\u4e2d\u7684\u80fd\u529b\uff0c\u4f7f\u5206\u6790\u5e08\u80fd\u591f\u4ee5\u8868\u683c\u683c\u5f0f\u6d3b\u7528\u539f\u59cb\u6570\u636e\u3002<\/ol>\n<\/li>\n<\/ol>\n

       <\/p>\n

        \n
      1. \n
          \u63d0\u4f9b\u52a8\u6001\u521b\u5efa\u8868\u683c\u4ee5\u9002\u5e94\u6f5c\u5728\u7684\u65b0Kafka\u4e3b\u9898\u7684\u7075\u6d3b\u6027\uff0c\u4f7f\u65b0\u6570\u636e\u7684\u53d1\u73b0\u548c\u5229\u7528\u53d8\u5f97\u5bb9\u6613\u3002<\/ol>\n<\/li>\n<\/ol>\n

           <\/p>\n

            \n
          1. \n
              \u81ea\u52a8\u66f4\u65b0\u6bcf\u4e2a\u4e3b\u9898\u7684\u6a21\u5f0f\uff0c\u4ee5\u9002\u5e94\u6765\u81eaKafka\u7684\u6570\u636e\u66f4\u6539\u3002<\/ol>\n<\/li>\n<\/ol>\n

               <\/p>\n

                \n
              1. \n
                  \u63d0\u4f9b\u53ef\u4ee5\u901a\u8fc7\u663e\u5f0f\u7684\u8868\u683c\u89c4\u5219\u6765\u8bbe\u7f6e\u7684\u540e\u7eed\u5c42\uff0c\u4ee5\u786e\u4fdd\u6709\u6548\u7ba1\u7406\u5df2\u7ecf\u6295\u5165\u8fd0\u8425\u7684\u8868\u683c\uff0c\u5305\u62ec\u6a21\u5f0f\u5f3a\u5236\u3001\u6570\u636e\u8d28\u91cf\u671f\u671b\u3001\u6570\u636e\u7c7b\u578b\u6620\u5c04\u548c\u9ed8\u8ba4\u503c\u7b49\u3002<\/ol>\n<\/li>\n<\/ol>\n

                   <\/p>\n

                    \n
                  1. \n
                      \u5177\u6709\u5904\u7406\u6240\u6709\u663e\u5f0f\u8bbe\u7f6e\u7684\u8868\u683c\u7684SCD Type1\u7684\u6570\u636e\u6d41\u6c34\u7ebf\u3002<\/ol>\n<\/li>\n<\/ol>\n

                       <\/p>\n

                        \u80fd\u591f\u4e0e\u540e\u7eed\u5e94\u7528\u7a0b\u5e8f\u534f\u540c\u5de5\u4f5c\uff0c\u4ee5\u521b\u5efa\u6458\u8981\u7edf\u8ba1\u4fe1\u606f\u548c\u8d8b\u52bf\u805a\u5408\u3002<\/ol>\n

                        \u9019\u4e9b\u8981\u6c42\u9069\u7528\u65bc\u7a31\u70ba\u300c\u591a\u91cd\u5316\u300d\u8a2d\u8a08\u6a21\u5f0f\u7684\u7528\u4f8b\u3002\u7576\u7368\u7acb\u7684\u6d41\u96c6\u5171\u4eab\u76f8\u540c\u7684\u6e90\u6642\uff0c\u6703\u4f7f\u7528\u591a\u8def\u5f91\u8655\u7406\u3002\u5728\u9019\u500b\u4f8b\u5b50\u4e2d\uff0c\u6211\u5011\u5f9eKafka\u7684\u8a0a\u606f\u4f47\u5217\u548c\u4e00\u7cfb\u5217\u7684S3\u5132\u5b58\u6876\u4e2d\u63d0\u53d6\u5177\u6709100\u500b\u8b8a\u66f4\u4e8b\u4ef6\u7684\u751f\u8cc7\u6599\uff0c\u4e26\u4e14\u4e26\u884c\u89e3\u6790\u5b83\u5011\u5230\u4e00\u500b\u55ae\u4e00\u7684Delta\u8868\u3002<\/p>\n

                        \u8bf7\u6ce8\u610f\uff0c\u591a\u91cd\u5316\u662f\u4e00\u79cd\u590d\u6742\u7684\u6d41\u5f0f\u8bbe\u8ba1\u6a21\u5f0f\uff0c\u4e0e\u4f20\u7edf\u7684\u4e00\u5bf9\u4e00\u6e90\u548c\u76ee\u6807\u6d41\u7684\u5178\u578b\u6a21\u5f0f\u4e0d\u540c\u3002\u5982\u679c\u60a8\u8ba4\u4e3a\u9700\u8981\u591a\u91cd\u5316\uff0c\u4f46\u5c1a\u672a\u5b9e\u65bd\uff0c\u90a3\u4e48\u89c2\u770b\u6709\u5173\u57fa\u672c\u6d41\u5a92\u4f53\u6700\u4f73\u5b9e\u8df5\u4ee5\u53ca\u6db5\u76d6\u4e86\u8be5\u8bbe\u8ba1\u6a21\u5f0f\u5728\u6d41\u5f0f\u64cd\u4f5c\u4e2d\u7684\u6743\u8861\u7684\u89c6\u9891\u53ef\u80fd\u662f\u4e00\u4e2a\u597d\u7684\u8d77\u70b9\u3002<\/p>\n

                        \u8ba9\u6211\u4eec\u6765\u5ba1\u67e5\u5229\u7528Delta Lake\u7684Medallion\u4f53\u7cfb\u7ed3\u6784\u7684\u8fd9\u4e2a\u7528\u4f8b\u4e2d\u7684\u4e24\u4e2a\u901a\u7528\u89e3\u51b3\u65b9\u6848\u3002\u8fd9\u5c06\u6210\u4e3a\u52a0\u5f3a\u4ee5\u4e0b\u4e24\u79cd\u89e3\u51b3\u65b9\u6848\u7684\u57fa\u672c\u6846\u67b6\u3002<\/p>\n

                        \u591a\u5143\u5316\u89e3\u51b3\u65b9\u6848 (Du\u014d ‘\u00e0n)<\/h1>\n

                        Databricks\u306b\u304a\u3051\u308bSpark\u69cb\u9020\u5316\u30b9\u30c8\u30ea\u30fc\u30df\u30f3\u30b0\u306fforeachBatch\u30e1\u30bd\u30c3\u30c9\u3092\u7528\u3044\u30661\u5bfe\u591a\u306e\u30b9\u30c8\u30ea\u30fc\u30df\u30f3\u30b0\u3092\u6d3b\u7528\u3057\u307e\u3059\u3002\u3053\u306e\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u306f\u30d6\u30ed\u30f3\u30ba\u30b9\u30c6\u30fc\u30b8\u30c6\u30fc\u30d6\u30eb\u3092\u8aad\u307f\u8fbc\u307f\u3001\u30de\u30a4\u30af\u30ed\u30d0\u30c3\u30c1\u306e\u4e2d\u3067\u5358\u4e00\u306e\u30b9\u30c8\u30ea\u30fc\u30e0\u3092\u8907\u6570\u306e\u30c6\u30fc\u30d6\u30eb\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/p>\n

                        Databricks\u306eDelta Live Tables(DLT)\u306f\u3001\u4e26\u5217\u3067\u5168\u3066\u306e\u30b9\u30c8\u30ea\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u3001\u7ba1\u7406\u3059\u308b\u305f\u3081\u306b\u7528\u3044\u3089\u308c\u307e\u3059\u3002\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3067\u306f\u3001\u30d6\u30ed\u30f3\u30ba\u30c6\u30fc\u30d6\u30eb\u306e\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u30c8\u30d4\u30c3\u30af\u3092\u52d5\u7684\u306b\u8b58\u5225\u3057\u3001\u30c8\u30d4\u30c3\u30af\u3054\u3068\u306b\u660e\u793a\u7684\u306b\u30b3\u30fc\u30c9\u3092\u66f8\u3044\u305f\u308a\u30c1\u30a7\u30c3\u30af\u30dd\u30a4\u30f3\u30c8\u3092\u7ba1\u7406\u3059\u308b\u3053\u3068\u306a\u3057\u306b\u3001\u30c8\u30d4\u30c3\u30af\u3054\u3068\u306b\u72ec\u7acb\u3057\u305f\u30b9\u30c8\u30ea\u30fc\u30e0\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/p>\n

                        \u5728\u63a5\u4e0b\u6765\u7684\u7ae0\u8282\u4e2d\uff0c\u5047\u8bbe\u5927\u5bb6\u5df2\u7ecf\u5bf9Spark\u7ed3\u6784\u5316\u6d41\u4e0eDelta Live Tables\u7684\u57fa\u672c\u6982\u5ff5\u6709\u6240\u4e86\u89e3\u3002<\/p>\n

                        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cDelta Live Tables\u63d0\u4f9b\u4e86\u4e00\u4e2a\u58f0\u660e\u5f0f\u6d41\u6c34\u7ebf\uff0c\u5728\u9ad8\u5ea6\u7075\u6d3b\u7684\u6258\u7ba1\u67b6\u6784\u4e2d\u80fd\u591f\u5bf9\u6240\u6709\u8868\u5b9a\u4e49\u8fdb\u884c\u914d\u7f6e\u3002DLT\u53ef\u4ee5\u4f7f\u7528\u4e00\u4e2a\u6570\u636e\u6d41\u6c34\u7ebf\u6765\u5b9a\u4e49\u3001\u6d41\u5f0f\u5904\u7406\u548c\u7ba1\u7406100\u4e2a\u8868\uff0c\u800c\u4e14\u4e0d\u4f1a\u635f\u5931\u8868\u7ea7\u7075\u6d3b\u6027\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u5b9a\u671f\u66f4\u65b0\u67d0\u4e2a\u540e\u7eed\u8868\uff0c\u800c\u5176\u4ed6\u8868\u53ef\u4ee5\u5b9e\u65f6\u66f4\u65b0\u7528\u4e8e\u5206\u6790\u3002\u6240\u6709\u8fd9\u4e9b\u53ef\u4ee5\u5728\u4e00\u4e2a\u6570\u636e\u6d41\u6c34\u7ebf\u4e2d\u8fdb\u884c\u7ba1\u7406\u3002<\/p>\n

                        \u5728\u6df1\u5165\u7814\u7a76Delta Live Tables\uff08DLT\uff09\u89e3\u51b3\u65b9\u6848\u4e4b\u524d\uff0c\u8ba9\u6211\u4eec\u5148\u63a2\u8ba8\u4e00\u4e0b\u5728Databricks\u4e2d\u4f7f\u7528Spark Structured Streaming\u7684\u73b0\u6709\u89e3\u51b3\u65b9\u6848\u8bbe\u8ba1\u3002<\/p>\n

                        \u89e3\u51b3\u65b9\u68481\uff1a\u901a\u8fc7Databricks Delta\u548cSpark\u7ed3\u6784\u5316\u6d41\u5b9e\u73b0\u6570\u636e\u7684\u591a\u8def\u590d\u7528\u3002<\/h1>\n
                        \"\"<\/div>\n

                        \u5728\u6784\u5efa\u5316\u6d41\u5f0f\u4efb\u52a1\u4e2d\uff0c\u6d41\u5f0f\u6570\u636e\u4eceKafka\u8bfb\u53d6\u591a\u4e2a\u4e3b\u9898\uff0c\u5e76\u5728foreachBatch\u8bed\u53e5\u4e2d\u5411\u591a\u4e2a\u8868\u683c\u8f93\u5165\u4e00\u4e2a\u6d41\u3002\u4ee5\u4e0b\u7684\u4ee3\u7801\u5757\u5c55\u793a\u4e86\u5c06\u4e00\u4e2a\u6d41\u5199\u5165\u591a\u4e2a\u8868\u683c\u7684\u793a\u4f8b\u3002<\/p>\n

                        df_bronze_stage_1<\/span> =<\/span> spark<\/span>.<\/span>readStream<\/span>.<\/span>format<\/span>(<\/span>\u201c<\/span>json<\/span>\u201d<\/span>).<\/span>load<\/span>()<\/span>\r\n\r\ndef<\/span> writeMultipleTables<\/span>(<\/span>microBatchDf<\/span>,<\/span> BatchId<\/span>):<\/span>\r\n  \r\n  df_topic_1<\/span> =<\/span> (<\/span>microBatchDf<\/span>\r\n                 .<\/span>filter<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span>==<\/span> lit<\/span>(<\/span>\"<\/span>topic_1<\/span>\"<\/span>))<\/span>\r\n                  )<\/span>\r\n  \r\n  df_topic_2<\/span> =<\/span> (<\/span>microBatchDf<\/span>\r\n                 .<\/span>filter<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span>==<\/span> lit<\/span>(<\/span>\"<\/span>topic_2<\/span>\"<\/span>))<\/span>\r\n                  )<\/span>\r\n  \r\n  df_topic_3<\/span> =<\/span> (<\/span>microBatchDf<\/span>\r\n                 .<\/span>filter<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span>==<\/span> lit<\/span>(<\/span>\"<\/span>topic_3<\/span>\"<\/span>))<\/span>\r\n                  )<\/span>\r\n  \r\n  df_topic_4<\/span> =<\/span> (<\/span>microBatchDf<\/span>\r\n                 .<\/span>filter<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span>==<\/span> lit<\/span>(<\/span>\"<\/span>topic_4<\/span>\"<\/span>))<\/span>\r\n                  )<\/span>\r\n  \r\n  df_topic_5<\/span> =<\/span> (<\/span>microBatchDf<\/span>\r\n                 .<\/span>filter<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span>==<\/span> lit<\/span>(<\/span>\"<\/span>topic_5<\/span>\"<\/span>))<\/span>\r\n                  )<\/span>\r\n  \r\n  ### Apply schemas\r\n<\/span>  \r\n  ## Look up schema registry, check to see if the events in each event type are equal to the most recently registered schema, Register new schema\r\n<\/span>  \r\n  ##### Write to sink location (in series within the microBatch)\r\n<\/span>  df_topic_1<\/span>.<\/span>write<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>).<\/span>mode<\/span>(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span>\"<\/span>\/data\/dlt_blog\/bronze_topic_1<\/span>\"<\/span>).<\/span>saveAsTable<\/span>(<\/span>\"<\/span>bronze_topic_1<\/span>\"<\/span>)<\/span>\r\n  df_topic_2<\/span>.<\/span>write<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>mergeSchema<\/span>\"<\/span>,<\/span> \"<\/span>true<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span> \"<\/span>\/data\/dlt_blog\/bronze_topic_2<\/span>\"<\/span>).<\/span>mode<\/span>(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).<\/span>saveAsTable<\/span>(<\/span>\"<\/span>bronze_topic_2<\/span>\"<\/span>)<\/span>\r\n  df_topic_3<\/span>.<\/span>write<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>).<\/span>mode<\/span>(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span> \"<\/span>\/data\/dlt_blog\/bronze_topic_3<\/span>\"<\/span>).<\/span>saveAsTable<\/span>(<\/span>\"<\/span>bronze_topic_3<\/span>\"<\/span>)<\/span>\r\n  df_topic_4<\/span>.<\/span>write<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>).<\/span>mode<\/span>(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span> \"<\/span>\/data\/dlt_blog\/bronze_topic_4<\/span>\"<\/span>).<\/span>saveAsTable<\/span>(<\/span>\"<\/span>bronze_topic_4<\/span>\"<\/span>)<\/span>\r\n  df_topic_5<\/span>.<\/span>write<\/span>.<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>).<\/span>mode<\/span>(<\/span>\"<\/span>overwrite<\/span>\"<\/span>).<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span> \"<\/span>\/data\/dlt_blog\/bronze_topic_5<\/span>\"<\/span>).<\/span>saveAsTable<\/span>(<\/span>\"<\/span>bronze_topic_5<\/span>\"<\/span>)<\/span>\r\n  \r\nreturn<\/span>\r\n\r\n ### Using For each batch - microBatchMode\r\n<\/span> (<\/span>df_bronze_stage_1<\/span> # This is a readStream data frame\r\n<\/span>   .<\/span>writeStream<\/span>\r\n   .<\/span>trigger<\/span>(<\/span>availableNow<\/span>=<\/span>True<\/span>)<\/span> # ProcessingTime='30 seconds'\r\n<\/span>   .<\/span>option<\/span>(<\/span>\"<\/span>checkpointLocation<\/span>\"<\/span>,<\/span> checkpoint_location<\/span>)<\/span>\r\n   .<\/span>foreachBatch<\/span>(<\/span>writeMultipleTables<\/span>)<\/span>\r\n   .<\/span>start<\/span>()<\/span>\r\n )<\/span>\r\n<\/code><\/pre>\n
                        \u5728Spark\u69cb\u9020\u5316\u6d41\u6578\u64da\u8655\u7406\u65b9\u6848\u4e2d\uff0c\u6709\u5e7e\u500b\u95dc\u9375\u7684\u8a2d\u8a08\u8003\u616e\u4e8b\u9805\u3002<\/p>\n
                        \u8981\u4f7f\u7528\u7ed3\u6784\u5316\u6d41\u8fdb\u884c\u4e00\u5bf9\u591a\u7684\u8868\u683c\u6d41\u5f0f\u4f20\u8f93\uff0c\u9700\u8981\u4f7f\u7528foreachBatch\u51fd\u6570\uff0c\u5e76\u4e14\u9700\u8981\u5728\u6bcf\u4e2a\u5fae\u6279\u5904\u7406\u51fd\u6570\u4e2d\u8fdb\u884c\u8868\u683c\u5199\u5165\u64cd\u4f5c\uff08\u8bf7\u53c2\u8003\u4e0a\u9762\u7684\u793a\u4f8b\uff09\u3002\u8fd9\u662f\u4e00\u79cd\u975e\u5e38\u5f3a\u5927\u7684\u8bbe\u8ba1\uff0c\u4f46\u4e5f\u6709\u4e00\u4e9b\u9650\u5236\u3002<\/p>\n
                          \n
                        1. \n
                            \u53ef\u6269\u5c55\u6027\uff1a\u5f53\u8868\u6570\u91cf\u8f83\u5c11\u65f6\uff0c\u4e00\u5bf9\u591a\u7684\u8868\u5199\u5165\u5f88\u7b80\u5355\uff0c\u4f46\u6309\u7167\u4e0a\u8ff0\u4ee3\u7801\u793a\u4f8b\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u6240\u6709\u8868\u90fd\u662f\u4e32\u884c\u5199\u5165\u7684\uff08Spark\u4ee3\u7801\u6309\u987a\u5e8f\u8fd0\u884c\uff0c\u4e0b\u4e00\u6b65\u64cd\u4f5c\u5fc5\u987b\u7b49\u5f85\u524d\u4e00\u6b65\u5b8c\u6210\uff09\u3002\u56e0\u6b64\uff0c\u5f53\u8868\u6570\u91cf\u589e\u52a0\u5230100\u4e2a\u65f6\uff0c\u65e0\u6cd5\u5b9e\u73b0\u6269\u5c55\u3002\u8fd9\u610f\u5473\u7740\u6bcf\u6b21\u6dfb\u52a0\u8868\u65f6\u4f5c\u4e1a\u7684\u603b\u6267\u884c\u65f6\u95f4\u4f1a\u5927\u5927\u589e\u52a0\u3002<\/ol>\n<\/li>\n<\/ol>\n
                            \u590d\u6742\u6027\uff1a\u5199\u5165\u5904\u7406\u662f\u786c\u7f16\u7801\u7684\uff0c\u610f\u5473\u7740\u6ca1\u6709\u7b80\u5355\u7684\u65b9\u6cd5\u53ef\u4ee5\u81ea\u52a8\u68c0\u6d4b\u65b0\u4e3b\u9898\u5e76\u521b\u5efa\u8868\u3002\u6bcf\u5f53\u6709\u65b0\u7684\u6570\u636e\u6e90\u65f6\uff0c\u90fd\u9700\u8981\u8fdb\u884c\u4ee3\u7801\u53d1\u5e03\u3002\u8fd9\u662f\u4e25\u91cd\u7684\u65f6\u95f4\u6d6a\u8d39\uff0c\u4f7f\u5f97\u6d41\u6c34\u7ebf\u53d8\u5f97\u8106\u5f31\u3002\u867d\u7136\u53ef\u80fd\u5b9e\u73b0\uff0c\u4f46\u9700\u8981\u5927\u91cf\u7684\u5f00\u53d1\u5de5\u4f5c\u91cf\u3002<\/p>\n
                            \u4e25\u683c\u6027\uff1a\u8868\u53ef\u80fd\u9700\u8981\u4ee5\u4e0d\u540c\u7684\u5468\u671f\u8fdb\u884c\u66f4\u65b0\u3002\u8fd8\u53ef\u80fd\u6839\u636e\u4e0d\u540c\u7684\u6570\u636e\u8d28\u91cf\u671f\u671b\u3001\u5206\u533a\u548c\u6570\u636e\u5e03\u5c40\u8981\u6c42\uff0c\u9700\u8981\u4e0d\u540c\u7684\u9884\u5904\u7406\u903b\u8f91\u3002\u56e0\u6b64\uff0c\u9700\u8981\u4e3a\u4e0d\u540c\u8868\u7ec4\u521b\u5efa\u5b8c\u5168\u72ec\u7acb\u7684\u4f5c\u4e1a\u3002<\/p>\n
                            \u6548\u7387\uff1a\u6bcf\u4e2a\u8868\u7684\u6570\u636e\u91cf\u53ef\u80fd\u4e0d\u540c\uff0c\u5982\u679c\u5b83\u4eec\u4f7f\u7528\u76f8\u540c\u7684\u6d41\u5f0f\u96c6\u7fa4\uff0c\u53ef\u80fd\u65e0\u6cd5\u63d0\u9ad8\u96c6\u7fa4\u5229\u7528\u7387\u3002\u8981\u5b9e\u73b0\u8fd9\u4e9b\u6d41\u7684\u8d1f\u8f7d\u5e73\u8861\uff0c\u9700\u8981\u66f4\u591a\u7684\u5f00\u53d1\u5de5\u4f5c\u548c\u66f4\u521b\u9020\u6027\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n
                            \u603b\u4f53\u6765\u8bf4\uff0c\u8fd9\u4e2a\u89e3\u51b3\u65b9\u6848\u8fd0\u884c\u5f97\u5f88\u597d\uff0c\u4f46\u662f\u901a\u8fc7\u4f7f\u7528\u5355\u4e00\u7684\u5206\u5e03\u5f0f\u8d26\u672c\u6280\u672f\u7ba1\u9053\uff0c\u53ef\u4ee5\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u5e76\u4e14\u8fdb\u4e00\u6b65\u7b80\u5316\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n
                            \u89e3\u51b3\u65b9\u68482\uff1a\u5229\u7528Databricks\u7684Delta Live Tables\uff08Python\uff09\uff0c\u8fdb\u884c\u6570\u636e\u590d\u5236\u548c\u53d8\u66f4\u6570\u636e\u6355\u83b7\uff08CDC\uff09\u3002<\/h1>\n
                            \u4e3a\u4e86\u7b80\u5316\u6ee1\u8db3\u4e0a\u8ff0\u8981\u6c42\uff08\u81ea\u52a8\u68c0\u6d4b\u65b0\u5efa\u8868\uff0c\u5728\u4e00\u4e2a\u9876\u7ea7\u4e0a\u8fdb\u884c\u5e76\u884c\u6d41\u5904\u7406\uff0c\u5f3a\u5236\u6570\u636e\u8d28\u91cf\uff0c\u6bcf\u4e2a\u8868\u7684\u6a21\u5f0f\u6f14\u53d8\uff0c\u6267\u884c\u6240\u6709\u8868\u7684\u6700\u7ec8\u9636\u6bb5CDC upsert\uff09\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Python\u8fdb\u884cDelta Live Tables\u7684\u5143\u7f16\u7a0b\u6a21\u578b\uff0c\u4ee5\u4fbf\u5728\u6bcf\u4e2a\u9636\u6bb5\u5e76\u884c\u58f0\u660e\u548c\u521b\u5efa\u6240\u6709\u8868\u3002<\/p>\n
                            \"\"<\/div>\n
                            \u8fd9\u4e2a\u662f\u7531\u4e24\u4e2a\u4efb\u52a1\u7ec4\u6210\u7684\u4e00\u4e2a\u4f5c\u4e1a\u6765\u5b9e\u73b0\u3002<\/p>\n
                              \n
                            1. \n
                                \u4efb\u52a1A\uff1a\u9488\u5bf9\u540d\u4e3aBronze Stage 1\u7684\u5355\u4e2aDelta\u8868\uff0c\u4ece\u6240\u6709Kafka\u4e3b\u9898\u7684\u539f\u59cb\u6570\u636e\u4e2d\u8fdb\u884creadStream\u64cd\u4f5c\u3002\u968f\u540e\uff0c\u4efb\u52a1A\u5c06\u4e3a\u6d41\u68c0\u6d4b\u5230\u7684\u6bcf\u4e2a\u5355\u72ec\u4e3b\u9898\u521b\u5efa\u4e00\u4e2a\u89c6\u56fe\u3002\uff08\u8fd8\u53ef\u4ee5\u660e\u786e\u5730\u5b58\u50a8\u89e3\u6790\u6bcf\u4e2a\u4e3b\u9898\u7684\u6709\u6548\u8d1f\u8f7d\uff0c\u5e76\u4f7f\u7528\u6a21\u5f0f\u6ce8\u518c\u8868\u6765\u4f7f\u7528\u6a21\u5f0f\u3002\u8be5\u89c6\u56fe\u53ef\u4ee5\u4fdd\u5b58\u8be5\u6a21\u5f0f\u6ce8\u518c\u8868\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u6a21\u5f0f\u7ba1\u7406\u7cfb\u7edf\uff09\u3002\u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u7b80\u5355\u5730\u4ece\u6bcf\u4e2a\u4e3b\u9898\u7684JSON\u6709\u6548\u8d1f\u8f7d\u52a8\u6001\u63a8\u65ad\u51fa\u6240\u6709\u6a21\u5f0f\uff0c\u5e76\u5728Silver\u8868\u4e2d\u8fdb\u884c\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3002<\/ol>\n<\/li>\n<\/ol>\n
                                \u4efb\u52a1B\uff1a\u4eceBronze Stage 1\u63a5\u6536\u6d41\u76841\u4e2aDelta Live Tables\u7ba1\u9053\u5c06\u4f7f\u7528\u5728\u7b2c\u4e00\u4e2a\u4efb\u52a1\u4e2d\u751f\u6210\u7684\u89c6\u56fe\u4f5c\u4e3a\u914d\u7f6e\uff0c\u5e76\u4f7f\u7528\u5143\u7f16\u7a0b\u6a21\u578b\u89e6\u53d1\u521b\u5efaBronze Stage 2\u8868\uff0c\u9488\u5bf9\u89c6\u56fe\u4e2d\u7684\u6240\u6709\u4e3b\u9898\u3002\u7136\u540e\uff0c\u540c\u4e00\u4e2aDLT\u7ba1\u9053\u5c06\u8bfb\u53d6\u663e\u5f0f\u914d\u7f6e\uff08\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4e3aJSON\u914d\u7f6e\uff09\uff0c\u4ee5\u6ce8\u518c\u201c\u751f\u4ea7\u5c31\u7eea\u201d\u7684\u8868\uff0c\u4f7f\u7528\u66f4\u4e25\u683c\u7684\u6570\u636e\u8d28\u91cf\u671f\u671b\u548c\u6570\u636e\u7c7b\u578b\u5f3a\u5236\u3002\u5728\u6b64\u9636\u6bb5\uff0c\u7ba1\u9053\u5c06\u6e05\u6d17\u6240\u6709Bronze Stage 2\u8868\uff0c\u5e76\u4f7f\u7528APPLY CHANGES INTO\u65b9\u6cd5\u5c06\u66f4\u65b0\u5408\u5e76\u5230\u6700\u7ec8\u7684Silver Stage\u8868\u3002\u6700\u540e\uff0c\u4eceSilver Stage\u4e2d\u6c47\u603b\u751f\u6210\u4e00\u4e2a\u53ef\u4ee5\u7528\u4e8e\u62a5\u544a\u7684\u5206\u6790\u8868- Gold Stage\u3002<\/p>\n
                                \u4f7f\u7528Delta Live Tables\u8fdb\u884c\u590d\u7528\u548cCDC\u7684\u5b9e\u65bd\u6b65\u9aa4\u3002<\/h1>\n
                                \u4ee5\u4e0b\u662f\u4f7f\u7528Delta Live Tables\u8bbe\u7f6e\u591a\u8def\u590d\u7528 + CDC\u7684\u5404\u4e2a\u5b9e\u73b0\u6b65\u9aa4\u3002<\/p>\n
                                  \n
                                1. \n
                                    \u4ece\u539f\u59cb\u6570\u636e\u5230Bronze Stage 1 – \u4eceKafka\u8bfb\u53d6\u4e3b\u9898\u5e76\u5c06\u6570\u636e\u5199\u5165Bronze Stage 1 Delta\u8868\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/ol>\n<\/li>\n<\/ol>\n
                                    \u521b\u5efa\u4e3b\u9898\/\u4e8b\u4ef6\u7684\u552f\u4e00\u89c6\u56fe – \u4eceBronze Stage 1\u521b\u5efa\u89c6\u56fe\u3002<\/p>\n
                                    \u4ece\u5355\u4e2aBronze Stage 1\u5206\u652f\u5230\u5404\u4e2a\u8868 – \u4ece\u89c6\u56fe\u521b\u5efaBronze Stage 2\u7684\u4ee3\u7801\u793a\u4f8b\uff08\u5143\u7f16\u7a0b\uff09\u3002<\/p>\n
                                    \u5c06Bronze Stage 2\u8f6c\u6362\u4e3aSilver Stage – \u4f7f\u7528Silver\u914d\u7f6e\u5c42\u548cSilver\u8868\u7ba1\u7406\u914d\u7f6e\u793a\u4f8b\u8fdb\u884c\u5143\u7f16\u7a0b\u6a21\u578b\u6f14\u793a\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n
                                    \u521b\u5efaGold\u6c47\u603b – \u4f7f\u7528Delta Live Tables\u7684\u4ee3\u7801\u793a\u4f8b\u521b\u5efa\u5b8c\u6574\u7684Gold\u6c47\u603b\u8868\u3002<\/p>\n
                                    DLT\u7ba1\u9053\u7684DAG – \u6d4b\u8bd5\u548c\u6267\u884c\u4eceBronze Stage 1\u5230Gold\u7684DLT\u7ba1\u9053\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n
                                    DLT\u7ba1\u9053\u7684\u914d\u7f6e – \u4f7f\u7528\u53c2\u6570\u3001\u96c6\u7fa4\u5b9a\u5236\u548c\u5176\u4ed6\u5fc5\u8981\u7684\u8bbe\u7f6e\u66f4\u6539\u6765\u914d\u7f6eDelta Live Tables\u7ba1\u9053\uff0c\u4ee5\u5b9e\u73b0\u5728\u751f\u4ea7\u73af\u5883\u4e2d\u7684\u90e8\u7f72\u3002<\/p>\n
                                    \u521b\u5efa\u591a\u4efb\u52a1\u4f5c\u4e1a – \u5c06\u6b65\u9aa41\u548c\u6b65\u9aa42-7\uff08\u6240\u6709\u8fd9\u4e9b\u90fd\u662f\u4e00\u4e2aDLT\u7ba1\u9053\uff09\u5408\u5e76\u4e3a\u5355\u4e2aDatabricks\u4f5c\u4e1a\uff0c\u5176\u4e2d\u4e24\u4e2a\u4efb\u52a1\u6309\u987a\u5e8f\u6267\u884c\u3002<\/p>\n
                                    \u6b65\u9aa41\uff1a\u4ece\u539f\u59cb\u6570\u636e\u5230\u9752\u94dc\u9636\u6bb51<\/h2>\n
                                    startingOffsets<\/span> =<\/span> \"<\/span>earliest<\/span>\"<\/span>\r\n\r\nkafka<\/span> =<\/span> (<\/span>spark<\/span>.<\/span>readStream<\/span>\r\n  .<\/span>format<\/span>(<\/span>\"<\/span>kafka<\/span>\"<\/span>)<\/span>\r\n  .<\/span>option<\/span>(<\/span>\"<\/span>kafka.bootstrap.servers<\/span>\"<\/span>,<\/span> kafka_bootstrap_servers_plaintext<\/span>)<\/span> \r\n  .<\/span>option<\/span>(<\/span>\"<\/span>subscribe<\/span>\"<\/span>,<\/span> topic<\/span> )<\/span>\r\n  .<\/span>option<\/span>(<\/span>\"<\/span>startingOffsets<\/span>\"<\/span>,<\/span> startingOffsets<\/span>)<\/span>\r\n  .<\/span>load<\/span>()<\/span>\r\n        )<\/span>\r\n\r\nread_stream<\/span> =<\/span> (<\/span>kafka<\/span>.<\/span>select<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>key<\/span>\"<\/span>).<\/span>cast<\/span>(<\/span>\"<\/span>string<\/span>\"<\/span>).<\/span>alias<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>),<\/span> col<\/span>(<\/span>\"<\/span>value<\/span>\"<\/span>).<\/span>alias<\/span>(<\/span>\"<\/span>payload<\/span>\"<\/span>))<\/span>\r\n              )<\/span>\r\n\r\n(<\/span>read_stream<\/span>\r\n .<\/span>writeStream<\/span>\r\n .<\/span>format<\/span>(<\/span>\"<\/span>delta<\/span>\"<\/span>)<\/span>\r\n .<\/span>mode<\/span>(<\/span>\"<\/span>append<\/span>\"<\/span>)<\/span>\r\n .<\/span>option<\/span>(<\/span>\"<\/span>checkpointLocation<\/span>\"<\/span>,<\/span> checkpoint_location<\/span>)<\/span>\r\n .<\/span>option<\/span>(<\/span>\"<\/span>path<\/span>\"<\/span>,<\/span> )<\/span>\r\n saveAsTable<\/span>(<\/span>\"<\/span>PreBronzeAllTypes<\/span>\"<\/span>)<\/span>\r\n)<\/span>\r\n<\/code><\/pre>\n
                                    \u6b65\u9aa42\uff1a\u521b\u5efa\u4e00\u4e2a\u7279\u5b9a\u4e3b\u9898\/\u4e8b\u4ef6\u7684\u72ec\u7279\u89c6\u56fe\u3002<\/h2>\n
                                    %<\/span>sql<\/span>\r\nCREATE<\/span> VIEW<\/span> IF<\/span> NOT<\/span> EXISTS<\/span> dlt_types_config<\/span> AS<\/span>\r\nSELECT<\/span> DISTINCT<\/span> topic<\/span>,<\/span> sub_topic<\/span> -- Other things such as schema from a registry, or other helpful metadata from Kafka<\/span>\r\nFROM<\/span> PreBronzeAllTypes<\/span>;<\/span>\r\n<\/code><\/pre>\n
                                    \"\"<\/div>\n
                                    \u6b65\u9aa43\uff1a\u4ece\u4e00\u4e2a\u94dc\u9636\u6bb51\u5206\u652f\u5230\u5404\u4e2a\u4e2a\u4f53\u684c\u5b50<\/h2>\n
                                    %<\/span>python<\/span>\r\nbronze_tables<\/span> =<\/span> spark<\/span>.<\/span>read<\/span>.<\/span>table<\/span>(<\/span>\"<\/span>cody_uplift_dlt_blog.dlt_types_config<\/span>\"<\/span>)<\/span>\r\n\r\n## Distinct list is already managed for us via the view definition\r\n<\/span>topic_list<\/span> =<\/span> [[<\/span>i<\/span>[<\/span>0<\/span>],<\/span>i<\/span>[<\/span>1<\/span>]]<\/span> for<\/span> i<\/span> in<\/span> bronze_tables<\/span>.<\/span>select<\/span>(<\/span>col<\/span>(<\/span>'<\/span>topic<\/span>'<\/span>),<\/span> col<\/span>(<\/span>'<\/span>sub_topic<\/span>'<\/span>)).<\/span>coalesce<\/span>(<\/span>1<\/span>).<\/span>collect<\/span>()]<\/span>\r\n\r\nprint<\/span>(<\/span>topic_list<\/span>)<\/span>\r\n<\/code><\/pre>\n
                                    import<\/span> re<\/span>\r\n\r\ndef<\/span> generate_bronze_tables<\/span>(<\/span>topic<\/span>,<\/span> sub_topic<\/span>):<\/span>\r\n  topic_clean<\/span> =<\/span> re<\/span>.<\/span>sub<\/span>(<\/span>\"<\/span>\/<\/span>\"<\/span>,<\/span> \"<\/span>_<\/span>\"<\/span>,<\/span> re<\/span>.<\/span>sub<\/span>(<\/span>\"<\/span>-<\/span>\"<\/span>,<\/span> \"<\/span>_<\/span>\"<\/span>,<\/span> topic<\/span>))<\/span>\r\n  sub_topic_clean<\/span> =<\/span> re<\/span>.<\/span>sub<\/span>(<\/span>\"<\/span>\/<\/span>\"<\/span>,<\/span> \"<\/span>_<\/span>\"<\/span>,<\/span> re<\/span>.<\/span>sub<\/span>(<\/span>\"<\/span>-<\/span>\"<\/span>,<\/span> \"<\/span>_<\/span>\"<\/span>,<\/span> sub_topic<\/span>))<\/span>\r\n  \r\n  @dlt.table<\/span>(<\/span>\r\n    name<\/span>=<\/span>f<\/span>\"<\/span>bronze_<\/span>{<\/span>topic_clean<\/span>}<\/span>_<\/span>{<\/span>sub_topic_clean<\/span>}<\/span>\"<\/span>,<\/span>\r\n    comment<\/span>=<\/span>f<\/span>\"<\/span>Bronze table for topic: <\/span>{<\/span>topic_clean<\/span>}<\/span>, sub_topic:<\/span>{<\/span>sub_topic_clean<\/span>}<\/span>\"<\/span>\r\n  )<\/span>\r\n  \r\n  def<\/span> create_call_table<\/span>():<\/span>\r\n    ## For now this is the beginning of the DAG in DLT\r\n<\/span>    df<\/span> =<\/span> spark<\/span>.<\/span>readStream<\/span>.<\/span>table<\/span>(<\/span>'<\/span>cody_uplift_dlt_blog.PreBronzeAllTypes<\/span>'<\/span>).<\/span>filter<\/span>((<\/span>col<\/span>(<\/span>\"<\/span>topic<\/span>\"<\/span>)<\/span> ==<\/span> topic<\/span>)<\/span> &<\/span> (<\/span>col<\/span>(<\/span>\"<\/span>sub_topic<\/span>\"<\/span>)<\/span> ==<\/span> sub_topic<\/span>))<\/span>\r\n    \r\n    ## Pass readStream into any preprocessing functions that return a streaming data frame\r\n<\/span>    df_flat<\/span> =<\/span> _flatten<\/span>(<\/span>df<\/span>,<\/span> topic<\/span>,<\/span> sub_topic<\/span>)<\/span>\r\n    \r\n    return<\/span> df_flat<\/span>\r\n<\/code><\/pre>\n
                                    for<\/span> topic<\/span>,<\/span> sub_topic<\/span> in<\/span> topic_list<\/span>:<\/span>\r\n  #print(f\u201dBuild table for {topic} with event type {sub_topic}\u201d)\r\n<\/span>  generate_bronze_tables<\/span>(<\/span>topic<\/span>,<\/span> sub_topic<\/span>)<\/span>\r\n<\/code><\/pre>\n
                                    Step 4: \u5c07Bronze Stage 2\u63d0\u5347\u81f3Silver Stage\u3002<\/h2>\n
                                    \"\"<\/div>\n
                                    \u751f\u6210DLT\u51fd\u6570\u7684\u5b9a\u4e49\uff0c\u4ee5\u8fdb\u884cBronze Stage 2\u7684\u8f6c\u6362\u5904\u7406\u548c\u8868\u8bbe\u7f6e\u3002<\/p>\n
                                    def<\/span> generate_bronze_transformed_tables<\/span>(<\/span>source_table<\/span>,<\/span> trigger_interval<\/span>,<\/span> partition_cols<\/span>,<\/span> zorder_cols<\/span>,<\/span> column_rename_logic<\/span> =<\/span> ''<\/span>,<\/span> drop_column_logic<\/span> =<\/span> ''<\/span>):<\/span>\r\n  \r\n  @dlt.table<\/span>(<\/span>\r\n   name<\/span>=<\/span>f<\/span>\"<\/span>bronze_transformed_<\/span>{<\/span>source_table<\/span>}<\/span>\"<\/span>,<\/span>\r\n   table_properties<\/span>=<\/span>{<\/span>\r\n    \"<\/span>quality<\/span>\"<\/span>:<\/span> \"<\/span>bronze<\/span>\"<\/span>,<\/span>\r\n    \"<\/span>pipelines.autoOptimize.managed<\/span>\"<\/span>:<\/span> \"<\/span>true<\/span>\"<\/span>,<\/span>\r\n    \"<\/span>pipelines.autoOptimize.zOrderCols<\/span>\"<\/span>:<\/span> zorder_cols<\/span>,<\/span>\r\n    \"<\/span>pipelines.trigger.interval<\/span>\"<\/span>:<\/span> trigger_interval<\/span>\r\n  }<\/span>\r\n  )<\/span>\r\n  def<\/span> transform_bronze_tables<\/span>():<\/span>\r\n      source_delta<\/span> =<\/span> dlt<\/span>.<\/span>read_stream<\/span>(<\/span>source_table<\/span>)<\/span>\r\n      transformed_delta<\/span> =<\/span> eval<\/span>(<\/span>f<\/span>\"<\/span>source_delta<\/span>{<\/span>column_rename_logic<\/span>}{<\/span>drop_column_logic<\/span>}<\/span>\"<\/span>)<\/span>\r\n      return<\/span> transformed_delta<\/span>\r\n<\/code><\/pre>\n
                                    \u5b9a\u4e49\u4e00\u4e2a\u51fd\u6570\uff0c\u7528\u4e8e\u5728Delta Live Tables\u4e2d\u751f\u6210\u5e26\u6709CDC\u7684Silver\u8868\u3002<\/p>\n
                                    def<\/span> generate_silver_tables<\/span>(<\/span>target_table<\/span>,<\/span> source_table<\/span>,<\/span> merge_keys<\/span>,<\/span> where_condition<\/span>,<\/span> trigger_interval<\/span>,<\/span> partition_cols<\/span>,<\/span> zorder_cols<\/span>,<\/span> expect_all_or_drop_dict<\/span>,<\/span> column_rename_logic<\/span> =<\/span> ''<\/span>,<\/span> drop_column_logic<\/span> =<\/span> ''<\/span>):<\/span>\r\n\r\n\r\n  #### Define DLT Table this way if we want to map columns\r\n<\/span>  @dlt.view<\/span>(<\/span>\r\n  name<\/span>=<\/span>f<\/span>\"<\/span>silver_source_<\/span>{<\/span>source_table<\/span>}<\/span>\"<\/span>)<\/span>\r\n  @dlt.expect_all_or_drop<\/span>(<\/span>expect_all_or_drop_dict<\/span>)<\/span>\r\n  def<\/span> build_source_view<\/span>():<\/span>\r\n    #\r\n<\/span>    source_delta<\/span> =<\/span> dlt<\/span>.<\/span>read_stream<\/span>(<\/span>source_table<\/span>)<\/span>\r\n    transformed_delta<\/span> =<\/span> eval<\/span>(<\/span>f<\/span>\"<\/span>source_delta<\/span>{<\/span>column_rename_logic<\/span>}{<\/span>column_rename_logic<\/span>}<\/span>\"<\/span>)<\/span>\r\n    return<\/span> transformed_delta<\/span>\r\n    #return dlt.read_stream(f\"bronze_transformed_{source_table}\")\r\n<\/span>\r\n  ### Create the target table definition\r\n<\/span>  dlt<\/span>.<\/span>create_target_table<\/span>(<\/span>name<\/span>=<\/span>target_table<\/span>,<\/span>\r\n  comment<\/span>=<\/span> f<\/span>\"<\/span>Clean, merged <\/span>{<\/span>target_table<\/span>}<\/span>\"<\/span>,<\/span>\r\n  #partition_cols=[\"topic\"],\r\n<\/span>  table_properties<\/span>=<\/span>{<\/span>\r\n    \"<\/span>quality<\/span>\"<\/span>:<\/span> \"<\/span>silver<\/span>\"<\/span>,<\/span>\r\n    \"<\/span>pipelines.autoOptimize.managed<\/span>\"<\/span>:<\/span> \"<\/span>true<\/span>\"<\/span>,<\/span>\r\n    \"<\/span>pipelines.autoOptimize.zOrderCols<\/span>\"<\/span>:<\/span> zorder_cols<\/span>,<\/span>\r\n    \"<\/span>pipelines.trigger.interval<\/span>\"<\/span>:<\/span> trigger_interval<\/span>\r\n  }<\/span>\r\n  )<\/span>\r\n  \r\n  ## Do the merge\r\n<\/span>  dlt<\/span>.<\/span>apply_changes<\/span>(<\/span>\r\n    target<\/span> =<\/span> target_table<\/span>,<\/span>\r\n    source<\/span> =<\/span> f<\/span>\"<\/span>silver_source_<\/span>{<\/span>source_table<\/span>}<\/span>\"<\/span>,<\/span>\r\n    keys<\/span> =<\/span> merge_keys<\/span>,<\/span>\r\n    #where = where_condition,#f\"{source}.Column) <> col({target}.Column)\"\r\n<\/span>    sequence_by<\/span> =<\/span> col<\/span>(<\/span>\"<\/span>timestamp<\/span>\"<\/span>),<\/span>#primary key, auto-incrementing ID of any kind that can be used to identity order of events, or timestamp\r\n<\/span>    ignore_null_updates<\/span> =<\/span> False<\/span>\r\n  )<\/span>\r\n   return<\/span>\r\n<\/code><\/pre>\n
                                    \u83b7\u53d6Silver\u8868\u8bbe\u7f6e\u5e76\u4f20\u9012\u7ed9\u5408\u5e76\u51fd\u6570<\/p>\n
                                    for<\/span> table<\/span>,<\/span> config<\/span> in<\/span> silver_tables_config<\/span>.<\/span>items<\/span>():<\/span>\r\n  ##### Build Transformation Query Logic from a Config File #####\r\n<\/span>  \r\n  #Desired format for renamed columns\r\n<\/span>  result_renamed_columns<\/span> =<\/span> []<\/span>\r\n  for<\/span> renamed_column<\/span>,<\/span> coalesced_columns<\/span> in<\/span> config<\/span>.<\/span>get<\/span>(<\/span>'<\/span>renamed_columns<\/span>'<\/span>)[<\/span>0<\/span>].<\/span>items<\/span>():<\/span>\r\n    renamed_col_result<\/span> =<\/span> []<\/span>\r\n    for<\/span> i<\/span> in<\/span> range<\/span>(<\/span> 0<\/span> ,<\/span> len<\/span>(<\/span>coalesced_columns<\/span>)):<\/span>\r\n      renamed_col_result<\/span>.<\/span>append<\/span>(<\/span>f<\/span>\"<\/span>col(<\/span>'<\/span>{<\/span>coalesced_columns<\/span>[<\/span>i<\/span>]<\/span>}<\/span>'<\/span>)<\/span>\"<\/span>)<\/span>\r\n    result_renamed_columns<\/span>.<\/span>append<\/span>(<\/span>f<\/span>\"<\/span>.withColumn(<\/span>'<\/span>{<\/span>renamed_column<\/span>}<\/span>'<\/span>, coalesce(<\/span>{<\/span>'<\/span>,<\/span>'<\/span>.<\/span>join<\/span>(<\/span>renamed_col_result<\/span>)<\/span>}<\/span>))<\/span>\"<\/span>)<\/span>\r\n    \r\n  #Drop renamed columns\r\n<\/span>  result_drop_renamed_columns<\/span> =<\/span> []<\/span>\r\n  for<\/span> renamed_column<\/span>,<\/span> dropped_column<\/span> in<\/span> config<\/span>.<\/span>get<\/span>(<\/span>'<\/span>renamed_columns<\/span>'<\/span>)[<\/span>0<\/span>].<\/span>items<\/span>():<\/span>\r\n    for<\/span> item<\/span> in<\/span> dropped_column<\/span>:<\/span>\r\n      result_drop_renamed_columns<\/span>.<\/span>append<\/span>(<\/span>f<\/span>\"<\/span>.drop(col(<\/span>'<\/span>{<\/span>item<\/span>}<\/span>'<\/span>))<\/span>\"<\/span>)<\/span>\r\n    \r\n    \r\n  #Desired format for pk NULL check\r\n<\/span>  where_conditions<\/span> =<\/span> []<\/span>\r\n  for<\/span> item<\/span> in<\/span> config<\/span>.<\/span>get<\/span>(<\/span>'<\/span>upk<\/span>'<\/span>):<\/span>\r\n    where_conditions<\/span>.<\/span>append<\/span>(<\/span>f<\/span>\"<\/span>{<\/span>item<\/span>}<\/span> IS NOT NULL<\/span>\"<\/span>)<\/span>\r\n  \r\n  source_table<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>source_table_name<\/span>\"<\/span>)<\/span>\r\n  upks<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>upk<\/span>\"<\/span>)<\/span>\r\n\r\n  ### Table Level Properties\r\n<\/span>  trigger_interval<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>trigger_interval<\/span>\"<\/span>)<\/span>\r\n  partition_cols<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>partition_columns<\/span>\"<\/span>)<\/span>\r\n  zorder_cols<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>zorder_columns<\/span>\"<\/span>)<\/span>\r\n  column_rename_logic<\/span> =<\/span> ''<\/span>.<\/span>join<\/span>(<\/span>result_renamed_columns<\/span>)<\/span>\r\n  drop_column_logic<\/span> =<\/span> ''<\/span>.<\/span>join<\/span>(<\/span>result_drop_renamed_columns<\/span>)<\/span>\r\n  expect_all_or_drop_dict<\/span> =<\/span> config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>expect_all_or_drop<\/span>\"<\/span>)<\/span>\r\n  \r\n  print<\/span>(<\/span>f<\/span>\"\"\"<\/span>Target Table: <\/span>{<\/span>table<\/span>}<\/span> \\n<\/span> \r\n  Source Table: <\/span>{<\/span>source_table<\/span>}<\/span> \\n<\/span> \r\n  ON: <\/span>{<\/span>upks<\/span>}<\/span> \\n<\/span> Renamed Columns: <\/span>{<\/span>result_renamed_columns<\/span>}<\/span> \\n<\/span> \r\n  Dropping Replaced Columns: <\/span>{<\/span>renamed_col_result<\/span>}<\/span> \\n<\/span> \r\n  With the following WHERE conditions: <\/span>{<\/span>where_conditions<\/span>}<\/span>.<\/span>\\n<\/span> \r\n  Column Rename Logic: <\/span>{<\/span>column_rename_logic<\/span>}<\/span> \\n<\/span> \r\n  Drop Column Logic: <\/span>{<\/span>drop_column_logic<\/span>}<\/span>\\n\\n<\/span>\"\"\"<\/span>)<\/span>\r\n    \r\n  ### Do CDC Separate from Transformations\r\n<\/span>  generate_silver_tables<\/span>(<\/span>target_table<\/span>=<\/span>table<\/span>,<\/span> \r\n                         source_table<\/span>=<\/span>config<\/span>.<\/span>get<\/span>(<\/span>\"<\/span>source_table_name<\/span>\"<\/span>),<\/span> \r\n                         trigger_interval<\/span> =<\/span> trigger_interval<\/span>,<\/span>\r\n                         partition_cols<\/span> =<\/span> partition_cols<\/span>,<\/span>\r\n                         zorder_cols<\/span> =<\/span> zorder_cols<\/span>,<\/span>\r\n                         expect_all_or_drop_dict<\/span> =<\/span> expect_all_or_drop_dict<\/span>,<\/span>\r\n                         merge_keys<\/span> =<\/span> upks<\/span>,<\/span>\r\n                         where_condition<\/span> =<\/span> where_conditions<\/span>,<\/span>\r\n                         column_rename_logic<\/span>=<\/span> column_rename_logic<\/span>,<\/span>\r\n                         drop_column_logic<\/span>=<\/span> drop_column_logic<\/span>\r\n                         )<\/span>\r\n<\/code><\/pre>\n
                                    \u6b65\u9aa45: \u521b\u5efa\u91d1\u6c47\u603b<\/h2>\n
                                    \u521b\u5efaGold\u7edf\u8ba1\u8868<\/p>\n
                                    @dlt.table<\/span>(<\/span>\r\nname<\/span>=<\/span>'<\/span>Funnel_Metrics_By_Day<\/span>'<\/span>,<\/span>\r\ntable_properties<\/span>=<\/span>{<\/span>'<\/span>quality<\/span>'<\/span>:<\/span> '<\/span>gold<\/span>'<\/span>}<\/span>\r\n)<\/span>\r\ndef<\/span> getFunnelMetricsByDay<\/span>():<\/span>\r\n  \r\n  summary_df<\/span> =<\/span> (<\/span>dlt<\/span>.<\/span>read<\/span>(<\/span>\"<\/span>Silver_Finance_Update<\/span>\"<\/span>).<\/span>groupBy<\/span>(<\/span>date_trunc<\/span>(<\/span>'<\/span>day<\/span>'<\/span>,<\/span> col<\/span>(<\/span>\"<\/span>timestamp<\/span>\"<\/span>)).<\/span>alias<\/span>(<\/span>\"<\/span>Date<\/span>\"<\/span>)).<\/span>agg<\/span>(<\/span>count<\/span>(<\/span>col<\/span>(<\/span>\"<\/span>timestamp<\/span>\"<\/span>)).<\/span>alias<\/span>(<\/span>\"<\/span>DailyFunnelMetrics<\/span>\"<\/span>))<\/span>\r\n        )<\/span>\r\n  \r\n  return<\/span> summary_df<\/span>\r\n<\/code><\/pre>\n
                                    \u6b65\u9aa46\uff1aDLT\u7ba1\u9053\u7684\u6709\u5411\u65e0\u73af\u56fe<\/h2>\n
                                    \u901a\u8fc7\u5c06\u6240\u6709\u5185\u5bb9\u6c47\u603b\u8d77\u6765\uff0c\u521b\u5efa\u4ee5\u4e0b\u7684DLT\u7ba1\u9053\u3002<\/p>\n
                                    \"\"<\/div>\n
                                    \u7b2c\u4e03\u6b65\uff1a\u8bbe\u7f6eDLT\u6d41\u6c34\u7ebf<\/h2>\n
                                    {<\/span>\r\n    \"id\"<\/span>:<\/span> \"c44f3244-b5b6-4308-baff-5c9c1fafd37a\"<\/span>,<\/span>\r\n    \"name\"<\/span>:<\/span> \"UpliftDLTPipeline\"<\/span>,<\/span>\r\n    \"storage\"<\/span>:<\/span> \"dbfs:\/pipelines\/c44f3244-b5b6-4308-baff-5c9c1fafd37a\"<\/span>,<\/span>\r\n    \"configuration\"<\/span>:<\/span> {<\/span>\r\n        \"pipelines.applyChangesPreviewEnabled\"<\/span>:<\/span> \"true\"<\/span>\r\n    },<\/span>\r\n    \"clusters\"<\/span>:<\/span> [<\/span>\r\n        {<\/span>\r\n            \"label\"<\/span>:<\/span> \"default\"<\/span>,<\/span>\r\n            \"autoscale\"<\/span>:<\/span> {<\/span>\r\n                \"min_workers\"<\/span>:<\/span> 1<\/span>,<\/span>\r\n                \"max_workers\"<\/span>:<\/span> 5<\/span>\r\n            }<\/span>\r\n        }<\/span>\r\n    ],<\/span>\r\n    \"libraries\"<\/span>:<\/span> [<\/span>\r\n        {<\/span>\r\n            \"notebook\"<\/span>:<\/span> {<\/span>\r\n                \"path\"<\/span>:<\/span> \"\/Streaming Demos\/UpliftDLTWork\/DLT - Bronze Layer\"<\/span>\r\n            }<\/span>\r\n        },<\/span>\r\n        {<\/span>\r\n            \"notebook\"<\/span>:<\/span> {<\/span>\r\n                \"path\"<\/span>:<\/span> \"\/Users\/DataEngineering\/Streaming Demos\/UpliftDLTWork\/DLT - Silver Layer\"<\/span>\r\n            }<\/span>\r\n        }<\/span>\r\n    ],<\/span>\r\n    \"target\"<\/span>:<\/span> \"uplift_dlt_blog\"<\/span>,<\/span>\r\n    \"continuous\"<\/span>:<\/span> false<\/span>,<\/span>\r\n    \"development\"<\/span>:<\/span> true<\/span>\r\n}<\/span>\r\n<\/code><\/pre>\n
                                    \u5728\u8fd9\u4e2a\u8bbe\u7f6e\u4e2d\uff0c\u60a8\u53ef\u4ee5\u914d\u7f6e\u7ba1\u9053\u7ea7\u53c2\u6570\u3001\u4e91\u8bbe\u7f6e\uff08\u4f8b\u5982IAM\u5b9e\u4f8b\u914d\u7f6e\u6587\u4ef6\uff09\u548c\u96c6\u7fa4\u8bbe\u7f6e\u7b49\u3002\u6709\u5173\u53ef\u7528\u7684DLT\u8bbe\u7f6e\u7684\u5b8c\u6574\u5217\u8868\uff0c\u8bf7\u53c2\u9605\u6b64\u6587\u6863\u3002<\/p>\n
                                    \u6b65\u9aa4\u516b\uff1a\u521b\u5efa\u591a\u4efb\u52a1\u5de5\u4f5c<\/h2>\n
                                    \u5c06DLT\u7ba1\u9053\u548c\u524d\u5904\u7406\u7ec4\u5408\u4e3a\u4e00\u4e2a\u4f5c\u4e1a\u3002<\/p>\n
                                    \"\"<\/div>\n
                                    \u5728Delta Live Tables\u4e2d\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8868\u8bbe\u7f6e\u72ec\u7acb\u5730\u63a7\u5236\u6bcf\u4e2a\u8868\u7684\u6240\u6709\u8bbe\u7f6e\uff0c\u800c\u65e0\u9700\u66f4\u6539\u7ba1\u9053\u4ee3\u7801\u3002\u8fd9\u4f7f\u5f97\u7ba1\u9053\u7684\u4fee\u6539\u53d8\u5f97\u7b80\u5355\uff0c\u5e76\u901a\u8fc7\u9ad8\u7ea7\u81ea\u52a8\u6269\u5c55\u5927\u5927\u63d0\u9ad8\u4e86\u53ef\u6269\u5c55\u6027\uff0c\u5e76\u901a\u8fc7\u8868\u7684\u5e76\u884c\u751f\u6210\u6765\u63d0\u9ad8\u6548\u7387\u3002\u6700\u540e\uff0c\u652f\u6301\u5177\u6709100\u4e2a\u4ee5\u4e0a\u8868\u7684\u6574\u4e2a\u7ba1\u9053\u7684\u662f\u4e00\u4e2a\u4f5c\u4e1a\uff0c\u5e76\u4e14\u6240\u6709\u7684\u6d41\u5f0f\u57fa\u7840\u8bbe\u65bd\u90fd\u88ab\u62bd\u8c61\u5316\u4e3a\u7b80\u5355\u7684\u8bbe\u7f6e\uff0c\u5e76\u4e14\u4f7f\u7528\u7b80\u5355\u7684\u7528\u6237\u754c\u9762\u6765\u7ba1\u7406\u7ba1\u9053\u7684\u6240\u6709\u8868\u7684\u6570\u636e\u8d28\u91cf\u3002\u5728Delta Live Tables\u4e4b\u524d\uff0c\u7ba1\u7406\u8fd9\u6837\u7684\u7ba1\u9053\u6570\u636e\u8d28\u91cf\u548c\u91cd\u5ba1\u662f\u624b\u52a8\u4e14\u975e\u5e38\u8017\u65f6\u7684\u4e8b\u60c5\u3002<\/p>\n
                                    \u8fd9\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u4f8b\u5b50\uff0c\u5b83\u5c55\u793a\u4e86\u4f7f\u7528Delta Live Tables\u53ef\u4ee5\u7b80\u5316\u6570\u636e\u5de5\u7a0b\u5e08\u7684\u5de5\u4f5c\u4f53\u9a8c\u3002\u5982\u679c\u6570\u636e\u5de5\u7a0b\u5e08\u548c\u5206\u6790\u5e08\u81ea\u5df1\u521b\u5efa\u548c\u7ba1\u7406\u8fd9\u6837\u4e00\u4e2a\u7cbe\u7ec6\u7684\u6570\u636e\u7ba1\u9053\uff0c\u5c06\u9700\u8981\u6570\u767e\u5c0f\u65f6\u7684\u65f6\u95f4\u3002\u4f46\u662f\u901a\u8fc7\u4f7f\u7528Delta Live Tables\uff0c\u8fd9\u4e00\u8fc7\u7a0b\u53d8\u5f97\u7b80\u5355\u4e86\u3002<\/p>\n
                                    \u6700\u7ec8\uff0c\u901a\u8fc7Delta Live Tables\uff0cUplift\u80fd\u591f\u5728\u4e0d\u5fc5\u62c5\u5fc3\u7ba1\u7406\u4ee3\u7801\u548c\u4e2a\u522b\u6570\u636e\u6e90\u7684\u60c5\u51b5\u4e0b\uff0c\u4e13\u6ce8\u4e8e\u4e3a\u5176\u5408\u4f5c\u4f19\u4f34\u63d0\u4f9b\u66f4\u667a\u80fd\u548c\u6709\u6548\u7684\u4ea7\u54c1\u3002<\/p>\n
                                    \u514d\u8d39\u8bd5\u7528 Databricks<\/h3>\n
                                    \u6570\u636e\u6e56 \u6570\u636e\u539f\u578b<\/p>\n","protected":false},"excerpt":{"rendered":"
                                    \u5982\u4f55\u901a\u8fc7Databricks Delta\u5b9e\u65f6\u8868\u63d0\u5347 CDC \u6570\u636e\u7ba1\u9053\u7684\u89c4\u6a21 – Databrick […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-47000","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nUplift\u5982\u4f55\u4f7f\u7528Databricks Delta Live Tables\u6765\u6784\u5efaCDC\u548c\u591a\u8def\u6570\u636e\u6d41\uff1f - Blog - Silicon Cloud<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.silicloud.com\/zh\/blog\/uplift\u5982\u4f55\u4f7f\u7528databricks-delta-live-tables\u6765\u6784\u5efacdc\u548c\u591a\u8def\u6570\u636e\u6d41\uff1f\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uplift\u5982\u4f55\u4f7f\u7528Databricks Delta Live Tables\u6765\u6784\u5efaCDC\u548c\u591a\u8def\u6570\u636e\u6d41\uff1f\" \/>\n<meta property=\"og:description\" content=\"\u5982\u4f55\u901a\u8fc7Databricks Delta\u5b9e\u65f6\u8868\u63d0\u5347 CDC \u6570\u636e\u7ba1\u9053\u7684\u89c4\u6a21 – Databrick […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/zh\/blog\/uplift\u5982\u4f55\u4f7f\u7528databricks-delta-live-tables\u6765\u6784\u5efacdc\u548c\u591a\u8def\u6570\u636e\u6d41\uff1f\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - 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