探索大数据实战:十大经典案例揭示其威力

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"大数据十大经典案例ppt课件.ppt"是一份深入浅出地介绍大数据概念及其实际应用的PPT文件。该课程旨在通过十个生动的案例,帮助读者理解和体验大数据在日常生活、商业运营和政策决策中的影响力。以下是部分案例的详细解读: 1. 啤酒与尿布的经典关联:这是最早被广泛提及的大数据应用之一,沃尔玛通过对消费者购物数据的分析发现,男性顾客在购买尿布时可能会顺带购买啤酒,这一看似偶然的观察揭示了消费者行为的关联性。通过将两种商品放在一起销售,商家实现了销售增长。 2. 数据新闻与伊拉克战争:《卫报》利用维基解密提供的大量数据制作了一篇新闻报道,展示了伊拉克战争中的人员伤亡情况,通过可视化呈现,触动公众情绪,最终影响了政府决策,促使英国撤军。 3. 淘宝大数据揭示消费趋势:通过分析淘宝平台的销售数据,展示了不同地区女性消费者的文胸尺寸和颜色偏好,这不仅反映了时尚潮流,也体现了地域文化差异。 4. QQ圈子的人际网络挖掘:腾讯的社交网络产品QQ圈子利用大数据技术,分析用户的社交关系,能够实现精准的个性化推荐,如将前女友的信息推送给用户的现任伴侣,展现了人际关系的复杂性和数据的价值。 5. "魔镜"预测石油市场:这个大数据可视化工具不仅能识别个体间的美丑比较,更在现实中应用于金融领域,通过整合和分析市场数据,预测石油市场动态,帮助企业做出更精确的决策。 6. Google流感趋势预测:谷歌通过分析搜索关键词与流感报告之间的关联,构建模型预测流感的季节性传播,这种非传统数据源的运用展示了大数据在公共卫生领域的潜力。 这些案例展示了大数据如何通过分析海量数据来揭示隐藏的模式、趋势和洞察,不仅改变着商业策略,也影响着社会决策和个人生活。学习和理解这些案例,有助于人们更好地认识和利用大数据的力量。

SELECT PIS.SHOW_FLT_DETAIL AS SHOW_FLT_DETAIL -- new , PIS.SHOW_AWB_DETAIL AS SHOW_AWB_DETAIL -- new , PIS.DISPLAY_AIRLINE_CODE AS CARRIER_CODE , DECODE(PIS.REVERT_FLOW,'N',PIS.FLOW_TYPE,DECODE(PIS.FLOW_TYPE,'I','E','I')) AS FLOW_TYPE , PIS.SHIP_TO_LOCATION AS SHIP_TO_LOCATION , PIS.INVOICE_SEQUENCE AS INVOICE_SEQUENCE , PFT.FLIGHT_DATE AS FLIGHT_DATE , PFT.FLIGHT_CARRIER_CODE AS FLIGHT_CARRIER_CODE , PFT.FLIGHT_SERIAL_NUMBER AS FLIGHT_SERIAL_NUMBER , PFT.FLOW_TYPE AS AIRCRAFT_FLOW , FAST.AIRCRAFT_SERVICE_TYPE AS AIRCRAFT_SERVICE_TYPE , PPT.AWB_NUMBER AS AWB_NUMBER , PPT.WEIGHT AS WEIGHT , PPT.CARGO_HANDLING_OPERATOR AS CARGO_HANDLING_OPERATOR , PPT.SHIPMENT_PACKING_TYPE AS SHIPMENT_PACKING_TYPE , PPT.SHIPMENT_FLOW_TYPE AS SHIPMENT_FLOW_TYPE , PPT.SHIPMENT_BUILD_TYPE AS SHIPMENT_BUILD_TYPE , PPT.SHIPMENT_CARGO_TYPE AS SHIPMENT_CARGO_TYPE , PPT.REVENUE_TYPE AS REVENUE_TYPE , PFT.JV_FLIGHT_CARRIER_CODE AS JV_FLIGHT_CARRIER_CODE , PPT.PORT_TONNAGE_UID AS PORT_TONNAGE_UID , PPT.AWB_UID AS AWB_UID , PIS.INVOICE_SEPARATION_UID AS INVOICE_SEPARATION_UID , PFT.FLIGHT_TONNAGE_UID AS FLIGHT_TONNAGE_UID FROM PN_FLT_TONNAGES PFT , FZ_AIRLINES FA , PN_TONNAGE_FLT_PORTS PTFP , PN_PORT_TONNAGES PPT , FF_AIRCRAFT_SERVICE_TYPES FAST , SR_PN_INVOICE_SEPARATIONS PIS --new , SR_PN_INVOICE_SEP_DETAILS PISD--new , SR_PN_INV_SEP_PORT_TONNAGES PISPT --new WHERE PFT.FLIGHT_OPERATION_DATE >= trunc( CASE :rundate WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN ADD_MONTHS(SYSDATE,-1) ELSE ADD_MONTHS(:rundate,-1) END, 'MON') AND PFT.FLIGHT_OPERATION_DATE < trunc( CASE :rundate WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN TRUNC(SYSDATE) ELSE TRUNC(:rundate) END, 'MON') AND PFT.TYPE IN ('C', 'F') AND PFT.RECORD_TYPE = 'M' AND (PFT.TERMINAL_OPERATOR NOT IN ('X', 'A') OR (PFT.TERMINAL_OPERATOR <> 'X' AND FA.CARRIER_CODE IN (SELECT * FROM SPECIAL_HANDLING_AIRLINE) AND PPT.REVENUE_TYPE IN (SELECT * FROM SPECIAL_REVENUE_TYPE) AND PPT.SHIPMENT_FLOW_TYPE IN (SELECT * FROM SPECIAL_SHIPMENT_FLOW_TYPE) AND PFT.FLIGHT_OPERATION_DATE >= (select EFF_DATE from SPECIAL_HANDLING_EFF_DATE) )) AND PFT.DELETING_DATETIME IS NULL AND FA.AIRLINE_UID = PFT.AIRLINE_UID AND FA.DELETING_DATETIME IS NULL AND PTFP.FLIGHT_TONNAGE_UID = PFT.FLIGHT_TONNAGE_UID AND PTFP.RECORD_TYPE = 'M' AND PTFP.DELETING_DATETIME IS NULL AND PPT.TONNAGE_FLIGHT_PORT_UID (+)= PTFP.TONNAGE_FLIGHT_PORT_UID AND PPT.RECORD_TYPE (+)= 'M' AND PPT.DISCREPANCY_TYPE (+)= 'NONE' AND PPT.ADJUSTMENT_INC_FLAG (+)= 'Y' AND PPT.DELETING_DATETIME (+) IS NULL AND FAST.AIRCRAFT_SERVICE_TYPE_UID = PFT.AIRCRAFT_SERVICE_TYPE_UID AND FAST.DELETING_DATETIME IS NULL AND PIS.TEMPORAL_NAME = TO_CHAR((CASE :rundate --new WHEN TO_DATE('01/01/1900', 'DD/MM/YYYY') THEN TRUNC(SYSDATE) ELSE TRUNC(:rundate) END ), 'YYYYMM') || '00' AND PIS.INVOICE_SEPARATION_UID = PISD.INVOICE_SEPARATION_UID --new AND PISD.INVOICE_SEP_DETAIL_UID = PISPT.INVOICE_SEP_DETAIL_UID --new AND PISPT.PORT_TONNAGE_UID = PPT.PORT_TONNAGE_UID --new AND PIS.PRINT_SUPPORTING_DOC = 'Y';上面是oracle的写法,请转成spark SQL的写法。

2023-06-02 上传