Panoply.io for Database Warehousing and Post Analysis using Sequal Language (SQL)

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool. I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment. The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll. Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries. Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths. Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.如今，使用任意结构化查询语言（Structured Query Language，简称SQL）解决各类数据库相关问题已前所未有的便捷。下文将助您了解我如何借助Panoply.io工具，梳理出数据库间的部分关联关系。本次操作中，我成功导入新冠数据集，并生成了可提交、可复用的分析结果。希望本内容能为您在数据仓库（Data Warehouse）环境中的工作提供助力。以下为附加数据集上执行的SQL命令列表，最终结果存储于导出文件夹（Exports Folder）： 查询1：SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 描述：我们该如何估算新冠病毒已渗透，但患者已实现有效康复的地区？可通过筛选康复人数两倍于死亡人数的区域，直观查看此类地区。 查询2：SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country 描述：新冠疫情已蔓延至多个国家，掌握存在确诊新冠病例的国家名单，是做好防疫的前提。以下即为符合条件的国家列表。 查询3：SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country 描述：新冠疫情已蔓延至多个国家，掌握存在确诊新冠病例的国家名单，是做好防疫的前提。以下即为符合条件的国家列表。 查询4：SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC 描述：新冠病毒正以惊人速度蔓延。明确哪些国家首次出现病毒感染风险至关重要——此类国家若及时采取防疫措施，可有效避免人员伤亡。以下为无新冠相关死亡病例的疑似病例国家列表。 查询5：SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC 描述：新冠疫情在部分国家呈加剧态势，我们该如何量化这一态势？可通过统计无新冠相关死亡病例的国家中，疑似病例占全球总疑似病例的百分比，来衡量疫情扩散程度。以下为相关统计列表。