Coronavirus 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.

Data Provided by:
SRK, Data Scientist at H2O.ai, Chennai, India