how can I see data for last month in Fabric Answers? Or is it just for last 30 days overview
I looked all documents on Fabric Answers website, blog and read almost every question here, but I have seen only data for 1 day, last 7 days or last 30 days.
Fabric/Firebase here - it depends what you're looking for. If you're talking about the active user categories, the dashboard will show you Daily Active Users (1 day), Weekly Active Users (7 days), or Monthly Active Users (30 days). If you just want Answers data that's more than 30 days old in general, you can actually export up to a year's worth of data directly from the Fabric dashboard for many different metrics. Check out this link for more information: https://docs.fabric.io/android/answers/export-data.html. Hope that answers your question.
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I’m a data analyst in the insurance industry and we currently have a program in SAS EG that tracks catastrophe development week by week since the start of the event for all of the catastrophic events that are reported.
(I.E week 1 is catastrophe start date + 7 days, week 2 would be end of week 1 + 7 days and so on) then all transaction amounts (dollars) for the specific catastrophes would be grouped into the respective weeks based on the date each transaction was made.
Problem that we’re faced with is we are moving away from SAS EG to GCP big query and the current process of calculating those weeks is a manually read in list which isn’t very efficient and not easily translated to BigQuery.
Curious if anybody has an idea that would allow me to calculate each week number in periods of 7 days since the start of an event in SQL or has an idea specific for BigQuery? There would be different start dates for each event.
It is complex, I know and I’m willing to give more explanation as needed. Open to any ideas for this as I haven’t been able to find anything.
We're having trouble understanding the numbers in the daily summary emails we get from Fabric.
A search on SO only shows one somewhat-related question/answer.
Here are the emails from 2 consecutive days:
Our questions are:
Does “Monthly Active” mean over the last 30 days? If so, how can there be a 36% drop in 1 day if the counts went from 101 to 93 (an 8% drop)?
Why does “Daily Active” show a 75% drop if the current day is 1 and the previous day was 0?
Why does “Total Sessions” show a 94% drop if the current day is 1 and the previous day was 0?
Does the “Time in App per User” mean the average for the month or for the prior day? If it's for the month, why would 1 extra session cause the value to change so much? If it's for the day, why does it show “11:33m” even though the Total Sessions was 0?
Sometimes the “Time in App per User” ends in an “m” and sometimes it ends in an “s”. For example, “11:33m” and “0:44s”. Does that mean that “11:33m” is “11 hours and 33 minutes” and “0:44s” is “0 minutes and 44 seconds”? Or does the “11:33m” still mean “11 minutes and 33 seconds” and I should ignore the suffix?
Thanks for reaching out. Todd from Fabric here. The % change is actually % difference vs. what we expected based on the previous behavior of your app. This compensates for day of week etc.
The long session when getting zero, suggests that either the session was live/not reported to us at UTC midnight. The session gets created on session start and the duration gets set at the end.
Thanks!
For years I've been using webpage requests like the following to retrieve 20 days at a time of minutewise stock data from Google:
http://www.google.com/finance/getprices?q=.INX&i=60&p=20d&f=d,c,h,l,o,v
= Retrieve for .INX (S&P 500 index) 60-second interval data for the last 20 days, with format Datetime(in Unix format), Close, High, Low, Open, Volume.
The Datetime is in Unix format (seconds since 1/1/1970, prefixed with an "A") for the first entry of each day, and subsequent entries show the intervals that have passed (so 1 = 60 seconds after the opening of the market that day).
That worked up until 9/10/2017, but today (9/17) it only returns day-end data (it even reports the "interval" between samples as 86400). Pooey! I can get that anywhere, in bulk.
But if I ask for fewer days, or broader intervals, it seems to return data - but weird data. Asking for data every 120 seconds returns exactly that - but only for every other market day. Weird!
Has anyone got a clue what might have happened?
Whoa! I think I figured it out.
Google still returns minutewise data for the same approximate limitations (up to 20 calendar days), but instead of d=10 returning all the market data for the last 10 calendar days, it return the data for the last 10 market days. Previously, to get the last 10 market days you would ask for d=14 (M-Fx2, plus two weekends). Now, Google interprets the d variable as market days, and asking for d=20 exceeds the limits on what they will deliver.
It now appears that d=15 is the limit (three weeks of market days). No clue on why I got the very weird every-other-day data for a while... but maybe if you exceed their d-limits the intervals get screwy. Dunno. Don't care. Easy fix.
I used to have a number of queries running on the past 40 days of data using a decorator with [dataset.table#-4123456789-].
However, since September 15 all the decorators return maximum 10 days of data.
By the way [dataset.table#0] returns the whole table and not the past 7 days as told in the documentation.
Does anyone know what is going on. Do I have to move my table to partition in order to receive data for a limited period of time but more the a week?
Thanks
So I'm developing a database for an agency that manages many relief staff.
Relief workers set their availability for each day in one of three categories (day, evening, night).
We also need to be able to set some part-time relief workers as busy on weekly, biweekly, and in one instance, on a 9-week rotation. Since we're already developing recurring patterns of availability here, we might as well also give the relief workers the option of setting recurring availability days.
We also need to be able to query the database, and determine if an employee is available for a given day.
But here's the gotcha - we need to be able to use change data capture. So I'm not sure if calculating availability is the best option.
My SQL prototype table looks like this:
TABLE Availability Day
employee_id_fk | workday (DATETIME) | day | eve | night (all booleans)| worksite_code_fk (can be null)
I'm really struggling how to wrap my head around recurring events. I could create say, a years worth, of availability days following a pattern in 'x' day cycle. But how far ahead of time do we store information? I can see running into problems when we reach the end of the data set.
I was thinking of storing say, 6 months of information, then adding a server side task that runs monthly to keep the tables updated with 6 months of data, but my intuition is telling me this is a bad fix.
For absolutely flexibility in the future and keeping data from bloating my first thought would be something like
Calendar Dimension Table - Make it for like 100 years or Whatever you Want make it include day of week information etc.
Time Dimension Table - Hour, Minutes, every 15 what ever but only for 24 hour period
Shifts Table - 1 record per shift e.g. Day, Evening, and Night
Specific Availability Table - Relationship to Calendar & Time with Start & Stops recommend 1 record per day so even if they choose a range of 7 days split that to 1 record perday and 1 record per shift.
Recurring Availability Table - for day of week (1-7),Month,WeekOfYear, whatever you can think of. But again I am thinking 1 record per value so if they are available Mondays and Tuesday's that would be 2 rows. and if multiple shifts then it would be multiple rows.
Now and here is the perhaps the weird part, I would put a Available Column on the Specific and Recurring Availability Tables, maybe make it a tiny int and store something like 0 not available, 1 available, 2 maybe available, 3 available with notice.
If you want to take into account Availability with Notice you could add columns for that too such as x # of days. If you want full flexibility maybe that becomes a related table too.
The queries would be complex but you could use a stored procedure or a table valued function to handle it fairly routinely.