I am creating a Power BI report using data from https://www.mohfw.gov.in/ website which provides latest corona virus data for all Indian states/union territories.
Data is in below format -
+-----+-----------------------------+-----------+-------+-------+
| SNo | State | Confirmed | Cured | Death |
+-----+-----------------------------+-----------+-------+-------+
| 1 | Andaman and Nicobar Islands | 14 | 11 | 0 |
| 2 | Andhra Pradesh | 603 | 42 | 15 |
| 3 | Arunachal Pradesh | 1 | 0 | 0 |
| 4 | Assam | 35 | 12 | 1 |
| 5 | Bihar | 86 | 37 | 2 |
They website is refreshed with new data everyday, so there is no date wise tracker. I wanted to track the day wise change(increment/decrements) in cases for every state, is there any way I can model it in power BI to achieve this?
For now what I am doing is I am downloading the table from the web page everyday and adding a date column which will be today's date(getdate()) and loading the data into a SQL table. So everyday I am inserting a row for each of the state with that day's date-stamp in the table and then I can subtract it from previous day's data to see the changes, but I feel it is a inefficient way and the table size keep on increasing everyday.
So any suggestion to improve it, either by some changes in Power BI data model, or in SQL will be much appreciated.
Context
considering the data source is updated according to SCD 1 (Overwriting) the only way to track day wise change is to historize data every day. In practice, schedule a daily load of the data source and store the new data of that day.
Answer
You are implementing SCD 2 (Create a new record on change) in the correct way. It is important to make sure adding a technical field to each record with the timestamp when it was generated so you can study the trend later.
Extra
You could eventually optimize this approach by normalizing the model in order to reduce the size of the table you are applying SCD 2 (Create a new record on change).
Please let me give a simple example. Consider a table with:
only 1 record
1000 fields of which only 1 field (LAST_UPDATE) can change using SCD 2 (Create a new record on change)
If LAST_UPDATE changes 100,000 times a day, every days it triggers the creation of 100,000 new version of the same record (because we track its changes). Therefore, after one year the table would have still 1,000 fields and 36,500,000 records. Instead, if we normalize the model such that LAST_UPDATE field (historized with SCD 2) is stored in a separate table, after one year we would have one table with 1 record and 999 columns, and a different table with 1 column and 36,500,000 records.
In the case your database is a row database, you would much benefit from normalizing the model. Instead, if your database is columnar database, everything is already taken care of because each column is individually compressed instead of compressing row-wise.
Related
I'm trying to setup a PostgreSQL database for a calendar app, and was wondering what would be the preferred way to set up one of the tables.
If I want to have to have multiple users, would the best way to setup the table be:
UserID | Start Time | End Time | Activity |
Or would it be better to do:
User ID | Activity | 8am | 8:30am | 9am | ...| 12am | ... | 7:30am |
The time granularity could be 15 min or 5 min too if that would be the better choice.
The first way would likely be cleaner, but ideally I don't want to let things overlap, but the second way may be more difficult to work with?
Your 1st example is better. But, minimum of 3 tables.
User - you define it.
Timetable: RowID | UserID | Start Time (datetime) | End Time (datetime) | ActivityID
Activity - you define what activity details go in.
We're using U-SQL to extract sensor data from a set of .csv files. Each record contains a sensor ID, time of measurement and value, as well as a time for when the record was received:
+----------+---------------------+------------------+---------------------+
| SensorID | MeasurementTime | MeasurementValue | ReceivedTime |
+----------+---------------------+------------------+---------------------+
| xxx | 2017-09-10 11:00:00 | 12.342 | 2017-09-19 14:25:17 |
| xxx | 2017-09-10 12:00:00 | 14.654 | 2017-09-19 14:25:17 |
| yyy | 2017-09-10 11:00:00 | 1.054 | 2017-09-19 14:25:17 |
| yyy | 2017-09-10 12:00:00 | 1.354 | 2017-09-19 14:25:17 |
...
| xxx | 2017-09-10 11:00:00 | 10.261 | 2017-09-19 15:25:17 |
+----------+---------------------+------------------+---------------------+
The files are stored in ADLS in a path based on the date-portion of the measurement time, so the data seen above would be found in /Data/2017/09/10/measurements.csv, where the first four rows were written at 14:25:17 on the 19th of September, and the last row was appended one hour later, at 15:25:17.
As the above example illustrates, new values for the same SensorID and MeasurementTime can be received at a later time. Each partition holds a few million rows, with a few thousand rows being appended to a small number of partitions every day. We want to run a batch job say every 24 hours, that will output only the newest values, for any given SensorID and MeasurementTime. For this, we use a U-SQL script that looks similar to this:
#newestMeasurements_addRN =
SELECT *,
ROW_NUMBER() OVER (PARTITION BY PDate,
SensorId,
MeasurementTime
ORDER BY ReceivedTime DESC) AS MeasurementRN;
#newestMeasurements =
SELECT SensorId,
MeasurementTime,
MeasurementValue
FROM #newestMeasurements_addRN
WHERE MeasurementRN == 1;
Here, PDate is a virtual column inferred from the yyyy/MM/dd in the path of the CSV file (equals the date-portion of MeasurementTime).
Now, since we use PDate in the PARTITION BY part of the window function, I expected that this operation could be parallelised, since we don't have to consider different days (partitions) when trying to find the newest record for any given SensorID and MeasurementTime. Unfortunately, that does not seem to be the case, looking at a job graph:
Here, we are extracting data from 4 different days. Each of the Extract vertices outputs the full number of records, leaving the task of identifying only the newest records to the Combine vertex at the bottom, indicating that the ROW_NUMBER and subsequent filtering does not happen in parallel.
Is this a bug in the implementation of ROW_NUMBER?
Is there a different U-SQL technique we can use to ensure parallelism?
I managed to find a usable solution, in which I encapsulated the U-SQL that detects the latest measurements inside a U-SQL stored proc, which takes a value corresponding to pdate as input parameter.
Then, I simply execute this stored proc several times, with a list of dates that I want to process in parallel:
DetectLatestMeasurements(20170910);
DetectLatestMeasurements(20170911);
DetectLatestMeasurements(20170912);
DetectLatestMeasurements(20170913);
The stored proc handles EXTRACT, transformation and OUTPUT of one days worth of data, so this does the job, and it is parallelised the way I expect.
How to design database for tourism company to calculate cost of flight and hotel per every program tour based on date ?
what i do is
Table - program
+-----------+-------------+
| ProgramID | ProgramName |
+-----------+-------------+
| 1 | Alexia |
| 2 | Amon |
| 3 | Sfinx |
+-----------+-------------+
every program have more duration may be 8 days or 15 days only
it have two periods only 8 days or 15 days .
so that i do duration program table have one to many with program .
Table - ProgramDuration
+------------+-----------+---------------+
| DurationNo | programID | Duration |
+------------+-----------+---------------+
| 1 | 1 | 8 for Alexia |
| 2 | 1 | 15 for Alexia |
+------------+-----------+---------------+
And same thing to program amon program and sfinx program 8 and 15 .
every program 8 or 15 have fixed details for every day as following :
Table Duration Details
+------+--------+--------------------+-------------------+
| Days | Hotel | Flight | transfers |
+------+--------+--------------------+-------------------+
| Day1 | Hilton | amsterdam to luxor | airport to hotel |
| Day2 | Hilton | | AbuSimple musuem |
| Day3 | Hilton | | |
| Day4 | Hilton | | |
| Day5 | Hilton | Luxor to amsterdam | |
+------+--------+--------------------+-------------------+
every program determine starting by flight date so that
if flight date is 25/06/2017 for program alexia 8 days it will be as following
+------------+-------+--------+----------+
| Date | Hotel | Flight | Transfer |
+------------+-------+--------+----------+
| 25/06/2017 | 25 | 500 | 20 |
| 26/06/2017 | 25 | | 55 |
| 27/06/2017 | 25 | | |
| 28/06/2017 | 25 | | |
| 29/06/2017 | 25 | 500 | |
+------------+-------+--------+----------+
And this is actually what i need how to make relations ship to join costs with program .
for flight and hotel costs as above ?
for 5 days cost will be 1200
25 is cost per day for hotel Hilton
500 is cost for flight
20 and 55 is cost per transfers
image display what i need
relation between duration and cost
Truthfully, I don't fully understand exactly what you're trying to accomplish. Your description is not clear, your tables seem to be missing information / contain information that should not be in your tables, and the way that I'm understanding your description doesn't really make sense based on the UI screenshot that you shared.
It looks like you're working on an application for a travel agency which will allow agents to create an itinerary for a trip. They can give this trip a name (so if a particular package is a hit with customers, they can just offer the "Alexa" package), and the utility will calculate the total estimated cost of the trip. If I understand correctly, the trips will be either 8, or 15 days long.
Personally, I would delete the "ProgramDuration" table altogether. If there are two versions of the Alexa trip at index 1, then you're going to run into all manners of issues. I can get into the details of why this is a bad idea, but unless you're really hung up on having this ProgramDuration table, it's not worth the time. You should add a "duration" field to your "program" table, and assign a new ProgramID for each different duration version of the "Alexa" program.
Your table "Duration details" also misses the mark. Your fields in this table will make it harder to add new features to your application down the line. You should have a field "ProgramID," which we will use to join this table against the program table later. You should have a field "Day" which obviously indicates the day in the itinerary. You should have only one more field "ItemID." We're going to use the "ItemID" field to join your itinerary against a new items table we're going to create.
Your items table is where you define all of the items that can possibly appear in an itinerary. Your current itinerary table has three possible "types" of expenses, flights, hotels, and transfers. What if your travel agents want to start adding meal expenditures into their itineraries / budgets? What about activities that cost money? What about currency exchange fees? What about items that your clientele will need before their trip (wall adapters, luggage, etc.)? In your items table, you will have fields for an ItemID, ItemName, ItemUnitPrice, and ItemType. A possible item is as follows:
ItemID: 1, ItemName: Night At The Hilton, ItemUnitPrice: 300, ItemType: Lodging
Using the "SELECT [Column] AS [Alias]" syntax with some CTEs or subqueries and the JOIN operator, we can easily reconstitute a table that looks like your "Program Duration Details" table, but we will be afforded considerably more flexibility to add or remove things later down the line.
In the interests of security and programmability, I would also add a table called "ItemTypeTable" with a single field "TypeName." You can use this table to prevent unauthorized users from defining new item types, and you can use this table to create drop down menus, navigation, and all manners of other useful features. There might be cleaner implementations, but this shouldn't represent a serious performance or size hit.
All in all, at the risk of being somewhat rude, it seems like you're trying to take on a rather large, sophisticated task with a very rudimentary understanding of basic relational database design and implementation. If you are doing this in a professional context, I would strongly encourage you to consider consulting with another professional that may be more experienced in this area.
My data is like -
+-----------+------------------+-----------------+-------------+
| Issue Num | Created On | Closed at | Issue Owner |
+-----------+------------------+-----------------+-------------+
| 1 | 12/21/2016 15:26 | 1/13/2017 9:48 | Name 1 |
| 2 | 1/10/2017 7:38 | 1/13/2017 9:08 | Name 2 |
| 3 | 1/13/2017 8:57 | 1/13/2017 8:58 | Name 2 |
| 4 | 12/20/2016 20:30 | 1/13/2017 5:46 | Name 2 |
| 5 | 12/21/2016 19:30 | 1/13/2017 1:14 | Name 1 |
| 6 | 12/20/2016 20:30 | 1/12/2017 9:11 | Name 1 |
| 7 | 1/9/2017 17:44 | 1/12/2017 1:52 | Name 1 |
| 8 | 12/21/2016 19:36 | 1/11/2017 16:59 | Name 1 |
| 9 | 12/20/2016 19:54 | 1/11/2017 15:45 | Name 1 |
+-----------+------------------+-----------------+-------------+
What I am trying to achieve is
Number of issues created per week
Number of issues closed per week
Net number of issues remaining per week
I am able to resolve the top two points but unable to approach the last.
My attempt -
This gives me number of issues created every week.
Similarly I have done for Closed per week.
For Net number of issues (Created-Closed) -
I tried adding Closed At column along with Created On but I can't see second bar in the chart along with Created On either.
Something like this
I tried doing the same in excel -
I want something of this sort but with another column as the difference of
number of issues created that week - number of issues closed that week.
In this case, 8-6=2.
You could use a calculated field(Analysis->Create Calculated Field). Something like this:
{FIXED [Create Date]:Count(if DATEPART('year',[Create Date]) = 2016 then [Number of Records] end)} - {FIXED [Closed Date]:Count(if DATEPART('year',[Closed Date]) = 2016 then [Number of Records] end)}
This function is using LOD expressions to pull back both sets of values. It will filter on all 2016 results for both date sets and then minus them from each other.
For more on LOD's see here:
https://www.tableau.com/about/blog/LOD-expressions
Use this as your measure and pull in one of your date fields as the dimension.
The normal way to solve this problem is to reshape the data so you have one row per status change instead of one row per issue, with a column named [Date] and a column named [Action]. The action can be submit and close (or in a more complex world include approve, reject, whatever - tracking the history.
You can do the reshaping without modifying your source data by using a UNION to get two copies of each row with appropriate calculated fields to make the visible columns make sense (e.g., create calculated a field called Date that returns the submission date or closing date depending on whether the row is from the first or second union, with a similar one called Action whose value depends on that as well. Filter out Close actions that have a null date)
Or you can preprocess the data to reshape it.
Or you can use data blending to make two sources that point to the same data source but customizing the linking fields to line up the submit and close dates (e.g., duplicate the data connection and rename both date fields to have the same name). But in this case, you probably want to create scaffolding source that has every date, but no other data, to use as the primary data source to avoid filtering out data from the secondary for dates that don't appear in the primary. The blending approach can be brittle.
Assuming you used the UNION approach instead of Data Blending, then you can count the number of submissions and closures within a certain date range, or compute a running total of the difference to see the backlog size over time.
I'm going to do my best to explain this so I apologize in advance if my explanation is a little awkward. If I am foggy somewhere, please tell me what would help you out.
I have a table filled with circuits and dates. Each circuit gets trimmed on a time cycle of about 36 months or 48 months. I have a column that gives me this info. I have one record for every time the a circuit's trim cycle has been completed. I am attempting to link a known circuit outage list, to a table with their outage data, to a table with the circuit's trim history. The twist is the following:
I only want to get back circuits that have exceeded their trim cycles by 6 months. So I would need to take all records for a circuit, look at each individual record, find the most recent previous record relative to the record currently being examined (I will need every record examined invididually), calculate the difference between the two records in months, then return only the records that exceeded 6 months of difference between any two entries for a given feeder.
Here is an example of the data:
+----+--------+----------+-------+
| ID | feeder | comp | cycle |
| 1 | 123456 | 1/1/2001 | 36 |
| 2 | 123456 | 1/1/2004 | 36 |
| 3 | 123456 | 7/1/2007 | 36 |
| 4 | 123456 | 3/1/2011 | 36 |
| 5 | 123456 | 1/1/2014 | 36 |
+----+--------+----------+-------+
Here is an example of the result set I would want (please note: cycle can vary by circuit, so the value in the cycle column needs to be in the calculation to determine if I exceeded the cycle by 6 months between trimmings):
+----+--------+----------+-------+
| ID | feeder | comp | cycle |
| 3 | 123456 | 7/1/2007 | 36 |
| 4 | 123456 | 3/1/2011 | 36 |
+----+--------+----------+-------+
This is the query I started but I'm failing really hard at determining how to make the date calculations correctly:
SELECT temp_feederList.Feeder, Temp_outagesInfo.causeType, Temp_outagesInfo.StormNameThunder, Temp_outagesInfo.deviceGroup, Temp_outagesInfo.beginTime, tbl_Trim_History.COMP, tbl_Trim_History.CYCLE
FROM (temp_feederList
LEFT JOIN Temp_outagesInfo ON temp_feederList.Feeder = Temp_outagesInfo.Feeder)
LEFT JOIN tbl_Trim_History ON Temp_outagesInfo.Feeder = tbl_Trim_History.CIRCUIT_ID;
I wasn't really able to figure out where I need to go from here to get that most recent entry and perform the mathematical comparison. I've never been asked to do SQL this complex before, so I want to thank all of you for your patience and any assistance you're willing to lend.
I'm making some assumptions, but this uses a subquery to give you rows in the feeder list where the previous completed date was greater than the number of months ago indicated by the cycle:
SELECT tbl_Trim_History.ID, tbl_Trim_History.feeder,
tbl_Trim_History.comp, tbl_Trim_History.cycle
FROM tbl_Trim_History
WHERE tbl_Trim_History.comp>
(SELECT Max(DateAdd("m", tbl_Trim_History.cycle, comp))
FROM tbl_Trim_History T2
WHERE T2.feeder = tbl_Trim_History.feeder AND
T2.comp < tbl_Trim_History.comp)
If you needed to check for longer than 36 months you could add an arbitrary value to the months calculated by the DateAdd function.
Also I don't know if the value of cycle specified the number of month from the prior cycle or the number of months to the next one. If the latter I would change tbl_Trim_History.cycle in the DateAdd function to just cycle.
SELECT tbl_trim_history.ID, tbl_trim_history.Feeder,
tbl_trim_history.Comp, tbl_trim_history.Cycle,
(select max(comp) from tbl_trim_history T
where T.feeder=tbl_trim_history.feeder and
t.comp<tbl_trim_history.comp) AS PriorComp,
IIf(DateDiff("m",[priorcomp],[comp])>36,"x") AS [Select]
FROM tbl_trim_history;
This query identifies (with an X in the last column) the records from tbl_trim_history that exceed the cycle time - but as noted in the comments I'm not entirely sure if this is what you need or not, or how to incorporate the other 2 tables. Once you see what it is doing you can modify it to only keep the records you need.