PowerBI – Resource Profiles from P6

One of the biggest critiques/limitations in using P6 data is the lack of time phased resource assignment data. The only effective way to pull time phased resource assignment data into PowerBI (or even excel ) is to copy-paste from P6 into Excel. This is what I have recommended in the past and still what I would recommend for anyone moving forward. However, that does not mean that PowerBI can’t produce time phased data using a start date, end date, and profile. What follows is a simply guide on how I have tackled the problem (and the limitations I have run into)

One of the biggest critiques/limitations in using P6 data is the lack of time phased resource assignment data. The only effective way to pull time phased resource assignment data into PowerBI (or even excel ) is to copy-paste from P6 into Excel. This is what I have recommended in the past and still what I would recommend for anyone moving forward. However, that does not mean that PowerBI can’t produce time phased data using a start date, end date, and profile. What follows is a simply guide on how I have tackled the problem (and the limitations I have run into)

Note: In all likelihood this problem has already been solved my many people in many different ways. I do not want to suggest this is “the way” to solve this. More so, I want to simply raise awareness of at least my approach and welcome comments and feedback on how to really solve the problem

Part 1 The Problem

P6 data does not contain time phased distributions. Instead, the backend (and inside XER) data only contains information about the activity, the resource, and the profile applied. So, our problem is to try to extract these data elements and generate a time phased distribution of the resource according to the profile and activity start and end dates.

Typical Activity Level Data
Here we have our Resource Details (including our “curv_id)
Resource Profile Data

I’ve taken a few liberties with the data above to try to focus on the:

Key Problem: how to allocate the resources assigned to an activity according to a resource profile?

I’ve seen this done in excel a lot although, i’ve never been comfortable with the excel solutions. Typically we count the weeks and distribute the hours equally to all the weeks. I know more complex files exist that allow for spreads using profiles. So to add to the problem isn’t nessessarily to simply spread per the profile, but to perform the operation inside PowerBI (or perhaps more to the point, inside Power Query).

At this point, I’d again love to call upon anyone who has a nice solution to include links about how you tackled this as what follows is just my initial stab at this.

Part 2: Getting the data into PowerBI

Firstly, although the data above is from a P6 XER file, I wanted to make this a bit more general. Therefore, I have created a toy model approach. Thus, here is what my source data looks like

Resource Profile Data:

Excel Profiles

We can run this through PowerQuery and convert it to a usable table. Note in the above I have created a PeriodCum field. This will be used to calculate an end date for each of the 10 periods required.


Activity and Resource Data:

In this example, I am combining the Activity and Resource data into just 1 table. Obviously if you were doing this formally, you would need to build a scheme to link the Resource Assignment data into the Activity level data

Excel Data

And running the above through Power Query we end up with something as seen below. Note I have added a calculation for the duration (in cal day) and have converted the date formats to numbers. This make the subsequent steps a little easier

Note: a critical hard step (for me at least) came duration this stage. Because we are breaking the duration into 10 periods and will ultimately be allocating a qnty per day to each each, if we have a fraction of a day (example a duration of 15 days), my method bombed. This caused an overlap of qnty allocation on the day of the overlap. As such, I have rounded the duration to the closest 10s.


Part 3: Time Phasing (where the magic happens)

The first step of generating the time phasing is to now split the activities into the 10 periods per the resource allocation. We do this by first merging tbl_activies with tbl_profiles using the ProfileDesc field. After expanding the result, we will end up with 10 records for each activity (corresponding to each of the 10 periods).

We will now want to calculate a start and finish date unique for each of the 10 periods. In the profile table is a PeriodCum field that we can use to multiply by the duration and then add that to the start date to get a finish for each period. The result table will now look something like this. Note, at this step, its good to now use the profile allocation for each period and multiple that by the hours_total field. This will give us a hours per each period. The last step will be the divide that by the period duration to calculate what will now be an hours per day for that period.

Lastly, we now want to perform 1 additional expansion to get the DAYS for each period. Here is the code I have used. This is a nice little bit of code that can generate a sequential list from a start to an end (we are using days, but its works for any beginning to end sequence)

= Table.AddColumn(#”Changed Type2″, “Custom”, each { Number.From([PeriodStart])..Number.From([PeriodFinish]) })

resulting resource allocation table with profiles applied

In our resulting table (see above), we can clearly see that the hours per day adjusts for each period based on the profile.

Part 4: Putting It All Together

We have our activity data, we have our time phased resource data, the last step is to generate a DIM_Date table that can be used to bin the days to either weeks or months (or quarters or years or any custom grouping defined inside the DIM_Date table)

I don’t want to get to into the DIM_Date table, effectively all we need is each day assigned to a Month-Yr for the purpose of generating a nice little graph below, which is ultimately what we are after.


Again, I do not recommend using this approach. Instead I am more interested in how others have decided to approach this. Personally, as I noted at the beginning, my recommendation is to use a copy-paste from the P6 Resource Assignment tab. Although, this time phasing approach can be used for other (non P6) applications. But alas, I believe there are much smarter ways to achieve the spread using the DIM_Date table and perhaps custom functions. In my research for my method, I ideally wanted a “working day” spread as opposed to the calendar day spread. There are some awesome custom functions that can provide an integer for the number of working days between 2 dates. However, even when taking that approach, I ran into additional complications in getting everything to work.

So, really keen for feedback!


PowerBI Incremental refresh Parquet files, without a Database.

TL;DR, you can incremental refresh PowerBI using Parquet files stored in an Azure Storage without using any Database in the middle, you can download sample pbix here

I am using this blog post by Gilbert Quevauvilliers which is based on a technique from Rafael Mendonça, Please read it first

Maybe read this, it is using Synapse Serverless , but has a section where you can Partition your data using Python to Parquet

1-Add a new Table, Parquet

make sure it is not loaded, here is the M code

     Source = AzureStorage.DataLake("https://xxxxxx.core.windows.net/parquet"),
     #"Removed Other Columns" = Table.SelectColumns(Source,{"Content", "Folder Path"}),
     #"Inserted Text Between Delimiters" = Table.AddColumn(#"Removed Other Columns", "Text Between Delimiters", each Text.BetweenDelimiters([Folder Path], "D", "/", 1, 0), type text),
     #"Renamed Columns" = Table.RenameColumns(#"Inserted Text Between Delimiters",{{"Text Between Delimiters", "Date"}}),
     #"Changed Type" = Table.TransformColumnTypes(#"Renamed Columns",{{"Date", type datetime}}),
     #"Removed Columns" = Table.RemoveColumns(#"Changed Type",{"Folder Path"})
     #"Removed Columns"

here is the result

3-Merge using Inner Join

to read the parquet file content we use this function , notice we used inner join in the previous step to avoid reading null Content, which generate errors when you refresh in the service


and here is the final table

we configure incremental refresh to refresh the Last 2 days

4- Testing in PowerBI Service

as you can see the second refresh is way faster then the First one

here is the partition Table

now let’s check the transaction history from Azure storage, I refreshed again just to be sure

The second refresh read substantially less data as only two files are read

I Think with PowerBI desktop supporting Parquet, we will see more exciting scenarios, I can’t wait for Dataflow to support export to Parquet !!!!

if you are still reading, I appreciate a vote on this idea, Having an option in Dataflow to export to a dynamic file name

Using PowerBI with Azure Synapse Serverless, First Look

Recently I come across a new use case, where I thought Azure Synapse serverless may make sense, if you never heard about it before, here is a very good introduction

TLDR; Interesting new Tool !!!!, will definitely have another serious look when they support cache for the same Queries

Basically a new file arrive daily in an azure storage and needs to be processed and later consumed in PowerBI

The setup is rather easy, here is an example of the user interface, this is not a step by step tutorial, but just my first impression.

I will use AEMO (Australian electricity market Operator) data as an example, the raw data is located here

Load Raw Data

First I load the csv file as it is, I define the columns to be loaded from 1 to 44 , make sure you load only 1 file to experiment then when you are ready you change this line


Then it will load all files, notice when you use filename(), it will add a column with the files name, very handy

USE [test];


result.filename() AS [filename],
        BULK 'https://xxxxxxxx.dfs.core.windows.net/tempdata/PUBLIC_DAILY_201804010000_20180402040501.CSV',
        FORMAT = 'CSV',
    with (
c1   varchar(255),
c2   varchar(255),
c3   varchar(255),
c4   varchar(255),
c5   varchar(255),
c6   varchar(255),
c7   varchar(255),
c8   varchar(255),
c9   varchar(255),
c10   varchar(255),
c11   varchar(255),
c13   varchar(255),
c14   varchar(255),
c15   varchar(255),
c16   varchar(255),
c17   varchar(255),
c18   varchar(255),
c19   varchar(255),
c20   varchar(255),
c21   varchar(255),
c22   varchar(255),
c23   varchar(255),
c24   varchar(255),
c25   varchar(255),
c26   varchar(255),
c27   varchar(255),
c29   varchar(255),
c30   varchar(255),
c31   varchar(255),
c32   varchar(255),
c33   varchar(255),
c34   varchar(255),
c35   varchar(255),
c36   varchar(255),
c37   varchar(255),
c38   varchar(255),
c39   varchar(255),
c40   varchar(255),
c41   varchar(255),
c42   varchar(255),
c43   varchar(255),
c44   varchar(255)
 AS result

The previous Query create a view that read the raw data

Create a View for a Clean Data

As you can imagine , Raw data by itself is not very useful, we will create another view that reference the raw data view and extract a nice table ( in this case the Power generation every 30 minutes)

USE [test];


select [_].[filename] as [filename],
   convert(Datetime,[_].[c5],120) as [SETTLEMENTDATE],
    [_].[c7] as [DUID],
   cast( [_].[c8] as DECIMAL(18, 4)) as [INITIALMW]
from [dbo].[aemo] as [_]
where (([_].[c2] = 'TUNIT' and [_].[c2] is not null) and ([_].[c4] = '1' and [_].[c4] is not null)) and ([_].[c1] = 'D' and [_].[c1] is not null)

Connecting PowerBI

Connecting to azure synapse is extremely easy, PowerBI just see it as a normal SQL server.

here is the M script

Source = Sql.Databases("xxxxxxxxxxx-ondemand.sql.azuresynapse.net"),
test = Source{[Name="test"]}[Data],
dbo_GL_Clean = test{[Schema="dbo",Item="TUNIT"]}[Data]

And the SQL Query generated by PowerQuery ( which Fold)

select [$Table].[filename] as [filename],
[$Table].[DUID] as [DUID],
from [dbo].[TUNIT] as [$Table]

Click refresh and perfect, here is 31 files loaded

Everything went rather smooth, nothing to set up and I have now an Enterprise Grade Data warehouse in Azure, how cool is that !!!

How Much it cost ?

Azure Synapse serverless pricing model is based on how much data is processed

First let’s try with only 1 file ,running Query from the Synapse Workspace, the file is 85 MB, good so far, data processed is 90 MB, file size + some meta Data

now let’s see using the Queries generated by PowerBI, in theory my files size are 300 MB, I will be paying only for 300 MB, let’s have a look at the Metrics

My first reaction was, there must be a bug , 2.4 GB !!!, I refreshed again and it is the same number !!!

A look at the PowerQuery diagnostic and a clear picture emerges, PowerBI SQL Connectors is famous for being “Chatty”, in this case you would expect PowerQuery to send only 1 Query but in reality it will send multiple Queries , at least 1 of them to check the top 1000 rows to define the fields type.

Keep in mind Azure Synapse Serverless has no cache ( they are working on it), so if you run the same query multiple times even with the same data, it will “scan” the files multiple times, and as there is no data statistic a select 1000 rows will read all files even without order by.

Obviously, I was using import mode, as you can imagine using it with directQuery will generate substantially more queries.

Just to be sure I tried to do refresh on the service.

The same, it is still 2.4 GB, I think it is fair to say, there is no way to control how many time PowerQuery send a SQL Query to Synapse.

Edit 17 October 2020 :

I got a feedback that probably my PowerBI desktop was open when I run the test in the service, turn out it is true, I tried again with The desktop closed and it worked as expected, one refresh generate 1 query

Notice even if the CSV file was compressed, it will not make a difference, Azure synapse bill uncompressed data.

Parquet file would made a difference as only columns used would be charged, but I did not want to used another tool in this example.

Take Away

It is an interesting Technology, the integration with Azure cloud storage is straightforward, the setup is easy,you can do transformation using only SQL, Pay only what you use and Microsoft is investing a lot of resources on it.

But the lack of cache is a show stopper !!

I will definitely check it again when they add the cache and cost control, after all it is still in Preview 🙂

Drill Down to Another Page in Google Data Studio

Edit : 28 November 2020, there is an easy way without using parameter

Drill down to another page is a well known technique in BI software, you have a main page with aggregate data, and you can select one category then drill down to another page with more details and keeping the filter selection.

Data Studio does not support this functionality natively but we can simulated using parameter URL

in this example, we will drill down from Country to cities

1- Create a country parameter

2- add a new Page

the first report will show data at the country level, like this

make sure interaction is one

3- Go to Resource, Manage Report URL Parameter

4- Allow to be modified in report URL

tick the option on, you can edit the parameter to remove ds1.drillcountry to drillcountry

5- Build the URL

that’s the main part of the post, create a new calculated field using this forumula


the first part is the second tab URL address , drillcountry is the parameter name as written in step 4, country is the field you are filtering on

6- create a Table

add this field in a table visual and limit the result to 1 row

7-Create a second calculated field to filter using parameter

currently you can’t use parameter in visual filter, instead we will use a dummy filter

8-Filter the visual in the second page

and that’s all, unfortunately as of Sept 2020, the URL by default will open a new tab

the report is here