BigQuery GEOGRAPHY Support in Data Studio

Google Data Studio added recently support for BigQuery Geography field, which is a fantastic development and open all kind of new scenarios for creating free to share Maps.

It is straightforward, you just add a geography field and it just render, for example I have this dataset that contains polygons, lines and points

and here is the result

Some Observations

  • It seems the initial focus of the dev team was on polygons which are fully supported
  • You can’t fill color for lines yet
  • Although points support color coding, I could not make them smaller ( The Piles in a Solar Farm are obviously much smaller)

BI Engine to the rescue

Now the confusing part, BI Engine for Data Studio does not accelerate GEOGRAPHY yet , so you will incur BigQuery Cost, but if you connect the new BI Engine SQL interface, the Query will be accelerated, according to the devs, the BI Engine used in Data Studio is to be considered Version 1 , SQL Interface as the next version and they will be merged together eventually. ( This should be in the Documentation)

here is an example of a Query generated by Data Studio, I would say it is very complex SQL Query with analytical functions, UNNEST, you name it, and the New BI Engine support it just fine ( I really like Bytes billed 0 B)

A more interesting use case

I came across this excellent dataset, and thought let’s try it with Data Studio, first I imported the two tables airports.dat and routes.dat  then using this SQL Query to generate the routes, which is a line between Source coordinates and Destination Coordinates

  xxxx AS (
    yy.Name AS source_name,
    yy.City AS source_city,
    yy.Country AS source_country,
    zz.Name AS destination_name,
    zz.City AS destination_city,
    zz.Country AS destination_country,
        zz.Latitude)) AS route
    `testing-bi-engine.test.airportroute` xx
    `testing-bi-engine.test.airport` yy
    SourceairportID= AirportID
    `testing-bi-engine.test.airport` zz
    DestinationairportID = zz.AirportID),
  ttt AS (
    ST_ASTEXT(route) AS route_wkt
  ST_GEOGFROMTEXT(route_wkt) AS route,

I save the Query in a table, then plotted using Data Studio

and here is the Result, which i share it in Reddit đŸ™‚

I think it is fair to say, people love maps, and a lot of users appreciate that you can download the data straight from Data Studio , you can play with the report here

Really Excellent Works by Data Studio Team.

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!


First Look at BigQuery BI Engine with PowerBI

Google made BigQuery BI engine available in a public preview , you need to enroll first here, for the last two years it was available only for Google Data Studio, and I had use it extensively for this Project, so I was really curious how it will work with PowerBI.

I don’t think I know enough to even try to reproduce a benchmark, Instead I am interested in only one Question, how much value I can get using the lowest tier of BI Engine and can PowerBI works smooth enough t make Direct Query a realistic option.

BigQuery team was nice enough for the preview period to have 100 GB reservation free of charge, just to keep it realistic, I kept reservation to 1 GB with a cost of $30 per month, I built a couple of reports in PowerBI and tried to observe how BI engine behave and observe the Query statistic , The report is located here, The Data is using Direct Query Mode, the Query statistics update every 1 hour.

How BI Engine Works

it is extremely easy to setup just select how much memory you want to reserve by Project, and that’s all, you pay by GB reserved per hour.

Keep in mind the Project used for reservation can Query any other projects as long as it is in the same region, in PowerBI, you can define which project you use for the Query

After you wait a couple of minutes for BI engine to start, this is more or less how it works

1-Query received by BigQuery, based on columns used in the Query, BI engine will load only those columns and partition into the Memory, the First Query will be always slower as it has to scan the data from BigQuery Storage and compress it in memory in my case usually between 1-2 second

2-Second Query arrive, the data is already in Memory, very Fast 100 ms to 500 ms

3- Same Query arrive as 2, BigQuery will just hit the cache, that’s the sweet spot, less than 100 ms

4- A new Query arrive that target different table, that’s the interesting part, BI Engine based on the size of the scanned column, and the available reservation, either evict the old table from memory or decide that there is not enough Reservation then it will fall back to the default BI Engine, where you pay how much data is scanned

5- A Query arrive that contains feature not supported by BI Engine , it will fall back to the default engine

6- Data appended to the Base table or Table changed, BI Engine will invalidate the cache , it will load the delta to memory or load everything again if the table was truncated

Obviously it is much more complex behind the scene, But I find it fascinating that BI engine in a fraction of a second decide what’s the best way to serve the Query ( cache, Memory or Storage)

Personally I am very interested in Case 4, obviously if I reserve a Memory I want to minimize scanned storage to the lowest possible, here is the result for the last 10 days, I think that’s a great result, my ” Big Table is 6 GB, 50 Million rows” other tables are smaller , the dev team said they are working on improving even more how BI Engine algorithm deals with smaller tables, so far happy with that. ( it is fixed now, the memory consumption is extremely low now )

I appreciate other users with flat rate pricing would not care that much about file scanned , for user with usage based pricing, it is a very important factor

Query Performance

Again the results is based on my usage, the only way to know is to try it yourself, nearly 72 % of Queries render in less than 100 ms, I think it is fair to say, we are into a different kind of data warehouse architecture

PowerBI Performance

The Query Performance is only a portion of the whole story, you need to add network latency and PowerBI overhead ( DAX calculation, Viz rendering etc), my data is in Tokyo Region and PowerBI Service is located in Melbourne, a bit of distance I would say and using Publish to web add an extra latency.

The good thing, using Direct Query on a 51 Million Fact table with 5 dimensions is an achievement in itself, I feel I can use that in a Production, at the same time, using other report, it seems I am hitting a bug in the ODBC driver, and the performance is not good.

but to be totally Honest, it seems PowerBI driver for BigQuery is far from being optimized, it seems they are using SIMBA ODBC , other BI tools are using the native API and it is substantially faster, but I have reason to believe the PowerBI team will invest more in better Integration ( PowerBI parameter in SQL Query is coming for example)

I Think it is extremely interesting new development, specially if you have Big Fact tables or data that change very frequently, Direct Query mode have a lot of advantages, it is very simple to setup, the data is always fresh and there is no data movement. and BI Engine is fast, extremely Fast, and Cheap !!!, I am using a state of the art data warehouse for $30 per month !!!!, now it is up to The PowerBI Team to take advantage of that.

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(""),
     #"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