was listening to this Excellent Podcast about Minerva, basically Airbnb went and built a Metrics Store which is a central tool that hold all Measures and dimensions, the idea is to have one source of truth.
The Podcast is very insightful and worth listening to, But I was very surprised that none of the participants mentioned how existent BI vendors tried to solve the same Problem ,they talked Quickly about LookML but as far as I know it is not available yet for third party tools
Metrics Store is not new, it is just another name for Semantic Model, Business Objects had one from 30 years ago.
So Just for fun, and I am not sure if it is even practical, I wanted to test if PowerBI can act like a pure Metrics Store for third party tools.
I am using BigQuery as a data source, 1 fact table 80 Million records and 5 dimension Tables all using Direct Query Mode, PowerBI is not holding any data, all it does , it receive Queries from the Viz tool in this case Excel and Tableau and translated it to SQL and serve the results back.
I publish the Model to PowerBI service, here is the catch it has to be Premium workspace, Premium per user works too
now Using Tableau, I connect to SSAS, I get the server address from Workspace setting, notice PowerBI and SSAS are using the same engine.
and Voila Tableau Indeed Work just fine
now trying Excel
and here the SQL Queries generated by PowerBI
Notice here Tableau/Excel talk to PBI Semantic Model using MDX, which you can see here using DAX Studio where I can capture all the Queries coming to PBI
The Performance is not great but tolerable I guess, MDX Queries render from 6 to 10 seconds, But it did work which is rather amazing by itself 🙂
Metrics Store is an interesting idea on paper, but not sure in practice, it is hard to justify paying for multiple tools, if you build your semantic Model in one tool, why pay for another BI tool ? The Other challenge is self service Scenario, the user needs to be able to add extra Measures and Dimensions.
and for new vendors, it will be helpful to explain what kind of problems they are trying to solve that the existing tools couldn’t, just creating a new fancy name is not good enough.
Looker was always a bit of mystery for me, as there was no trial version to test, and they have high profile customers, luckily Google recently start providing trial version without asking too much Questions, I registered 2 months ago, and recently got an email with a trial offer.
it is just an impression, I just used it a couple of times in the weekend with no training
You get an URL instance with the name of your company, I used a dummy name.
The user interface is rather streamlined, nothing unusual which a good thing , as expected it is hosted in Google Cloud, but you can’t see anything from the console ( Looker is available in AWS, Azure and GCP)
Looker by default has two modes, development and production, everything you build is linked to Git repository
let’s say you want to build a new Dashboard , first thing to do is to create a new LookML Project, setting up GitHub was straightforward, I made the repo public
Probably you are asking where is the data preparation the Likes of PowerQuery or Tableau Dataprep, there is none, Looker connect only to Databases, it assume you have already a Data Warehouse, Looker Provide Just the Semantic Model using LookML code and the Viz, and the API which you can call from any Clients.
There is no Import mode, all connections are live, there is a cache though that save the result caches which you can adjust by Project or you can turn it off by using cache = 0 second.
There are aggregation Table , which you can use to speed up Queries but again you need to store them in the source database.
Looker use an internal Database ( I think MySQL), but is only to store internal Metadata ( users names, SQL Queries etc), all calculation depends on the source system
To my surprise at least for simple Semantic Models, LookML is not too hard, I connected to a dataset in BigQuery and Looker imported the Table Schema, all I had to do is to define relationship between Tables, and add Measures using SQL
funnily enough the most confusing Part; Looker does not use standard technical Terms:
Views means Table
Explore is a semantic Model
Model is a group of Explores and Views
Look is a chart
at any time you can View the Diagram View
One interesting aspect of Looker Modeling compared to PowerBI, you can have Multiple Models that join the same tables in different ways !!! but at the same time, I could not reproduce a typical Multi Fact Tables easily (Example from PowerBI, I am not saying it is not possible, but I did not know how to do it)
Once the Model is completed you can start exploration using “Explore”, I guess this is the core Concept of Looker , every time you add a metric and dimension, Looker automatically generate the Correct SQL, and because the Model are defined Enterprise wide , the metric definitions and dimensions are consistent regardless of the users.
Notice here, the first thing you get is the data in a tabular Form, then you define the Visual, there is a saying that PowerBI is 80 % Data and 20 % Viz, I would say Looker is 99 % Data and 1 % Viz :), OK I am a bit exaggerating but don’t expect a Tableau Level Customization
When you are happy with your explore you can save it as a Look which is basically an individual Chart which you can pin to a Dashboard like this one
Another very nice functionality is Roles, in PowerBI you can customize what a user can do , build new content, sharing etc, But in Looker it is very granular, you can customize any option , you can even authorize who can and can’t download data !!
Sharing is very trivial, you can share with external users with different email address, even users with gmail and hotmail.
Tracking performance is simple, you can see the history of all SQL Queries and identify any issues
and a nice overall Performance of your Instance
Regarding Performance, I think Looker will perform Better compared to PowerBI in Direct Query mode , simply because there is less moving parts, in PowerBI, the viz generate a DAX Query against the Model which call the engine which call PowerQuery that convert to SQL, Looker Just Generate SQL Queries based on the Data Model that’s all
You can’t really Talk about Looker without talking pricing, here is a screenshot from Google Cloud Pricing
yes, there is no typo, 5000 $/Month get you 12 users and you need to pay 30 $/Month for any extra user !!!
apparently Looker is very popular with High scale embedded scenarios but it require some JavaScript skills which is out of my scope.
Take Away
as Far as I can see the target Market of Looker is Large Enterprises with Mature Data infrastructure and deep pockets, I can see the attraction of code based solutions with version control and centralized Semantic Models, no wonder BI professional with Software engineering background love it.
Apparently in the short term a lot of Data is moving to the cloud and Cloud Data warehouse are getting really Fast and cheap ( See BigQuery BI Engine), the concept of Live connection using a centralized Semantic Model is very attractive and according to some industry commentators it is the next big thing in Business intelligence.
Personally I am not sold, I think every Business users should be able to build his own analysis without waiting on a central BI team( not all reports are equals, some are strategic which require governance other less ), on the bright side LookML is not as hard as I thought at least for simple Models, but with the current Pricing, it is fair to say it is not a threat to PowerBI at least for smaller Installation
edit : got a feedback that a self service user can add custom measures and dimension that did not exist in the original Model , see documentation , but still if you want to use new data, it has to be imported first in the Source Database.
edit : Google shut down my trial instance after three weeks, unfortunately I can’t write any more about Looker, if someone from google is reading this , you’re not helping yourself.
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 https://openflights.org/data.html, 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
WITH
xxxx AS (
SELECT
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,
DestinationairportID,
SourceairportID,
ST_MAKELINE(st_geogpoint(yy.Longitude,
yy.Latitude),
st_geogpoint(zz.Longitude,
zz.Latitude)) AS route
FROM
`testing-bi-engine.test.airportroute` xx
LEFT JOIN
`testing-bi-engine.test.airport` yy
ON
SourceairportID= AirportID
LEFT JOIN
`testing-bi-engine.test.airport` zz
ON
DestinationairportID = zz.AirportID),
ttt AS (
SELECT
source_name,
source_city,
source_country,
destination_name,
destination_city,
destination_country,
DestinationairportID,
SourceairportID,
ST_ASTEXT(route) AS route_wkt
FROM
xxxx
GROUP BY
1,
2,
3,
4,
5,
6,
6,
7,
8,
9)
SELECT
*,
ST_GEOGFROMTEXT(route_wkt) AS route,
ROW_NUMBER() OVER() AS id
FROM
ttt
I save the Query in a table, then plotted using Data Studio
and here is the Result, which i share it in Reddit 🙂
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
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 DataHere 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.
tbl_profiles
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.
tbl_activities
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.
Conclusion
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.
Small Data And self service 