Dynamic Date Granularity using Parameter in Google Data Studio

I created this dynamic bar chart which change the granularity based on the date selected, so if you the date selected > year, it will group the data per year then you can drill down to Month, the same if it is Month, you can drill down to week etc. see Example here.

it was just for fun, but someone asked me how it works, I thought it is worth a Quick Blog.

As of 27 October 2020, the date parameter are only available if you connect to BigQuery.

ok, basically you need just create a custom Query that calculate the duration between the start date Parameter and end date parameter which are passed by the Calendar Control

This example is using Average Australian wholesale Electricity Market, Obviously it works with any dataset that contain date.

WITH
   xx AS (
   SELECT
     *EXCEPT(YEAR),
     CAST(DATE_TRUNC(DATE(SETTLEMENTDATE), YEAR) AS timestamp) AS YEAR,
     CAST(DATE_TRUNC(DATE(SETTLEMENTDATE), MONTH) AS timestamp) AS MONTH,
     CAST(DATE_TRUNC(DATE(SETTLEMENTDATE), WEEK) AS timestamp) AS WEEK,
     CAST(DATE(SETTLEMENTDATE) AS timestamp) AS date,
     DATE_DIFF(PARSE_DATE('%Y%m%d',
         @DS_END_DATE),PARSE_DATE('%Y%m%d',
         @DS_START_DATE),DAY) + 1 AS nbrdays
   FROM
     xxxxxxx.PRICEARCHIVE
   WHERE
     DATE(SETTLEMENTDATE) >= PARSE_DATE('%Y%m%d',
       @DS_START_DATE)
     AND DATE(SETTLEMENTDATE) <= PARSE_DATE('%Y%m%d',
       @DS_END_DATE)
     AND UNIT="DUNIT")
 SELECT
   REGIONID,
   RRP,
   nbrdays,
   CASE
     WHEN nbrdays <= 1 THEN SETTLEMENTDATE
     WHEN nbrdays <= 7 THEN date
     WHEN nbrdays <= 31 THEN WEEK
     WHEN nbrdays <= 365 THEN MONTH
   ELSE
   YEAR
 END
   AS newdate,
   CASE
     WHEN nbrdays <= 1 THEN SETTLEMENTDATE
     WHEN nbrdays <= 7 THEN SETTLEMENTDATE
     WHEN nbrdays <= 31 THEN date
     WHEN nbrdays <= 365 THEN WEEK
   ELSE
   MONTH
 END
   AS newdate2
 FROM
   xx

Change Dimension Dynamically using Parameter in PowerBI

At Last, PowerBI added support for parameters that can be changed by the end user, I guess from a Business perspective, it is mostly useful when you deal with Big Data load, and you want to control exactly the Query generated at the data source level, but in this short Blog, I will show how some use cases where hard or clunky using DAX became extremely easy to do using Parameters.

pbix file here : notice it is connecting to my DB instance, so it will not work but you can see the Data Model.

I think it is wise to read the documentation here first

Chris Webb has a great use case using Azure Data explorer here

Update : I added a new use case here, changing weekend Date Dynamically

We want to change a dimension based on a user selection from a slicer, currently Only DirectQuery is supported and to be honest, the documentation does not tell which data source works, we know SQL server is not one of them, Thanks to Alex for his clarification, Luckily BigQuery Works ( that was a very nice surprise to be honest)

I am using the Covid19 data set as an example (as it is free and don’t incur any charge till sept 2021), we want to switch dynamically between countries and continent

1- Load the main Table as import mode

2- Create a parameter ” Level_Details”

3- Import dimension Table with the values countries and Continent in Direct Mode:

I created a view in BigQuery , PowerQuery stopped folding when I tried to remove duplicated, although it is free data source, it is important to use directQuery only with dimension Tables to reduce cost and Data volume

4- Include the parameter logic in Dimension Table

I created a new Column “Grouping_Details” based on the Parameter Value, it will Take either Countries or Continent

5- create a new Table that contains all the possible values for the Parameter

by the way, you can use any table, either imported, or generated using DAX, this is a very clever implementation by the PowerBI team compared to Other BI Tool.

6- Bind the value of the column “Selection” to the Parameter

here is a View of the Data Model

it is very Important that “Selection_Details” stay as a disconnected Table, otherwise it will create new filter selection in the Queries which we don’t want, it will work but we want to control exactly the Query generated by PowerBI

And the Report

The feature is in Preview and I am sure, they will introduce more Data Sources and functionalities, by adding support to BigQuery, Microsoft sent a clear message, PowerBI is the best Data Analytics tool and they will support any third Party Data Warehouse, even if it is a direct Competitor.

Personally,I am very excited by the thought that we are very close to Finally have Parameter Action In PowerBI , and that will introduce a new class of Visual Analytics Interaction that was not even Possible, Please need some Votes here

Btw, if you use BigQuery with PowerBI, I appreciate some votes here, we need the support of Custom SQL Query with Parameter

Three years to finish a Dashboard

in 2017, at my previous job, we were using PowerBI Desktop as our reporting solution, but there was a big limitation, we couldn’t use the service, so sharing the reports was either in Excel or pdf.

I remember I did try different solutions (Rstudio, Qlik, SSRS), they were great Products, but you need some kind of server to share the reports. At that time all I wanted is a simple web app where people can click on a slicer and get a fancy charts.

At that time Google made their reporting solution free, I was really excited about Data Studio, a free product, extremely easy to share but unfortunately a bit slow and lacked some basic functionality, I still managed to build something but it was not really good

It is all history now, moved to another job, we have PowerBI service ( and Tableau), but still for some reason, I felt like a missed opportunity, what if Data Studio became a good enough to be used as a free report tool.

If I remember correctly 2017 and 2018, there was no major progress but then they released custom viz, which basically means you can port any javacript library relatively easily , I managed to build a custom viz see example here

and in sept 2019, BI Engine showed up !!

It was really a big Deal, BI Engine is an analytics in-memory Database , and it is fast and they gave away 1 GB for free, it means you can connect your data from BigQuery and pay nothing ( with a fair limit of course), this made this report possible

In May 2020, they finally released Google Map Integration , although with a limit of 10K points, it was not useful for my use cases ( Solar farm needs a lot of point around 40k to 60K)

That was great and all, but still I couldn’t write complex measures easily ( or maybe did not know how), but something changed in August 2020

At last we have Proper support for parameter, that changed everything, now you can write any complex business logic using SQL in BigQuery and visualize the results using Google Data Studio, and you can do a lot of fancy stuff see those examples

Still there was still a major bug, Pivot table in Data Studio show 0 for null values needless to say, it is extremely annoying although you can build workaround, it was a hack and not sustainable.

That was fixed last week

So yes, it took me three years to finish this report, BI Engine + Parameter + Custom Viz and a bug fix in the Pivot Table to make this report possible

I added a workflow explanation in the report, but basically create a reporting dataset as large flat fact table and show the results in BigQuery with further control by SQL Parameter, if the native visual are not satisfying, you can show pretty much anything using Vega-lite custom viz.

One aspect was impossible to do without Parameter is the dynamic grouping of dates, in the time series, the weekend update dynamically based on the cut off selected.

Please don’t get me wrong, there is still a lot of work to be done, but the foundation of the product is already there, I can see clearly the vision of the product team, hopefully they keep investing but faster this time ( Parameter Action, support for BigQuery geography field, analytics functions, Tiles for Custom Viz ……)

Take away:

– If you need near real time reports

– You want a reporting solution and don’t have a decent budget.

– Used Data Studio in 2017 and dismiss it.

I have a good news for you, BigQuery/Data Studio is a viable option now, and you get 1 TB free for BigQuery and 1 GB compressed in -memory for BI Engine, that’s a lot of free resources, and there is no catch, you can share securely with anyone, again totally free.

Although I am a PowerBI developer and I love it, I think it is very healthy for the industry to have more choices, 2021 will be exciting !!!

How to plot Digital Elevation Model in Data Studio.

TL;DR : a sample dataset with x,y,z,red,green,blue and a custom Viz in Google Data Studio Using Deck.GL point Cloud, see example here

I added a new dataset , so you can test it yourself, you can either load it using BigQuery or use the load file connector in Data studio.

section explain how we got the data, if you are only interested in testing the visual go to section 2.

1-How to get the Data

for some reason it it is extremely painful to get a dataset with x,y,z,r,g,b

luckily a couple of days ago, I was in twitter and saw this tweet by Michael Sumner

it turn out extracting coordinated and elevation is extremely easy using R, all you need is the center location and the dimension of the area you are interested in, and R ceramic will extract x,y,z automatically in a nice dataframe, then I took that data and uploaded it to BigQuery using the package bigrquery then plot using a custom Viz I built using Deck.gl ( see the linked report)

here is a script I used

library(raster)
library(ceramic)
library(bigrquery)
bq_auth("XXXXXXXX.json")
Sys.setenv(MAPBOX_API_KEY = "DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD")
cc <- ceramic::cc_location(cbind(14.428778,40.822973), buffer = c(2000, 2000), zoom = 15)
el <- ceramic::cc_elevation(cc)
el1 <- resample(el, cc, method = "bilinear")

df1 <- as.data.frame(cc,xy=TRUE)
df2 <- as.data.frame(el1,xy=TRUE)
df <- merge(x = df1, y = df2, by = c("x", "y"), all.x = TRUE)

df <-transform(df, lng=x/100000,lat=y/100000,red=layer.1,blue=layer.2,green=layer.3)
df <- df[c("lng", "lat","layer","red","blue","green")]
job <-  insert_upload_job("PROJECT_ID",
                "GIS",
                "VOLCANO",
                df,
                create_disposition = "CREATE_IF_NEEDED",
                write_disposition = "WRITE_TRUNCATE")
wait_for(job)

2-Plot the Data using Point Cloud Viz

the Custom Viz address is

or you can just copy the report and use your own data

all fields are required except tooltips, by default it will show coordinates

I used Mount Tahat as an example, it is a highest Moutain in the south of Algeria, extremely beautiful area

Data Studio limit the number of rows passed to a custom visual to 1 Million, here I made sure it is less than 750K as it is the maximum that can be downloaded from the visual

3-The end Results

Mount Uluru in Australia

Volcano Vesuvius in Italy