Connect Streamlit to PowerBI service using XMLA end point

Streamlit is a new framework to build data web app using only python, you don’t need any knowledge of javascript/HTML.

Connecting to a PowerBI using Python is well documented , see those excellent tutorials here by David Eldersveld

using this code, I managed to build a small app that using an existing XMLA end point, first it will extract the existing models and then you can run arbitrary DAX queries.

please note as of August 2020, XMLA end point is a PowerBI premium only feature

the main connection string and how to export to a df was copied from this Answer in Stackoverflow

import adodbapi as ado
import numpy as np
import pandas as pd
import streamlit as st

def get_df(data):
    ar = np.array(data.ado_results) # turn ado results into a numpy array
    df = pd.DataFrame(ar).transpose() # create a dataframe from the array
    df.columns = data.columnNames.keys() # set column names
    return df
source=st.sidebar.text_input('Write your XMLA endpoint')
if source:
    with ado.connect("Provider=MSOLAP.8; Data Source="+source) as con:
        with con.cursor() as cur:
         cur.execute('select * from $SYSTEM.DBSCHEMA_CATALOGS')
         data = cur.fetchall()
         catalogue = get_df(data)
         catalogue_Select= st.sidebar.selectbox('Select Models', catalogue['catalog_name'])
dax=st.text_area('Write your DAX Query:')
if dax:
    with ado.connect("Provider=MSOLAP.8; Data Source="+source+" ;Initial catalog="+catalogue_Select) as con:
        with con.cursor() as cur:
         cur.execute(dax)
         data = cur.fetchall()
         df = get_df(data) 
         st.write (df)

and here is the result

Unfortunately adodbapi required Windows , which make deploying the app a bit harder, yo can try Azure Web app which has a windows runtime, I wish it was as easy as Heroku !!!

The good new Microsoft added recently the support for .Net Core, so hopefully I will Update the blog with a cross platform solution

to run the app on your laptop, just type

streamlit run app.py

it is a proof of concept but I see a lot of use cases, an obvious one is to build web app for visualization not supported by PowerBI like massive dataset maps, or 3 D viz.

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

Using the new Convexhull function in BigQuery to reduce Geometry complexity

BigQuery recently introduced two new GIS functions, ST_CONVEXHULL and ST_DUMP

Read the announcement here , when I saw the announcement I already thought about this use case.

The Problem

Although showing map in BI software has improved dramatically in the last couple of years, still unless you use Tableau, there is always a hard limit how much data you can show in a map, even if you can show more, it is better to reduce the volume of data just for performance sake, users are so spoiled those day that they complain when their report does not show up in less than 2 second.

Although the example used here is very specified, I am sure it can be extended to other uses cases.

Let’s say you want to show a lot of points  with one colour coded attribute, in a lot of cases, the end user wants only to see the distribution of the attribute not the individual points, see here  

That’s a lot of points ( my real case is 58 Thousand)

Convexhull to the rescue

Convexhull is very handy the input will be group of points and the output will be a closed polygons, I use it a lot in QGIS, but the killer feature here, because it is SQL and the attribute are dynamic, (in my use case they changed daily), you can write a Query that dynamically generate new geometries, either polygons or linestring or even  keep the original points if they can’t be grouped.

Now the trick is we group by status and existing grouping, for example in this dataset.

  1. Check if in one area all the status is the same using count distinct, if in one area it is the same attribute, it will generate a polygons.
  2. if one area has multiple status and hence multiple colours then fine, we jump to the row level and generate line strings.
  3.  If one line string has multiple colors then we jump to points.

I built this SQL View with the help of  Mikhail Berlyant, the source data is here, replace “xxxxx.SolarFarm ” with your table.

WITH
  source AS (
  SELECT
    *,
    ST_GEOGFROMTEXT(CONCAT( "POINT (",x," ", y,")")) AS POINT,
    COUNT(DISTINCT status) OVER (PARTITION BY ROW) AS multiple_status,
    COUNT(DISTINCT status) OVER (PARTITION BY area) AS multiple_status_area
  FROM
    `xxxxxxx.SolarFarm`),
  tt AS (
  SELECT
    id, pole_nr,color,area, ROW,status, POINT,
    CASE
      WHEN multiple_status_area=1 THEN area
      WHEN multiple_status=1 THEN row
    ELSE
    CAST (id AS string)
  END
    AS newgroup
  FROM
    source),
  ff AS (  SELECT newgroup, ST_ASTEXT(ST_CONVEXHULL(ST_UNION_AGG(POINT))) AS WKT
  FROM
    tt
  GROUP BY
    1),
  xx AS (
  SELECT
    tt.newgroup,
    wkt,
    tt.status
  FROM
    tt
  LEFT JOIN
    ff
  ON
    tt.newgroup = ff.newgroup)
SELECT
  newgroup,
  wkt,
  status
FROM
  xx
GROUP BY
  1,
  2,
  3

and here is the result side by side with the original data from 3528 rows to 283 rows, that’s a big improvement,

as of July 2020, Google Data Studio does not support Geometry, and the total number of points is limited to 10K, you can use other custom Visual but currently tiles are blocked.

if you are using PowerBI to view the data, you need to use the excellent Icon Map as it support WKT geometry

NASA Apollo Cost Tracker

Quick how to guide on building my NASA cost tracker.

To follow up a recent video showcasing the NASA Apollo Costs, I wanted to illustrate how easy it is to use PowerBI to generate quick program of works dashboard. If you have several projects following a pipeline of work, some features here might spur some discussions or thoughts on what is possible.

The Data

I have sourced data from a google drive folder

NASA COSTS

However, like most data you find, the format is not suited to analytics. So a little manipulation was in order. Firstly, I had to create a WBS structure.  Typically, information we find is buried under headers, however for databases, we need to turn group headings into a column data field.

We can see I have inserted a 3 layer WBS structure, plus a company name field. This will allow me the flexibility to add subsequent data to this file from perhaps multiple companies, not just NASA. Again, when you build flexible data structures, the way you can use the structure is much more powerful

I know that I also want more contextual information displayed on the dashboard beyond simple data. Specifically, I want a description blurb to be viewable on a tool tip, along with a picture. Additionally, I want to display the leading contractor as well. Therefore, I added a few columns to the excel file. When you import the data into PowerBI, the URL needs to be set as a special format of “Image URL”. Took for some time to find that setting: its under “data category” on the column tools tab.

At some point, I will hopefully build out this dataset to include subsequent NASA budgets, and also publish this data through an API that everyone can access. However, there are limitation to what I can do and what I want to do typically far outstrips my abilities.

The Dashboard

Importing the data is quite straight forward, we do need our usual “unpivot” trick to convert the year information (which is contained inside columns) into row based data. However once that is done, lets look the various parts of the dashboard.

Before I jump into the various aspects of the dashboard, what really gives a dashboard a little polish is the use of a background image. Here is my go to ground image. Just a little playing around with Paint can produce something very valuable to your end product.

The dashboard utilizes 3 slicers. Each has a slightly different formatting. I definitely recommend playing around with the formatting of your slicers

The TREEMAP is where I have put a little extra bit of attention

What pops out here is the tooltip. I have created a separate page just for this tooltip. I am by no means an expert in designing tooltip, but know the power of inserting extra dimensions of data that again allows your dashboard to pop. This specific tooltip includes the blurb, an image URL and the main contractors. This information would be too dense for the overall dashboard and perhaps not dense enough for its own dash, therefore a tooltip is a perfect medium between.

The final element of the dashboard is the line graph and histogram. I still find creating line graphs difficult and in this case I had to add a measure to my data. I think there is a much easier way to achieve rolling sum data, but in my case, the below measure works easy enough for me.

CTD_line = CALCULATE(SUM(NASA_Budgets[Value]),filter(ALLSELECTED(NASA_Budgets),NASA_Budgets[Year]<=MAX(NASA_Budgets[Year])))

And with that, we have our completed dashboard

Extensions

There is a lot I can do with this framework now. We have a cost file that is quite generic and a dashboard that is also generic. We can in theory use this to outline any type of project pipeline. Although this dashboard is looking in the past, we can also have a rolling wave where we can see past spend on specific projects and what our future pipeline of work looks like. I love seeing project pipelines and following my NASA theme for the moment, here is a great view of what the NASA project pipeline looked like in 1973