Want to show a rolling average for your data, but not the granular breakdown? We can use the average window function to run this calculation. First, I created a CTE that contains the number of new users per day on a fictional gaming platform. If you ...
NOTE: you don't need to be doing Machine Learning to make a cool 3D plot like this! This post takes the output of a machine learning model and shows the results on a 3D plot, but you can plot almost anything with 3 numerical dimensions using basicall...
Sisense for Cloud Data Teams has a feature called Text on Dashboards that allow users to add text boxes as banners, announcements, and descriptions of charts! However, they currently only support one font. With the R and Python integration, users can...
In MySQL, it’s easy to convert an integer representing a number of seconds into a string representing a human-readable amount of time.This is especially useful when you want to compare two timestamps, for example if you wanted to know how much time e...
There are lots of knobs (a.k.a hyperparameters) we can turn when coming up with a Machine Learning model. in the script below, we take the well-known iris dataset, and play around with different hyperparameters. First, a few notes: In machine learnin...
Let’s say you have some aggregated data and want to break it out into multiple rows. Maybe the original data source provided the values in this format, or maybe it was produced by another SQL View using Listagg() or Stringagg(). Perhaps it's extracte...
A common request is to apply conditional formatting on entire rows based on a value in a given column. Currently, with Sisense for Cloud Data Teams charts, you can only conditionally format columns. Python charts give us much more customizability aro...
SQL Alerts are very handy to keep track of any critical metrics. Oftentimes it makes most sense to do a relative comparison of your data. For instance, "alert me if my metric dropped between yesterday and today." The below SQL code is an example of h...
MySQL aficionados have likely leveraged its ability to declare variables. Unfortunately, this capability isn't supported by all flavors of SQL. For such situations, we can still achieve a similar result by creating a CTE of variables (for more inform...
Plotly is great for its interactivity on visualizations! With choropleth charts we can add a slider to the bottom of the chart to see how data changes over time on a map. The end user can drag the slider across the bottom to see the colors on the hea...
Wanted to alter the thickness of the lines in your series, add markers to your lines, and make them dashed or dotted? The plot.ly library support on Sisense for Cloud Data Teams' Python/R integration allows you to tailor these parameters to your hear...
Aggregating data by various periods of time is made easy with Sisense for Cloud Data Teams' built-in date aggregation filter. Sometimes, however, you may wish to group your data by different periods of time. In this post, we'll look at how to create ...
More often than not, you'll need data from multiple different tables to create the perfect chart! There are many ways to do this but the most common way in SQL would be to use Joins! There are four basic types of Joins: inner, left, right and full. S...
Coming from the world of spreadsheets, you may have defined a set of values recursively with ease, referencing the previous value plus another variable to calculate a new value. Something like this:In the above screenshot, the result column is calcul...
Color is an extremely useful visualization tool. On a line chart, a color gradient can be used to emphasize the highs and lows. Using the Python / R integration in Sisense for Cloud Data Teams, we can definitely achieve this look. Here's the SQL outp...
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