Ever have a SQL view that won't seem to materialize? Getting that dreaded red X even when results are showing in the View editor? Well follow the below instructions to turn those red X's into green checks!1. YOUR SQL SYNTAX ISN'T REDSHIFT COMPATIBLE....
Databases within DatabasesThe Warehouse is a managed Redshift solution which has the ability to store data from multiple origin data sources. Each Redshift cluster can contain multiple databases, and the Warehouse stores all relevant tables pulled in...
When summarizing statistics across multiple categories, analysts often have to decide between using weighted and unweighted averages.An unweighted average is essentially your familiar method of taking the mean. Let's say 0% of users logged into my si...
One of the first steps in exploratory data analysis is to identify the characteristics of the data, importantly including a test for distribution patterns. In this example, learn how to check if your data is normally distributed in Python with a visu...
Often times, we want to analyze data with the date from yesterday. How do we auto-populate yesterday's date? There are a few formats that we can use! The syntax below lets us get yesterday's date with timestamp:select dateadd(day,-1,getdate()) To onl...
Let's say we want a printout of our confidence interval for an entire sample (Note, if you're looking for a visual of a confidence interval over time, check out the post here!) The solution here requires Periscope Data's Python/R Integration as we'll...
Here's the scenario: you just used SQL and have an output that shows the results you want, but it isn't optimally displayed. Instead of writing cumbersome case when statements, unions/union alls, we can quickly use a few short functions in Python or ...
Gantt charts are a great way to show which projects and tasks different teams are working on at a given time. This chart type is heavily used by Project Managers, Task Managers, and anyone else managing multiple workflows.Using Periscope Data's Pytho...
Usually when I need to upload a CSV I will use the Sisense for Cloud Data Team's CSV functionality. It's fast, easy, allows me to join the data with all my databases, and automatically casts types. Sometimes, however, I like to interact directly with...
Pandas makes transposing tables beautifully simple! Below are a couple examples using the example SQL output below:This first Python snippet allows you to define your own column headers:# SQL output is imported as a pandas dataframe variable called "...
Update: Sisense for Cloud Data Teams now allows you the ability to create additional date range filters without having to go through the below workaround. Refer to our docs here under "Additional Date Range Filters"Sometimes, users would like to appl...
A common hazard when adding data labels to a chart, is that often times the data labels will overlap when there are two points located relatively close to one another. Fortunately, the flexibility of python allows us a way around overlapping data lab...
Sometimes outliers are just in the way of chart creation and we want to put them in their own section of chart. Python can help us do that! I started with a data set like "count of users created by month" where there were only values for Jan-Feb and ...
Funnel charts are a great way to represent any drop-offs in sample size throughout a series of steps. Using a little bit of Python handiwork in Sisense for Cloud Data Teams' R/Python integration, we can easily create this chart type.A common use case...
Data can oftentimes have extreme outliers, which can heavily skew certain metrics, such as the mean. One way to get around this is to using a trimmed mean. Using a trimmed mean, users will remove the top and bottom x percent of their data and take th...
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