Welcome to the KQL Art Gallery, where data transforms into visual masterpieces using Kusto Query Language (KQL). KQL, the query language for Azure Data Explorer, is a powerful tool for slicing, dicing, and visualizing datasets. In this post, we’ll explore KQL’s visualization capabilities, particularly the render
operator, to craft stunning charts that tell creative data stories. Whether you’re a data enthusiast or a storytelling artist, you’ll learn to paint with queries using datasets like weather data or social media trends. Let’s dive in and create some data art!
Grab the dataset for this blog post: https://github.com/rod-trent/WeatherData
The Canvas: KQL and the render
Operator
KQL is designed to query large datasets with speed and precision. Its visualization superpower lies in the render
operator, which transforms query results into charts like bar graphs, line charts, heatmaps, and more. Think of render
as your paintbrush—it takes raw data and turns it into a visual narrative.
For this gallery, we’ll use a weather dataset to create three pieces of “data art”:
A bar chart showing average temperatures by city.
A time series tracking temperature trends over a week.
A heatmap revealing temperature patterns across hours and days.
Each example will teach a KQL concept while emphasizing creative storytelling. Let’s set up our dataset and start painting!
The Dataset: Weather Observations
Imagine a dataset called WeatherData
with the following schema:
Timestamp
: Date and time of the observationCity
: Location (e.g., Seattle, Miami, Denver)Temperature
: Temperature in FahrenheitHumidity
: Humidity percentage
This dataset, inspired by real-world weather logs, is perfect for crafting visual stories. If you’re following along in Azure Data Explorer, you can simulate this dataset or use a similar one available in the help
cluster (e.g., StormEvents
).
Artwork 1: Bar Chart of Average Temperatures
Let’s start with a simple yet striking bar chart to compare average temperatures across cities. This teaches the KQL summarize
operator, which aggregates data.
Query
WeatherData
| where Timestamp > ago(30d)
| summarize AvgTemp = avg(Temperature) by City
| render barchart
Explanation
where Timestamp > ago(30d)
: Filters data to the last 30 days.summarize AvgTemp = avg(Temperature) by City
: Calculates the average temperature per city.render barchart
: Visualizes the results as a bar chart, with cities on the x-axis and average temperatures on the y-axis.
Storytelling Tip
This bar chart is your first piece of art—a snapshot of climate diversity. To make it pop, customize the title in Azure Data Explorer’s visualization pane: “City Climate Showcase.” Use bold colors for each bar to reflect the cities’ vibes (e.g., sunny yellow for Miami, cool blue for Seattle). Narrate a story about how these temperatures shape local culture or tourism.
Artwork 2: Time Series of Temperature Trends
Next, let’s create a time series to track temperature fluctuations over a week. This introduces the bin
function for time-based aggregation.
Query
WeatherData
| where Timestamp > ago(7d) and City == "Seattle"
| summarize AvgTemp = avg(Temperature) by bin(Timestamp, 1h)
| render timechart
Explanation
where Timestamp > ago(7d) and City == "Seattle"
: Focuses on the last 7 days for Seattle.summarize AvgTemp = avg(Temperature) by bin(Timestamp, 1h)
: Aggregates data into 1-hour bins, computing the average temperature per hour.render timechart
: Plots a line chart with time on the x-axis and temperature on the y-axis.
Storytelling Tip
This time series is a dynamic narrative of Seattle’s weather. Highlight daily patterns—perhaps temperatures dip at night and peak in the afternoon. Add annotations in the visualization pane to mark key moments, like a sudden cold front. Tell a story about how residents adapt to these shifts, perhaps with cozy coffee shop visits during chilly mornings.
Artwork 3: Heatmap of Temperature Patterns
For our final piece, let’s craft a heatmap to reveal temperature patterns across days and hours. This showcases the make-series
operator for complex visualizations.
Query
WeatherData
| where Timestamp > ago(7d) and City == "Miami"
| summarize AvgTemp = avg(Temperature) by bin(Timestamp, 1h)
| project HourOfDay = hourofday(Timestamp), DayOfWeek = dayofweek(Timestamp), AvgTemp
| summarize Heat = avg(AvgTemp) by HourOfDay, DayOfWeek
| render heatmap
Explanation
summarize AvgTemp = avg(Temperature) by bin(Timestamp, 1h)
: Aggregates hourly temperatures.project HourOfDay = hourofday(Timestamp), DayOfWeek = dayofweek(Timestamp), AvgTemp
: Extracts the hour of the day and day of the week for each data point.summarize Heat = avg(AvgTemp) by HourOfDay, DayOfWeek
: Creates a grid of average temperatures by hour and day.render heatmap
: Displays a heatmap where color intensity represents temperature (e.g., red for hot, blue for cool).
Storytelling Tip
This heatmap is a vibrant mosaic of Miami’s climate. Use a gradient color scheme to emphasize temperature shifts—bright reds for midday heat, soft blues for cooler nights. Craft a story about how these patterns influence daily life, like beachgoers flocking to the shore during peak warmth. Add a legend to guide viewers through your “art.”
Creativity in Data Storytelling
Each visualization is more than a chart—it’s a story. Here are tips to elevate your KQL art:
Choose the Right Chart: Bar charts excel for comparisons, time series for trends, and heatmaps for patterns.
Customize Visuals: Use Azure Data Explorer’s visualization options to adjust colors, titles, and labels.
Weave a Narrative: Connect the data to human experiences. How does weather shape a city’s rhythm?
Experiment with Datasets: Try social media trends (e.g., tweet volumes) or IoT sensor data for new stories.
Try It Yourself!
Ready to create your own data art? Head to Azure Data Explorer, load a dataset (weather, social media, or even StormEvents
), and experiment with these queries. Play with render
options like columnchart
, areachart
, or scatterchart
. Share your visualizations on social media with #KQLArtGallery to inspire others!
KQL’s visualization capabilities make data a canvas for creativity. Whether you’re charting temperatures or trending hashtags, you’re not just querying—you’re storytelling. So grab your KQL paintbrush and start creating!