KQL in Pop Culture: Querying Your Favorite Fictional Worlds
Use KQL to uncover fascinating insights, like identifying the most active smugglers in the galaxy or analyzing potion class performance trends.
Welcome to a fun exploration of the Kusto Query Language (KQL) through the lens of pop culture! KQL, used in Azure Data Explorer, is a powerful tool for querying and analyzing data. In this blog post, we’ll dive into two fictional datasets inspired by beloved universes—Star Wars trade logs and Hogwarts student records—and use KQL to uncover fascinating insights, like identifying the most active smugglers in the galaxy or analyzing potion class performance trends. Let’s get started!
Get the datasets for this exercise: https://github.com/rod-trent/pop-culture-kql-datasets
Setting the Scene: Our Fictional Datasets
We’ll work with two datasets:
Star Wars Trade Logs: A dataset capturing smuggling activities across the galaxy, including smuggler names, cargo types, planets, and timestamps.
Hogwarts Student Records: A dataset of student performance in Potions class, including student names, houses, grades, and academic years.
These datasets are fictional but structured to mimic real-world data, making them perfect for learning KQL in an engaging way.
Dataset 1: Star Wars Trade Logs
Imagine a galactic ledger tracking smuggling operations. The dataset, named SmugglerLogs, has the following schema:
Timestamp: Date and time of the smuggling activity (datetime)Smuggler: Name of the smuggler (string)CargoType: Type of cargo, e.g., Spice, Blasters, or Kyber Crystals (string)Planet: Planet where the activity occurred (string)Quantity: Amount of cargo smuggled (int)
Sample data might look like:
Timestamp Smuggler CargoType Planet Quantity 2023-01-01T08:00:00 Han Solo Spice Tatooine 50 2023-01-02T09:00:00 Lando Calrissian Blasters Corellia 30 2023-01-03T10:00:00 Han Solo Kyber Crystals Jedha 10
Dataset 2: Hogwarts Student Records
This dataset, named PotionsGrades, tracks student performance in Hogwarts’ Potions class:
AcademicYear: Year of the record (int)Student: Student’s name (string)House: Hogwarts house (string)Potion: Name of the potion brewed (string)Grade: Grade received, e.g., Outstanding, Exceeds Expectations (string)Score: Numerical score out of 100 (int)
Sample data:
AcademicYear Student House Potion Grade Score 1995 Harry Potter Gryffindor Draught of Peace Acceptable 70 1995 Hermione Granger Gryffindor Draught of Peace Outstanding 95 1995 Draco Malfoy Slytherin Wit-Sharpening Exceeds Expectations 85
KQL in Action: Querying the Galaxy and Hogwarts
Let’s write KQL queries to explore these datasets, starting with simple queries and progressing to more advanced ones. We’ll assume the data is ingested into Azure Data Explorer tables named SmugglerLogs and PotionsGrades.
Query 1: Most Active Smugglers in the Galaxy
Let’s find the top smugglers by the number of smuggling activities in SmugglerLogs.
SmugglerLogs
| summarize ActivityCount = count() by Smuggler
| order by ActivityCount desc
| top 3 by ActivityCount
Explanation:
summarize ActivityCount = count() by Smuggler: Groups the data bySmugglerand counts the number of activities.order by ActivityCount desc: Sorts results in descending order.top 3 by ActivityCount: Returns the top 3 smugglers.
Sample Output:
Smuggler ActivityCount Han Solo 150 Lando Calrissian 100 Qi’ra 80
Han Solo is the galaxy’s busiest smuggler! This query helps us quickly identify key players in the smuggling world.
Query 2: Smuggling Trends by Cargo Type
What’s the most smuggled cargo type in the last 30 days?
SmugglerLogs
| where Timestamp > ago(30d)
| summarize TotalQuantity = sum(Quantity) by CargoType
| order by TotalQuantity desc
Explanation:
where Timestamp > ago(30d): Filters for activities in the last 30 days.summarize TotalQuantity = sum(Quantity) by CargoType: Sums the quantity of cargo by type.order by TotalQuantity desc: Sorts by total quantity.
Sample Output:
CargoType TotalQuantity Spice 2000 Blasters 1500 Kyber Crystals 500
Spice is the most smuggled item—perhaps Jabba the Hutt is driving demand!
Query 3: Hogwarts House Performance in Potions
Let’s analyze which Hogwarts house performs best in Potions class by averaging scores across all years.
PotionsGrades
| summarize AvgScore = avg(Score) by House
| order by AvgScore desc
Explanation:
summarize AvgScore = avg(Score) by House: Calculates the average score per house.order by AvgScore desc: Sorts houses by average score.
Sample Output:
House AvgScore Ravenclaw 88.5 Slytherin 85.2 Gryffindor 82.1 Hufflepuff 80.3
Ravenclaw takes the lead—those bookworms must be acing their brews!
Query 4: Potion Class Performance Trends Over Time
Are students getting better at brewing potions over the years? Let’s check the average score per academic year.
PotionsGrades
| summarize AvgScore = avg(Score) by AcademicYear
| order by AcademicYear
| render timechart
Explanation:
summarize AvgScore = avg(Score) by AcademicYear: Averages scores by year.order by AcademicYear: Sorts chronologically.render timechart: Visualizes the trend as a line chart.
Sample Output: A chart showing scores trending upward from 1991 (75) to 1997 (85), suggesting Professor Snape’s teaching methods might be improving—or students are studying harder!
Query 5: Top Students by Potion
Who excels at brewing specific potions, like the Draught of Peace?
PotionsGrades
| where Potion == "Draught of Peace"
| top 3 by Score desc
| project Student, House, Score
Explanation:
where Potion == "Draught of Peace": Filters for the specific potion.top 3 by Score desc: Selects the top 3 students by score.project Student, House, Score: Returns only the specified columns.
Sample Output:
Student House Score Hermione Granger Gryffindor 95 Luna Lovegood Ravenclaw 90 Draco Malfoy Slytherin 88
Hermione’s precision in potion-making shines!
Why KQL? And Why Pop Culture?
KQL’s simplicity and power make it ideal for analyzing structured data, whether it’s real-world logs or fictional datasets. By using pop culture, we make learning KQL fun and relatable:
Engagement: Querying Star Wars or Hogwarts data feels like exploring a story, not just crunching numbers.
Real-World Skills: The techniques used here—filtering, aggregating, and visualizing—apply to business, IT, or scientific data analysis.
Creativity: Fictional datasets encourage creative problem-solving, like deciding which smuggler to hire for a job or which house to join at Hogwarts.
Try It Yourself!
To experiment with KQL:
Set Up Azure Data Explorer: Use the free cluster at dataexplorer.azure.com.
Ingest Sample Data: Create tables for
SmugglerLogsandPotionsGradesusing the schemas above. You populate the tables using the downloads from the accompanying GitHub repo: https://github.com/rod-trent/pop-culture-kql-datasetsRun Queries: Copy-paste the queries above or tweak them to explore new questions, like “Which planet sees the most Kyber Crystal smuggling?” or “Which potion is the hardest to brew?”
Conclusion
KQL lets us dive into the data of our favorite fictional worlds, uncovering insights that bring stories to life. From tracking Han Solo’s smuggling empire to celebrating Hermione’s potion prowess, KQL makes data analysis both powerful and fun. So, grab your lightsaber or wand, fire up Azure Data Explorer, and start querying your favorite universes!
Happy querying, and may the Force (and data) be with you!