KQL Cooking Show: Recipes for Tasty Data Insights

Grab your data tables, preheat your Azure Data Explorer, and let’s get cooking!

Welcome to the KQL Cooking Show, where we whip up delicious data insights using Kusto Query Language (KQL)! In this episode, we’re serving two mouthwatering recipes: a Data Smoothie for a quick, refreshing insight and a Hearty Analytics Stew for a deep, flavorful analysis. Grab your data tables, preheat your Azure Data Explorer, and let’s get cooking!

Grab the datasets for this blog post: https://github.com/rod-trent/KQLCookingShow

Recipe 1: Data Smoothie

Dish Description: A light, refreshing blend of filtered events and summarized metrics, perfect for quick insights into user behavior.

Ingredients

• EventsTable: A table containing user activity logs with columns Timestamp, UserId, EventType, and SessionDuration.

• UsersTable: A table with user demographics, including UserId, AgeGroup, and Region.

• 1 cup of where operator: For filtering fresh data.

• 2 tbsp of join operator: To blend tables smoothly.

• A pinch of summarize operator: For that perfect aggregated finish.

• A dash of top operator: To garnish with the most relevant results.

Cooking Instructions

1. Prep the Base: Start by filtering EventsTable to include only recent events (e.g., last 7 days) to keep the smoothie fresh.

2. Blend the Flavors: Join EventsTable with UsersTable to enrich events with user demographics.

3. Smooth it Out: Summarize the data to calculate average session duration by age group and region.

4. Garnish: Select the top 5 results for a visually appealing presentation.

EventsTable
| where Timestamp > ago(7d)
| join kind=inner UsersTable on UserId
| summarize AvgSessionDuration = avg(SessionDuration) by AgeGroup, Region
| top 5 by AvgSessionDuration desc

Cooking Tips

• Optimize Filtering: Use where early in the query to reduce the dataset size before expensive operations like join. This keeps your query light and fast.

• Choose the Right Join: Use kind=inner for efficiency when you only need matching records. Avoid kind=leftouter unless you need all events, as it increases processing time.

• Indexing: Ensure Timestamp and UserId columns are indexed in your tables for faster filtering and joining.

Serving Suggestion

Serve this Data Smoothie in a dashboard for stakeholders needing a quick sip of user engagement trends. Pair with a time-series chart for extra zest!

Recipe 2: Hearty Analytics Stew

Dish Description: A rich, slow-cooked stew combining multiple data sources, perfect for uncovering deep insights into system performance over time.

Ingredients

• LogsTable: System logs with columns Timestamp, MachineId, LogLevel, ErrorCode, and ResponseTime.

• MachinesTable: Machine metadata with MachineId, Location, and MachineType.

• 3 cups of extend operator: To enrich the stew with calculated columns.

• 1 lb of summarize operator: For robust aggregation.

• A handful of bin operator: To simmer time-based data into manageable chunks.

• 2 sprigs of let statement: For reusable ingredients.

• A splash of render operator: To plate the stew beautifully.

Cooking Instructions

1. Set the Base: Define a reusable time range using a let statement to keep the stew consistent.

2. Chop the Data: Use bin to group logs into hourly intervals for a hearty texture.

3. Enrich the Broth: Add calculated columns with extend to flag high-response-time errors.

4. Simmer: Summarize error counts and average response times by machine type and location.

5. Plate: Render the results as a time chart for a warm, inviting presentation.

let TimeRange = ago(30d);
LogsTable
| where Timestamp > TimeRange
| join kind=inner MachinesTable on MachineId
| extend IsHighResponse = iif(ResponseTime > 500, 1, 0)
| where LogLevel == "Error" and IsHighResponse == 1
| summarize ErrorCount = count(), AvgResponseTime = avg(ResponseTime) by MachineType, Location, bin(Timestamp, 1h)
| render timechart

Cooking Tips

• Reuse Ingredients: Use let statements for constants like time ranges or thresholds to avoid hardcoding and improve readability.

• Time Binning: Choose an appropriate bin size (e.g., 1h or 1d) based on your analysis needs. Smaller bins increase granularity but may slow queries.

• Early Filtering: Filter on LogLevel and Timestamp before joining to reduce the dataset, making the stew cook faster.

• Visual Debugging: Use render during development to visualize results early and ensure your stew is simmering correctly.

Serving Suggestion

This Hearty Analytics Stew is best served in a performance monitoring dashboard. Pair with alerts for high error counts to keep your systems warm and toasty.

Chef’s Notes

• Experiment with Flavors: KQL is versatile! Try adding operators like mv-expand for array data or make-series for time-series forecasting to spice up your dishes.

• Performance Matters: Always profile your queries using the .show query command to identify bottlenecks and optimize cooking time.

• Clean Your Kitchen: Document your queries with comments (//) to make your recipes easy to follow for other data chefs.

Tune in next time for more KQL Cooking Show recipes, where we’ll bake a predictive analytics cake! Bon appétit, data chefs!