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
, andSessionDuration
.UsersTable: A table with user demographics, including
UserId
,AgeGroup
, andRegion
.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
Prep the Base: Start by filtering
EventsTable
to include only recent events (e.g., last 7 days) to keep the smoothie fresh.Blend the Flavors: Join
EventsTable
withUsersTable
to enrich events with user demographics.Smooth it Out: Summarize the data to calculate average session duration by age group and region.
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 likejoin
. This keeps your query light and fast.Choose the Right Join: Use
kind=inner
for efficiency when you only need matching records. Avoidkind=leftouter
unless you need all events, as it increases processing time.Indexing: Ensure
Timestamp
andUserId
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
, andResponseTime
.MachinesTable: Machine metadata with
MachineId
,Location
, andMachineType
.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
Set the Base: Define a reusable time range using a
let
statement to keep the stew consistent.Chop the Data: Use
bin
to group logs into hourly intervals for a hearty texture.Enrich the Broth: Add calculated columns with
extend
to flag high-response-time errors.Simmer: Summarize error counts and average response times by machine type and location.
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
andTimestamp
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 ormake-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!