Query Details
# Estimate Potential Savings by Moving Tables to Data Lake # Description The following query simulates cost savings if high-volume tables currently on Analytics were moved to Data Lake, using hypothetical prices. While this query could help support cost optimization decisions, don't forget to take into account processing and retention costs. ### Microsoft Sentinel ``` let AnalyticsPricePerGB = 2.48; // Example price per GB for Analytics let DataLakePricePerGB = 0.05; // Example price per GB for Data Lake Usage | where TimeGenerated >= startofday(ago(30d)) | where IsBillable == true | summarize IngestedGB = round(sum(Quantity) / 1000.0, 2) by DataType, Plan | where Plan == "Analytics" | extend CurrentCost = round(IngestedGB * AnalyticsPricePerGB, 2) | extend IfDataLakeCost = round(IngestedGB * DataLakePricePerGB, 2) | extend PotentialSavings = round(CurrentCost - IfDataLakeCost, 2) | where PotentialSavings > 0 | project DataType, IngestedGB, CurrentCost, IfDataLakeCost, PotentialSavings | order by PotentialSavings desc ``` ### Versioning | Version | Date | Comments | | ------------- |---------------| ---------------------------------------| | 1.0 | 17/05/2026 | Initial publish |
This query is designed to estimate potential cost savings by moving large data tables from an Analytics platform to a Data Lake. Here's a simple breakdown of what the query does:
Define Costs: It sets hypothetical prices for storing data: $2.48 per GB for Analytics and $0.05 per GB for Data Lake.
Filter Data: It looks at data usage from the last 30 days that is billable.
Calculate Ingested Data: It calculates the total amount of data ingested (in gigabytes) for each data type and plan, specifically focusing on the "Analytics" plan.
Calculate Costs: For each data type:
Determine Savings: It calculates the potential savings by subtracting the Data Lake cost from the current Analytics cost.
Filter and Display: It only shows data types where there are potential savings and orders them by the amount saved, from highest to lowest.
This analysis helps identify which data types could save the most money if moved to a Data Lake, but it also reminds users to consider other factors like processing and retention costs before making decisions.

Michalis Michalos
Released: May 17, 2026
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