Introduction
In Part 3 of this series, we covered how to onboard Microsoft Sentinel Data Lake. Once onboarding is complete, the next step is learning how to query your data effectively. Efficient Kusto Query Language (KQL) queries are critical for SOC teams during historical investigations. Well-structured queries reduce costs, accelerate investigations, and reveal meaningful patterns. Poorly written queries, however, increase spend and slow down response. Importantly, managing data within a Sentinel Data Lake effectively is key to leveraging these queries fully.
Therefore, in this post we’ll focus on best practices for KQL queries in that help you work faster and smarter.
📚 Reference: Query Microsoft Sentinel Data Lake
1. Filter Data Early
First, apply filters at the start of your query. By limiting time and data sources, you reduce the scanned volume and lower costs in your Sentinel Data Lake analysis.
SecurityEvent
| where TimeGenerated > ago(7d)
| where AccountType == "User"
| summarize count() by Account
As a result, analysts receive relevant results quickly without processing unnecessary data.
2. Limit the Time Range
Next, constrain queries to specific time windows. Wide-open queries can scan massive datasets, which drives up costs.
SigninLogs
| where TimeGenerated between (datetime(2025-09-01) .. datetime(2025-09-07))
Consequently, this approach keeps your analysis focused on the time period that matters, optimized by the security data lake’s capabilities involving Sentinel systems.
3. Summarize for Trends
Often, SOC analysts need patterns instead of raw event streams. Therefore, use summarization operators like count, top, avg, or percentile to produce concise insights.
SigninLogs
| where TimeGenerated > ago(24h)
| summarize Failures = count() by UserPrincipalName
| top 10 by Failures
This query surfaces the most affected accounts in seconds, helping your team prioritize response based on the data collected.
4. Project Only Needed Fields
In addition, use project to return only the fields you need. Large tables contain dozens of columns, but not all are necessary. Selecting fewer fields speeds up queries and reduces storage overhead.
SecurityEvent
| where TimeGenerated > ago(1d)
| project TimeGenerated, Account, EventID
5. Reuse Queries with Functions
Finally, build KQL functions to package common logic. Functions save time, reduce errors, and promote consistency.
let PrivilegedAccounts = datatable(Account:string) ["admin","secadmin","globaladmin"];
SigninLogs
| where UserPrincipalName in (PrivilegedAccounts)
| summarize Logins = count() by UserPrincipalName
As a result, your team can quickly track high-risk activity without rewriting queries, ensuring the security data stays organized within a Sentinel Data Lake.
Conclusion
KQL is the backbone of Microsoft Sentinel Data Lake analysis. By filtering early, narrowing time ranges, summarizing for trends, projecting only relevant fields, and reusing functions, your SOC improves both query performance and cost efficiency.
This completes the first three parts of the Unlocking Scalable Security Analytics series:
- Part 1: Why pair Sentinel with a Data Lake
- Part 2: How integration slashes costs
- Part 3: How to set up Sentinel Data Lake
👉 Next week, we’ll explore how further into how to create KQL jobs in Sentinel Data Lake and writing results to Analytics Tier
📚 Reference: Microsoft Data Lake Querying Guide