Analyzing data with KQL
KQL queries are the primary tool for data exploration and analysis on Data Explorer pools. As with Structured Query Language (SQL), KQL uses a hierarchical structure to organize databases, tables, and columns and offers language elements to enable data retrieval. Unlike SQL, however, KQL supports read-only statements only, which makes sense since analytical data is meant for exploration and analysis, not for updates or deletions.
KQL gained popularity due to its support for pattern discovery, anomaly detection, statistical modeling, time series analysis, and other features. Several Azure services such as Application Insights, Log Analytics, and Azure Monitor (to name a few) offer support for the exploration of log data using KQL, which also helped increase the popularity of the language among Azure professionals.
Most KQL queries follow the pattern of tabular expression statements and have the following syntax:
Source | Operator A | Operator B | …...