Self-Hosting Kusto
The article delves into self-hosting Kusto for timeseries analytics, detailing setup with the Kusto Emulator in Docker. It covers connecting, database management, data import, querying with KQL, and anomaly detection. Speed and efficiency are highlighted.
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The article discusses self-hosting Kusto, the engine behind Azure Data Explorer, for timeseries analytics. The author shares their experience setting up Kusto locally using the Kusto Emulator in a Docker container. They explain the process of connecting to Kusto, creating databases, managing tables, importing data, and querying results using KQL in Jupyter notebooks. Additionally, the author explores time series anomaly detection in KQL and provides a basic example. They highlight the speed and performance of the Kusto emulator for analytics tasks, emphasizing its efficiency in handling large data volumes. The author mentions encountering some challenges with error messages and debugging in Jupyter but overall finds the Kusto emulator to be fast and effective for their analytics needs.
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