Synapse Data Explorer (SDX) to Eventhouse Migration Capability (Preview)
Synapse Data Explorer (SDX), part of Azure Synapse Analytics, is an enterprise analytics service that enables you to explore, analyze, and visualize large volumes of data using the familiar Kusto Query Language (KQL). SDX has been in public preview since 2019. The evolution of Synapse Data Explorer The next generation of SDX offering is evolving to become Eventhouse, part of Real-Time Intelligence in Microsoft Fabric. Eventhouse offers the same powerful features and capabilities as SDX, but with enhanced scalability, performance, and security. Eventhouse is built on the same technology as SDX, and is compatible with all the applications, SDKs, integrations, and tools that work with SDX. For existing customers considering a move to Fabric, we are excited to offer a seamless migration capability. You can now migrate your Data Explorer pools from Synapse workspace to Eventhouse effortlessly. To initiate the migration of your SDX cluster to Eventhouse, simply create a support ticket. Our team will collaborate with you to ensure a smooth transition. Looking ahead, we plan to release a self-service API in early 2025, enabling you to manage the migration independently.The #1 factor in ADX/KQL database performance
The most important thing determining the performance of a KQL query is making sure that the minimum part of the data is scanned. In almost all cases a filter on a datetime column is used to determine what part of the data is relevant for the query results. The filter can be expressed in many ways on the actual table or a joined table. All variations are returning the correct results but the difference in performance can be 50X The different variations are described, and the reasons why are performant, and some are not.Timespan/duration values in KQL, Power Query and Power BI
In many scenarios event durations are very important. They can be measured in days or in milliseconds and anything in between. In real time analytics it is especially important. In this article I'll describe how such values are treated in KQL and what happens to them when they are brought into Power Query and eventually to Power BI. Finally, I'll provide my recommendation for treating these values in Power BI.Calculating distinct counts in Power BI with data in Kusto
Distinct counts using the dcount KQL function return estimated values. A new setting in Ower BI allows you to control how accurate (and how expensive) these counts will be You can also choose to use the new count_distinct function which return exact numbers but can be very expensive.Relationships between ADX tables on two databases or two clusters
When using relationships between two ADX tables, we always prefer a join to be used between the two tables What if the two tables are not in the same database? This article will explain how to ensure proper joins are generated for these case.
