![]() This section allows users to see exactly what they will be ingesting when configuring the xPath. The events and a description of what the events are can be found below this. If more than 200, the same will be done with a third template. ![]() To assist with this, the tool detects when there are more than 100 events and will generate a second set of xPath and a second template to deploy. Currently, DCRs have a limit of 100 items within xPath. The number of distinct event IDs is shown with the array of the IDs. ![]() If looking to manually add or exclude events, there is a section for manually adding/excluding events that will modify the xPath. The only category that is different will be the file path DCR as it leverages the existing UI for DCR's. This xPath is used when deploying the DCR. These event IDs are being converted to xPath in the background via a KQL function. Selecting a category will produce a preconfigu red array of event IDs and options for modifying the array. Recommended: Recommended event IDs based on Microsoft documentation.MITRE: Event IDs that align with the MITRE tactics.The Windows section is broken up into categories that determine which event IDs will be collected. When validation has passed, click create.Assign resources that should be subscribed to the DCR.If using CEF, click on ‘create new collection rule’ option.Click on either the Syslog or the CEF button to open the creation wizard.Click on the Linux DCR button to expand the options.These buttons open the existing experience for making data collection rules through the wizard provided by Azure Monitor. Useful Tools: This tab can be used to find useful workbooks and external tools that can assist with data collection rules, migration from MMA to AMA, and more.Simple reporting: This tab will show a simple breakdown of the type of DCR, the events that are being brought in, and the amount of data that each item is contributing to in the workspace.Dataflow and Transformation: This tab can be used to break down a selected data collection rule in order to show the data source, transformation KQL if it is configured, and the destination of the data per stream.If needed, there is a section to modify the template of a selected rule. It will also highlight items such as if a data collection endpoint is being used in a DCR and if there is ingestion transformation applied. This allows users to see what is already configured, what data they are ingesting, and where that data is going. Monitor/Modify Existing DCR’s: This tab can be used to review all existing data collection rules for an environment.The experience is streamlined so users can click buttons and switches in order to configure what data will be ingested. Identify Data Sources/Create New DCRs: This tab can be used to create new data collection rules.The workbook is broken up into 4 main tabs: To address this, a new workbook has been developed in order to make interacting with data collection rules easier, cleaner, and more efficient. These components are split up between Azure Log Analytics, Azure Monitor, and Microsoft Sentinel. It can be a little confusing when it comes to creating, monitoring, and modifying data collection rules from Azure Monitor. It is available today in the Workbooks Gallery within Microsoft Sentinel.* TLDR: This workbook serves as a toolkit for data collection rules to make creating, editing, and monitoring DCRs in an environment easier. I recommend reading it but alternatively there will be a video recording soon that will cover the workbook. For the next regression attempt, I will break out outcomes A & D separately.*Thank you to Jing Nghik for assisting with the creation of this toolkit and to the Customer Connection Program for testing this solution.* With the above as evidence, our next step is to focus back on only Outcomes A & D, and to disregard all other variables. In fact, the observed correlation coefficient between the two is over 90%. It tells us that foul tips are highly correlated to pure swinging strikes. Instead, we find that foul tips are better modeled using a multiplier for Outcome D. One would think that foul tips should theoretically arise from FanGraphs contact events. I might have expected a bit more weight for indexes B & E. It makes sense that whiffs are slightly negatively correlated with takes, and slightly positively correlated with contact. Outcomes C & F are the non-swinging ones, whereas B & E are the swinging with contact events. Intuitively, the signed results make sense. Indexes for outcomes C & F are slightly negative, while indexes for outcomes B & E are slightly positive. All of the other weights are negligible … but I do find one aspect interesting. Just as suspected, outcomes A & D are the key variables to the equation.
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