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Copy file name to clipboardExpand all lines: gdi/get-data-in/connect/aws/aws-console-ms.rst
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@@ -93,7 +93,6 @@ Connect to Splunk Observability Cloud from the AWS console
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Before you proceed to create your Metric Streams connection between your AWS and your Splunk Observability Cloud accounts in the AWS console, follow the steps in :ref:`aws-console-ms-start` to ensure you have an active AWS integration in your associated Splunk Observability Cloud account:
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* Make sure you selected :guilabel:`Streaming (AWS-managed)` as the ingestion method in the integration.
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* Make sure the AWS account you used to create the integration contains the required policies for Metric Streams, as described in :ref:`metricstreams_iampolicy`.
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To connect Splunk Observability Cloud from the AWS console, follow these steps:
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After you connect Splunk Observability Cloud with AWS, you can use Splunk Observability Cloud to track a series of metrics and analyze your AWS data in real time.
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- See the AWS official documentation for a list of the available AWS resources.
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- See :ref:`how to leverage data from integration with AWS <aws-post-install>` for more information.
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- See :ref:`how to leverage data from integration with AWS <aws-post-install>` for more information.
msgid"If you prefer to see sampled values, you can select the :strong:`Latest`rollup, or if you prefer to see the peaks and valleys, you can select the :strong:`Max` or :strong:`Min` rollups, respectively."
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msgid"If you prefer to see sampled values, you can select the :strong:`Latest`rollup, or if you prefer to see the peaks and valleys, you can select the :strong:`Max` or :strong:`Min` rollups, respectively."
msgid"In all likelihood, this has an impact similar to the :strong:`Average`rollup for a gauge: it provides an accurate representation of the data, and one whose visualization is aligned with how you typically use line or area charts."
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msgid"In all likelihood, this has an impact similar to the :strong:`Average`rollup for a gauge: it provides an accurate representation of the data, and one whose visualization is aligned with how you typically use line or area charts."
msgid"You can also run SignalFlow programs directly. For more information, see the :new-page:`SignalFlow API <https://dev.splunk.com/observability/docs/signalflow#SignalFlow-API/>`topic in the Splunk Observability Cloud Developer Guide."
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msgid"You can also run SignalFlow programs directly. For more information, see the :new-page:`SignalFlow API <https://dev.splunk.com/observability/docs/signalflow#SignalFlow-API/>`topic in the Splunk Observability Cloud Developer Guide."
"Aggregations operate across all of the data points at a single instance in time, for example the mean CPU utilization across a group of five servers at time t, t+1, t+2, and so on. The output of an aggregation is a single :term:`metric time series <Metric time series>` (MTS), where each "
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"data point represents the aggregation of all the data points over a specific period of time. For more information, see :new-page:`Aggregations <https://dev.splunk.com/observability/docs/signalflow/#Aggregations>`."
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"例えば、時刻 t、t+1、t+2 における5台のサーバーグループの平均 CPU 使用率などです。集約の出力は、単一の :term:`メトリック時系列<Metric time series>` (MTS)であり、各データポイントは、特定の期間におけるすべてのデータポイントの集約を表します。詳細については、:new-page:`集計<https://dev.splunk."
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"例えば、時刻 t、t+1、t+2 における5台のサーバーグループの平均 CPU 使用率などです。集約の出力は、単一の :term:`メトリック時系列<Metric time series>` (MTS)であり、各データポイントは、特定の期間におけるすべてのデータポイントの集約を表します。詳細については、:new-page:`集計<https://dev.splunk."
"Transformations operate in parallel on each MTS over a window of time and yield one output time series for each input time series. For example, the average CPU utilization for five servers over a rolling window of one day will display five MTS; each output value will be the moving average "
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"for that MTS over the previous 24 hours. For more information, see :new-page:`Transformations <https://dev.splunk.com/observability/docs/signalflow/#Transformations>`."
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msgstr""
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"変換は各 MTS に対し時間ウィンドウ上で並列に実行され、各入力時系列に対し1つの出力時系列が得られます。たとえば、5台のサーバーの1日の平均 CPU 使用率は、5つの MTS を表示し、各出力値はその MTS の過去24時間の移動平均となります。詳細については、:new-page:`変換<https://dev.splunk.com/observability/"
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"変換は各 MTS に対し時間ウィンドウ上で並列に実行され、各入力時系列に対し1つの出力時系列が得られます。たとえば、5台のサーバーの1日の平均 CPU 使用率は、5つの MTS を表示し、各出力値はその MTS の過去24時間の移動平均となります。詳細については、:new-page:`変換<https://dev.splunk.com/observability/"
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"docs/signalflow/#Transformations>` を参照してください。"
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#:../../analytics/signalflow.rst:52
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#,fuzzy
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msgid"See the following sections to learn more about the 3 types of transformations available, moving window, calendar window, and dashboard window. For examples of how to use transformation analytics in charts, see :ref:`gain-insights-through-chart-analytics`."
"The :guilabel:`Hide partial values` setting lets you optimize the output of a calendar window function, based on whether you are interested only in the final values calculated at the ends of cycles, as well as partial values calculated during a cycle. For example, if you have a cycle length "
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"of one day, hiding partial values means that you will only see one value for each day; you won't see how values change during the course of the day."
msgid"For more information about dashboard window transformations, see :new-page:`Dashboard window transformations <https://dev.splunk.com/observability/docs/signalflow/#Dashboard-window-transformations>`."
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