updated flux-related placeholder content
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@ -31,7 +31,8 @@ A separate bucket where aggregated, downsampled data is stored.
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To downsample data, it must be aggregated in some way.
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What specific method of aggregation you use depends on your specific use case,
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but examples include mean, median, top, bottom, etc.
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View [Flux's aggregate functions](#) for more information and ideas.
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View [Flux's aggregate functions](/v2.0/reference/flux/functions/transformations/aggregates/)
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for more information and ideas.
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## Create a destination bucket
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By design, tasks cannot write to the same bucket from which they are reading.
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@ -45,8 +46,8 @@ The example task script below is a very basic form of data downsampling that doe
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1. Defines a task named "cq-mem-data-1w" that runs once a week.
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2. Defines a `data` variable that represents all data from the last 2 weeks in the
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`mem` measurement of the `system-data` bucket.
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3. Uses the [`aggregateWindow()` function](#) to window the data into 1 hour intervals
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and calculate the average of each interval.
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3. Uses the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/)
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to window the data into 1 hour intervals and calculate the average of each interval.
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4. Stores the aggregated data in the `system-data-downsampled` bucket under the
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`my-org` organization.
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@ -51,7 +51,8 @@ in form fields when creating the task.
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{{% /note %}}
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## Define a data source
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Define a data source using Flux's [`from()` function](#) or any other [Flux input functions](#).
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Define a data source using Flux's [`from()` function](/v2.0/reference/flux/functions/inputs/from/)
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or any other [Flux input functions](/v2.0/reference/flux/functions/inputs/).
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For convenience, consider creating a variable that includes the sourced data with
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the required time range and any relevant filters.
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@ -84,7 +85,7 @@ specific use case.
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The example below illustrates a task that downsamples data by calculating the average of set intervals.
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It uses the `data` variable defined [above](#define-a-data-source) as the data source.
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It then windows the data into 5 minute intervals and calculates the average of each
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window using the [`aggregateWindow()` function](#).
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window using the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/).
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```js
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data
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@ -100,7 +101,8 @@ _See [Common tasks](/v2.0/process-data/common-tasks) for examples of tasks commo
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In the vast majority of task use cases, once data is transformed, it needs to sent and stored somewhere.
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This could be a separate bucket with a different retention policy, another measurement, or even an alert endpoint _(Coming)_.
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The example below uses Flux's [`to()` function](#) to send the transformed data to another bucket:
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The example below uses Flux's [`to()` function](/v2.0/reference/flux/functions/outputs/to)
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to send the transformed data to another bucket:
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```js
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// ...
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