diff --git a/content/v2.0/process-data/common-tasks/downsample-data.md b/content/v2.0/process-data/common-tasks/downsample-data.md
index 93472b688..c0ef87c45 100644
--- a/content/v2.0/process-data/common-tasks/downsample-data.md
+++ b/content/v2.0/process-data/common-tasks/downsample-data.md
@@ -31,7 +31,8 @@ A separate bucket where aggregated, downsampled data is stored.
 To downsample data, it must be aggregated in some way.
 What specific method of aggregation you use depends on your specific use case,
 but examples include mean, median, top, bottom, etc.
-View [Flux's aggregate functions](#) for more information and ideas.
+View [Flux's aggregate functions](/v2.0/reference/flux/functions/transformations/aggregates/)
+for more information and ideas.
 
 ## Create a destination bucket
 By design, tasks cannot write to the same bucket from which they are reading.
@@ -45,8 +46,8 @@ The example task script below is a very basic form of data downsampling that doe
 1. Defines a task named "cq-mem-data-1w" that runs once a week.
 2. Defines a `data` variable that represents all data from the last 2 weeks in the
    `mem` measurement of the `system-data` bucket.
-3. Uses the [`aggregateWindow()` function](#) to window the data into 1 hour intervals
-   and calculate the average of each interval.
+3. Uses the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/)
+   to window the data into 1 hour intervals and calculate the average of each interval.
 4. Stores the aggregated data in the `system-data-downsampled` bucket under the
    `my-org` organization.
 
diff --git a/content/v2.0/process-data/write-a-task.md b/content/v2.0/process-data/write-a-task.md
index acfb31a13..3c0315297 100644
--- a/content/v2.0/process-data/write-a-task.md
+++ b/content/v2.0/process-data/write-a-task.md
@@ -51,7 +51,8 @@ in form fields when creating the task.
 {{% /note %}}
 
 ## Define a data source
-Define a data source using Flux's [`from()` function](#) or any other [Flux input functions](#).
+Define a data source using Flux's [`from()` function](/v2.0/reference/flux/functions/inputs/from/)
+or any other [Flux input functions](/v2.0/reference/flux/functions/inputs/).
 
 For convenience, consider creating a variable that includes the sourced data with
 the required time range and any relevant filters.
@@ -84,7 +85,7 @@ specific use case.
 The example below illustrates a task that downsamples data by calculating the average of set intervals.
 It uses the `data` variable defined [above](#define-a-data-source) as the data source.
 It then windows the data into 5 minute intervals and calculates the average of each
-window using the [`aggregateWindow()` function](#).
+window using the [`aggregateWindow()` function](/v2.0/reference/flux/functions/transformations/aggregates/aggregatewindow/).
 
 ```js
 data
@@ -100,7 +101,8 @@ _See [Common tasks](/v2.0/process-data/common-tasks) for examples of tasks commo
 In the vast majority of task use cases, once data is transformed, it needs to sent and stored somewhere.
 This could be a separate bucket with a different retention policy, another measurement, or even an alert endpoint _(Coming)_.
 
-The example below uses Flux's [`to()` function](#) to send the transformed data to another bucket:
+The example below uses Flux's [`to()` function](/v2.0/reference/flux/functions/outputs/to)
+to send the transformed data to another bucket:
 
 ```js
 // ...