Fix typos (#11782)

Co-authored-by: ilyam8 <ilya@netdata.cloud>

6 files changed, 11 insertions, 11 deletions

diff --git a/docs/Running-behind-lighttpd.md b/docs/Running-behind-lighttpd.md
index 8649158002..1e86f334f9 100644
--- a/docs/Running-behind-lighttpd.md
+++ b/docs/Running-behind-lighttpd.md
@@ -14,7 +14,7 @@ $HTTP["url"] =~ "^/netdata/" {
 }
 ```
 
-If you have older lighttpd you have to use a chain (such as bellow), as explained [at this stackoverflow answer](http://stackoverflow.com/questions/14536554/lighttpd-configuration-to-proxy-rewrite-from-one-domain-to-another).
+If you have older lighttpd you have to use a chain (such as below), as explained [at this stackoverflow answer](http://stackoverflow.com/questions/14536554/lighttpd-configuration-to-proxy-rewrite-from-one-domain-to-another).
 
 ```txt
 $HTTP["url"] =~ "^/netdata/" {
diff --git a/docs/dashboard/import-export-print-snapshot.mdx b/docs/dashboard/import-export-print-snapshot.mdx
index e49a0063ff..7e94a52c8f 100644
--- a/docs/dashboard/import-export-print-snapshot.mdx
+++ b/docs/dashboard/import-export-print-snapshot.mdx
@@ -39,7 +39,7 @@ Some caveats and tips to keep in mind:
 
 - Only metrics in the export timeframe are available to you. If you zoom out or pan through time, you'll see the
   beginning and end of the snapshot.
-- Charts won't update with new inforamtion, as you're looking at a static replica, not the live dashboard.
+- Charts won't update with new information, as you're looking at a static replica, not the live dashboard.
 - The import is only temporary. Reload your browser tab to return to your node's real-time dashboard.
 
 ## Export a snapshot
diff --git a/docs/guides/monitor-cockroachdb.md b/docs/guides/monitor-cockroachdb.md
index 0ff9f3c772..0307381e3c 100644
--- a/docs/guides/monitor-cockroachdb.md
+++ b/docs/guides/monitor-cockroachdb.md
@@ -13,7 +13,7 @@ maximum granularity using Netdata. Collect more than 50 unique metrics and put t
 designed for better visual anomaly detection.
 
 Netdata itself uses CockroachDB as part of its Netdata Cloud infrastructure, so we're happy to introduce this new
-collector and help others get started with it straightaway.
+collector and help others get started with it straight away.
 
 Let's dive in and walk through the process of monitoring CockroachDB metrics with Netdata.
 
diff --git a/docs/guides/monitor/anomaly-detection.md b/docs/guides/monitor/anomaly-detection.md
index 1b224b9705..2d8b6d1d6e 100644
--- a/docs/guides/monitor/anomaly-detection.md
+++ b/docs/guides/monitor/anomaly-detection.md
@@ -123,7 +123,7 @@ configure the collector to monitor charts from the
 log](https://learn.netdata.cloud/docs/agent/collectors/go.d.plugin/modules/weblog) collectors.
 
 `charts_regex` allows for some basic regex, such as wildcards (`*`) to match all contexts with a certain pattern. For
-example, `system\..*` matches with any chart wit ha context that begins with `system.`, and ends in any number of other
+example, `system\..*` matches with any chart with a context that begins with `system.`, and ends in any number of other
 characters (`.*`). Note the escape character (`\`) around the first period to capture a period character exactly, and
 not any character.
 
diff --git a/docs/guides/monitor/statsd.md b/docs/guides/monitor/statsd.md
index a4d06043e0..e4f04c5752 100644
--- a/docs/guides/monitor/statsd.md
+++ b/docs/guides/monitor/statsd.md
@@ -111,7 +111,7 @@ Find more details about family and context in our [documentation](/web/README.md
 Now, having decided on how we are going to group the charts, we need to define how we are going to group metrics into different charts. This is particularly important, since we decide:
 
 - What metrics **not** to show, since they are not useful for our use-case.
-- What metrics to consolidate into the same charts, so as to reduce noice and increase visual correlation.
+- What metrics to consolidate into the same charts, so as to reduce noise and increase visual correlation.
 
 The dimension option has this syntax: `dimension = [pattern] METRIC NAME TYPE MULTIPLIER DIVIDER OPTIONS`
 
diff --git a/docs/guides/python-collector.md b/docs/guides/python-collector.md
index 0478bffe0c..b8facd9f02 100644
--- a/docs/guides/python-collector.md
+++ b/docs/guides/python-collector.md
@@ -24,7 +24,7 @@ prebuilt method for collecting your required metric data.
 
 In this tutorial, you'll learn how to leverage the [Python programming language](https://www.python.org/) to build a
 custom data collector for the Netdata Agent. Follow along with your own dataset, using the techniques and best practices
-covered here, or use the included examples for collecting and organizing eithre random or weather data.
+covered here, or use the included examples for collecting and organizing either random or weather data.
 
 ## What you need to get started
 
@@ -48,7 +48,7 @@ The basic elements of a Netdata collector are:
 - `ORDER[]`: A list containing the charts to be displayed.
 - `CHARTS{}`: A dictionary containing the details for the charts to be displayed.
 - `data{}`: A dictionary containing the values to be displayed.
-- `get_data()`: The basic function of the plugin which will retrun to Netdata the correct values.
+- `get_data()`: The basic function of the plugin which will return to Netdata the correct values.
 
 Let's walk through these jobs and elements as independent elements first, then apply them to example Python code.
 
@@ -138,7 +138,7 @@ correct values.
 The `python.d` plugin has a number of framework classes that can be used to speed up the development of your python
 collector. Your class can inherit one of these framework classes, which have preconfigured methods.
 
-For example, the snippet bellow is from the [RabbitMQ
+For example, the snippet below is from the [RabbitMQ
 collector](https://github.com/netdata/netdata/blob/91f3268e9615edd393bd43de4ad8068111024cc9/collectors/python.d.plugin/rabbitmq/rabbitmq.chart.py#L273).
 This collector uses an HTTP endpoint and uses the `UrlService` framework class, which only needs to define an HTTP
 endpoint for data collection.
@@ -298,7 +298,7 @@ class Service(SimpleService):
     def get_data(self):
         #The data dict is basically all the values to be represented
         # The entries are in the format: { "dimension": value}
-        #And each "dimension" shoudl belong to a chart.
+        #And each "dimension" should belong to a chart.
         data = dict()
 
         self.populate_data()
@@ -356,7 +356,7 @@ chart:
 Next, time to add one more chart that visualizes the average, minimum, and maximum temperature values.
 
 Add a new entry in the `CHARTS` dictionary with the definition for the new chart. Since you want three values
-represented in this this chart, add three dimensions. You shoudl also use the same `FAMILY` value in the charts (`TEMP`)
+represented in this this chart, add three dimensions. You should also use the same `FAMILY` value in the charts (`TEMP`)
 so that those two charts are grouped together.
 
 ```python
@@ -418,7 +418,7 @@ configuration in [YAML](https://www.tutorialspoint.com/yaml/yaml_basics.htm) for
 - Create a configuration file in the same directory as the `<plugin_name>.chart.py`. Name it `<plugin_name>.conf`.
 - Define a `job`, which is an instance of the collector. It is useful when you want to collect data from different
   sources with different attributes. For example, we could gather data from 2 different weather stations, which use
-  different temperature measures: Fahrenheit and Celcius.
+  different temperature measures: Fahrenheit and Celsius.
 - You can define many different jobs with the same name, but with different attributes. Netdata will try each job
   serially and will stop at the first job that returns data. If multiple jobs have the same name, only one of them can
   run. This enables you to define different "ways" to fetch data from a particular data source so that the collector has