Adding upstream version 1.16.0.upstream/1.16.0

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 2 insertions, 2 deletions

diff --git a/docs/why-netdata/1s-granularity.md b/docs/why-netdata/1s-granularity.md
index 089854543..0d12a2d41 100644
--- a/docs/why-netdata/1s-granularity.md
+++ b/docs/why-netdata/1s-granularity.md
@@ -34,13 +34,13 @@ So, the monitoring industry fails to massively provide high resolution metrics,
 2. Data collection needs optimization, otherwise it will significantly affect the monitored systems.
 3. Data collection is a lot harder, especially on busy virtual environments.
 
-## What does netdata do differently?
+## What does Netdata do differently?
 
 Netdata decentralizes monitoring completely. Each Netdata node is autonomous. It collects metrics locally, it stores them locally, it runs checks against them to trigger alarms locally, and provides an API for the dashboards to visualize them. This allows Netdata to scale to infinity.
 
 Of course, Netdata can centralize metrics when needed. For example, it is not practical to keep metrics locally on ephemeral nodes. For these cases, Netdata streams the metrics in real-time, from the ephemeral nodes to one or more non-ephemeral nodes nearby. This centralization is again distributed. On a large infrastructure, there may be many centralization points.
 
-To eliminate the error introduced by data collection latencies on busy virtual environments, Netdata interpolates collected metrics. It does this using microsecond timings, per data source, offering measurements with an error rate of 0.0001%. When running [in debug mode, netdata calculates this error rate](https://github.com/netdata/netdata/blob/36199f449852f8077ea915a3a14a33fa2aff6d85/database/rrdset.c#L1070-L1099) for every point collected, ensuring that the database works with acceptable accuracy.
+To eliminate the error introduced by data collection latencies on busy virtual environments, Netdata interpolates collected metrics. It does this using microsecond timings, per data source, offering measurements with an error rate of 0.0001%. When running [in debug mode, Netdata calculates this error rate](https://github.com/netdata/netdata/blob/36199f449852f8077ea915a3a14a33fa2aff6d85/database/rrdset.c#L1070-L1099) for every point collected, ensuring that the database works with acceptable accuracy.
 
 Finally, Netdata is really fast. Optimization is a core product feature. On modern hardware, Netdata can collect metrics with a rate of above 1M metrics per second per core (this includes everything, parsing data sources, interpolating data, storing data in the time series database, etc). So, for a few thousands metrics per second per node, Netdata needs negligible CPU resources (just 1-2% of a single core).