TimeSeries -> Time Series

Author: wjohnsto

docs/operate/observability/prometheus/index-prometheus.mdx

@@ -1,7 +1,7 @@
 ---
 id: index-prometheus
 title: How to monitor Redis with Prometheus and Grafana for Real-Time Analytics
-sidebar_label: Using TimeSeries data model in Redis Stack along with Prometheus and Grafana
+sidebar_label: Using Time Series data model in Redis Stack along with Prometheus and Grafana
 slug: /operate/observability/prometheus
 authors: [ajeet]
 ---
@@ -37,13 +37,13 @@ Relational databases carry the overhead of locking and synchronization that aren
 - Time series data support (ingestion & querying), including full-text search
 - Probabilistic data structures
 
-### What is TimeSeries data model in Redis Stack?
+### What is the Time Series data model in Redis Stack?
 
 [Redis Stack](https://redis.io/docs/stack/about/) supports time-series data model that addresses the needs of handling time-series data. It removes the limitations enforced by relational databases and enables you to collect, manage, and deliver time-series data at scale. As an in-memory database, Redis can ingest over 500,000 records per second on a standard node. Our benchmarks show that you can ingest over 11.5 million records per second with a cluster of 16 Redis shards.
 
-TimeSeries support in Redis Stack is resource-efficient. With Redis, you can add rules to compact data by downSampling. For example, if you’ve collected more than one billion data points in a day, you could aggregate the data by every minute in order to downSample it, thereby reducing the dataset size to 1,440 data points (24 \* 60 = 1,440). You can also set data retention policies and expire the data by time when you don’t need them anymore. Redis allows you to aggregate data by average, minimum, maximum, sum, count, range, first, and last. You can run over 100,000 aggregation queries per second with sub-millisecond latency. You can also perform reverse lookups on the labels in a specific time range.
+Time Series support in Redis Stack is resource-efficient. With Redis, you can add rules to compact data by downSampling. For example, if you’ve collected more than one billion data points in a day, you could aggregate the data by every minute in order to downSample it, thereby reducing the dataset size to 1,440 data points (24 \* 60 = 1,440). You can also set data retention policies and expire the data by time when you don’t need them anymore. Redis allows you to aggregate data by average, minimum, maximum, sum, count, range, first, and last. You can run over 100,000 aggregation queries per second with sub-millisecond latency. You can also perform reverse lookups on the labels in a specific time range.
 
-### Notables features related to TimeSeries in Redis Stack includes:
+### Notables features related to Time Series in Redis Stack includes:
 
 - High volume inserts, low latency reads
 - Query by start time and end-time
@@ -56,9 +56,9 @@ TimeSeries support in Redis Stack is resource-efficient. With Redis, you can add
 
 Prometheus is an open-source systems monitoring and alerting toolkit. It collects and stores its metrics as time series data, i.e. metrics information. The metrics are numeric measurements in a time series, meaning changes recorded over time. These metrics are stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels. Metrics play an important role in understanding why your application is working in a certain way.
 
-### Prometheus remote storage adapter for TimeSeries data model of Redis Stack
+### Prometheus remote storage adapter for Time Series data model of Redis Stack
 
-In the [TimeSeries Database over Redis](https://github.com/RedisTimeSeries) organization you can find projects that help you integrate TimeSeries data model of Redis Stack with other tools, including Prometheus and Grafana. The Prometheus remote storage adapter for Redis is hosted [on GitHub here](https://github.com/RedisTimeSeries/prometheus-redistimeseries-adapter.) It’s basically a read/write adapter to use Redis Stack as a backend database. This timeSeries Adapter receives Prometheus metrics via the remote write, and writes to Redis.
+In the [Time Series Database over Redis](https://github.com/RedisTimeSeries) organization you can find projects that help you integrate Time Series data model of Redis Stack with other tools, including Prometheus and Grafana. The Prometheus remote storage adapter for Redis is hosted [on GitHub here](https://github.com/RedisTimeSeries/prometheus-redistimeseries-adapter.) It’s basically a read/write adapter to use Redis Stack as a backend database. This timeSeries Adapter receives Prometheus metrics via the remote write, and writes to Redis.
 
 ## Getting Started
 
@@ -175,7 +175,7 @@ It’s time to test drive a few demo scripts built by the Redis team. To start w
  git clone https://github.com/RedisTimeSeries/prometheus-demos
 ```
 
-This repo contains a set of basic demos showcasing the integration of TimeSeries data model of Redis Stack with Prometheus and Grafana. Let’s pick up a sensor script.
+This repo contains a set of basic demos showcasing the integration of Time Series data model of Redis Stack with Prometheus and Grafana. Let’s pick up a sensor script.
 
 ```
  python3 weather_station/sensors.py
@@ -195,9 +195,9 @@ Open up `https://HOSTIP:9090` to access the Prometheus dashboard for the sensor
 
 ### Further References:
 
-- [Prometheus remote storage adapter for TimeSeries with Redis Stack](https://github.com/RedisTimeSeries/prometheus-redistimeseries-adapter)
+- [Prometheus remote storage adapter for Time Series with Redis Stack](https://github.com/RedisTimeSeries/prometheus-redistimeseries-adapter)
 - [Remote Storage Integration](https://prometheus.io/docs/prometheus/latest/storage/#remote-storage-integrations)
-- [TimeSeries Demos with Redis Stack](https://github.com/RedisTimeSeries/prometheus-demos)
+- [Time Series Demos with Redis Stack](https://github.com/RedisTimeSeries/prometheus-demos)
 
 ##