Chore: Implement tips by CodeRabbit: analytics, industrial, time series

Author: amotl

docs/explain/analytics/bitmovin.md

@@ -13,7 +13,7 @@ first commercial adaptive streaming player and deployed the first
 software-defined encoding service that runs on any cloud platform.
 
 The use-case of Bitmovin illustrates why traditional databases are
-not capable to deal with so many data records and keep them all
+incapable of handling so many data records while keeping them all
 available for querying in real time.
 
 > CrateDB enables use cases we couldn't satisfy with other
@@ -63,7 +63,7 @@ stream broadcasting provider, produces billions of rows of data and stores
 them in CrateDB, allowing their customers to do analytics on it.
 
 One of their product's subsystems, a video analytics component, required to
-serve real-time analytics on very large and fast-moving data, so they needed
+serve real-time analytics on massive, fast-moving data, so they needed
 to find a performing database at the right cost.
 
 :::{article-info}

docs/explain/industrial/index.md

@@ -18,7 +18,7 @@ The complexities of industrial big data are characterized by its high variety,
 unstructured features, different data sampling rates, and how these attributes
 influence data storage, retention, and integration.
 
-Learn how to use CrateDB in long term storage and analytics scenarios for
+Learn how to use CrateDB in long-term storage and analytics scenarios for
 industrial / IIoT / Industry 4.0 application scenarios within
 engineering, manufacturing, production, and logistics, as well as other
 operational domains, or within similar environments where billions of data

docs/explain/industrial/rauch.md

@@ -32,11 +32,11 @@ ensure real-time production quality control.
 :::{card} Scalable, high-performance database for FMCG
 :link: https://cratedb.com/fmcg-database
 :link-type: url
-CrateDB helps Rauch to identify and predict production issues and gives access
+CrateDB helps Rauch to identify and predict production issues and enables access
 to production data for many years, with no need for additional infrastructure
 while storing one to ten billion records in CrateDB at the same time.
 
-CrateDB enhances FMCG operations by optimizing shopfloor efficiency through
+CrateDB enhances FMCG operations by optimizing shop floor efficiency through
 real-time equipment monitoring and workflow optimization. It ensures product
 quality with integrated quality control measures, reducing defects and waste.
 Additionally, CrateDB enhances compliance and traceability by enabling batch

docs/explain/industrial/tgw.md

@@ -149,7 +149,8 @@ digital twins of physical warehouses around the world. The unique set
 of features is suitable for storing and querying complex industrial big data with
 high variety, unstructured features, and at different data sampling rates.
 
-**What's inside**
+:::{rubric} What's inside
+:::
 
 - The Complexity of IoT Data: An examination of the unique properties of
   industrial IoT data, including slow-moving structured information and

docs/explain/time-series/fundamentals.md

@@ -15,7 +15,7 @@ with CrateDB.
 
 ## Getting started
 
-After evaluating [connectivity options](#connect), you would like to get
+After evaluating {ref}`connectivity options <connect>`, you would like to get
 hands-on with CrateDB. We prepared a few introductory tutorials, some of
 them in executable forms, to demonstrate CrateDB's features to work with
 time series data on the spot. You may want to use them as starting points

docs/explain/time-series/index.md

@@ -69,7 +69,7 @@ Machine Learning on Time Series Data: EDA, Decomposition, AutoML.
 :::
 
 
-:::{grid-item-card} {material-outlined}`manage_history;2em` Long term storage
+:::{grid-item-card} {material-outlined}`manage_history;2em` Long-term storage
 :link: timeseries-longterm
 :link-type: ref
 :link-alt: About storing time series data for the long term
@@ -90,17 +90,17 @@ Optimizing storage for historic time series data.
 :::{seealso}
 
 **Domains:**
-[](#analytics) •
-[](#industrial) •
-[](#machine-learning) •
-[](#metrics-store)
+{ref}`analytics` •
+{ref}`industrial` •
+{ref}`machine-learning` •
+{ref}`metrics-store`
 
 **Features:**
-[](#connect) •
-[](#querying) •
-[](#document) •
-[](#fulltext) •
-[](#geospatial)
+{ref}`connect` •
+{ref}`querying` •
+{ref}`document` •
+{ref}`fulltext` •
+{ref}`geospatial`
 
 **Product:**
 [Time Series Data] •
@@ -114,7 +114,7 @@ Optimizing storage for historic time series data.
 Fundamentals <fundamentals>
 Advanced analysis <analysis>
 video
-Long term store <longterm>
+Long-term store <longterm>
 :::
 
 

docs/explain/time-series/longterm.md

@@ -1,6 +1,6 @@
 (timeseries-longterm)=
 (timeseries-long-term-storage)=
-# Time series long term storage
+# Time series long-term storage
 
 CrateDB stores large volumes of data, keeping it accessible for querying
 and insightful analysis, even considering historic data records.
@@ -45,12 +45,12 @@ a dedicated layout, and querying using the UNNEST table function.
 **CrateDB as metrics and log data store for the long term**
 
 Store and analyze high volumes of system monitoring information.
-Read more about using CrateDB as [](#metrics-store).
+Read more about using CrateDB as {ref}`metrics-store`.
 :::
 
 :::{grid-item}
 :columns: 3
-{tags-primary}`Long Term Storage`
+{tags-primary}`Long-term Storage`
 {tags-primary}`Metrics`
 {tags-primary}`Logging`
 :::
@@ -65,13 +65,13 @@ Read more about using CrateDB as [](#metrics-store).
 **CrateDB provides real-time analytics on raw data stored for the long term**
 
 Keep massive amounts of data ready in the hot zone for analytics purposes.
-Read more about using CrateDB for [](#analytics).
+Read more about using CrateDB for {ref}`analytics`.
 :::
 
 :::{grid-item}
 :columns: 3
-{tags-primary}`Long Term Storage`
-{tags-primary}`Real-Time Analytics`
+{tags-primary}`Long-term storage`
+{tags-primary}`Real-time analytics`
 :::
 
 ::::

docs/explain/time-series/video.md

@@ -20,7 +20,7 @@ CrateDB simplifies the complexity of managing time-series data.
 It provides a comprehensive solution for storing, querying, and extracting
 insights from large-scale and high-volume time-series datasets.
 
-Learn more about CrateDB and [](#timeseries).
+Learn more about CrateDB and {ref}`timeseries`.
 :::
 
 :::{grid-item}
@@ -54,7 +54,7 @@ Afterwards, it will demonstrate how to export data from CrateDB to a local file
 system, using the [`COPY TO`] statement.
 
 For more information about how to import and export
-data from/into CrateDB, please refer to [](#import-export).
+data from/into CrateDB, please refer to {ref}`import-export`.
 :::
 
 :::{grid-item}
@@ -120,7 +120,7 @@ strategy for your time series data in CrateDB.
 Learn how Bitmovin leverages CrateDB to support real-time analytics on
 top of tracking data from their video streaming broadcasting system.
 
-- [](#bitmovin)
+- {ref}`bitmovin`
 :::
 
 :::{grid-item}
@@ -146,14 +146,14 @@ top of tracking data from their video streaming broadcasting system.
 **Industrial Analytics Platform, High-Speed Production Lines, and Logistics**
 
 Learn how ABB, Rauch, SPGo!, and TGW leverage CrateDB to support their
-application platforms, high-speed shop-floor production lines, machine
+application platforms, high-speed shop floor production lines, machine
 monitoring solutions, and logistics databases for warehouses around the
 world.
 
-- [](#abb)
-- [](#rauch)
-- [](#spgo)
-- [](#tgw)
+- {ref}`abb`
+- {ref}`rauch`
+- {ref}`spgo`
+- {ref}`tgw`
 :::
 
 :::{grid-item}

docs/topic/migrate/rockset/index.md

@@ -288,8 +288,8 @@ Learn how to migrate your database use cases and workloads from Rockset to Crate
 - [MongoDB CDC Relay]
 :::{rubric} General I/O
 :::
-- [Data loading](#etl) with CrateDB.
-- [](#cdc) with CrateDB.
+- {ref}`Data loading <etl>` with CrateDB.
+- {ref}`cdc` with CrateDB.
 ::::
 
 ::::{grid-item-card}

docs/topic/migrate/rockset/query.md

@@ -70,16 +70,16 @@ under a different name, or there may be a simple workaround for your use cases.
 | APPROX_DISTINCT(x[, e])| `hyperloglog_distinct`  |
 | ARRAY_CONCAT(array1, array2, ...)| `array_cat`  |
 | ARRAY_CONTAINS(array, element)| `element = ANY (array)`  |
-| ARRAY_CREATE(val1, val2, ...)| `[val1, val2, ...]` or `_array(val1,val2, ...)`  |
+| ARRAY_CREATE(val1, val2, ...)| `[val1, val2, ...]` or `_array(val1,val2, ...)` |
 | ARRAY_DISTINCT(array)| `array_unique`  |
 | ARRAY_EXCEPT(array1, array2)| `array_unique(array_difference(array1, array2))` |
 | ARRAY_FLATTEN(array)| `array_unnest`  |
-| ARRAY_INTERSECT(array1, array2)| ` array(select DISTINCT a FROM UNNEST(array1) a WHERE a IN (SELECT UNNEST(array2)))`  |
+| ARRAY_INTERSECT(array1, array2)| ` array(select DISTINCT a FROM UNNEST(array1) a WHERE a IN (SELECT UNNEST(array2)))` |
 | ARRAY_JOIN(array, delimiter, nullReplacement)| [`array_to_string`]  |
-| ARRAY_MAP(function_name, array)| `(select array_agg(function_name(unnest)) from unnest(array))`  |
+| ARRAY_MAP(function_name, array)| `(select array_agg(function_name(unnest)) from unnest(array))` |
 | ARRAY_REMOVE(array, val)| `array_difference(array,[val])`  |
-| ARRAY_SHUFFLE(array)| `array(select unnest from unnest(array) ORDER BY random())`  |
-| ARRAY_SORT(array)| `array(select unnest from unnest(array) ORDER BY unnest)`  |
+| ARRAY_SHUFFLE(array)| `array(select unnest from unnest(array) ORDER BY random())` |
+| ARRAY_SORT(array)| `array(select unnest from unnest(array) ORDER BY unnest)` |
 | ARRAY_UNION(array1, array2)| `array_unique`  |
 | ASINH(x)| `LN(x + SQRT((x * x) + 1))`   |
 | ATANH(x)| `0.5*ln((1+x)/(1-x))`  |
@@ -112,7 +112,7 @@ under a different name, or there may be a simple workaround for your use cases.
 | MILLISECONDS(n)| `AGE(n::LONG,0)`  |
 | MINUTES(n)| `'n MINUTES'::INTERVAL`  |
 | MONTHS(n)| `'n MONTHS'::INTERVAL`  |
-| PARSE_DATE_ISO8601(string)| `date_trunc('day',string::TIMESTAMP)`  |
+| PARSE_DATE_ISO8601(string)| `date_trunc('day',string::TIMESTAMP)` |
 | PARSE_DATETIME_ISO8601(string)| `string::TIMESTAMP`  |
 | POSITION(substring IN string)| `strpos(string , substring)`  |
 | POW(x, y)| `power(x,y)`  |
@@ -121,7 +121,7 @@ under a different name, or there may be a simple workaround for your use cases.
 | SEQUENCE(start, stop[, step])| `generate_series`  |
 | SIGN(x)| See [^sign] for CrateDB <5.8  |
 | SPLIT(string, delimiter)[index]| `split_part(string, delimiter, index)` |
-| ST_ASTEXT(geography)| See [](#ST_ASTEXT) for `POLYGON`s  |
+| ST_ASTEXT(geography)| See {ref}`ST_ASTEXT` for `POLYGON`s  |
 | ST_GEOGFROMTEXT(well_known_text)| `well_known_text::geo_shape`  |
 | ST_GEOGPOINT(longitude, latitude)| `[longitude, latitude]::geo_point`  |
 | ST_INTERSECTS(geography_a, geography_b)| `intersects(geo_shape, geo_shape)`  |