TSML Primer: Add article series about "Machine Learning for Time Series Data"

.lycheeignore

@@ -0,0 +1,2 @@
+# https://lychee.cli.rs/recipes/excluding-paths/
+https://www.youtube-nocookie.com/

docs/admin/sharding-partitioning.rst

@@ -64,7 +64,7 @@ partition as a set of shards. For each partition, the number of shards defined
 by ``CLUSTERED INTO x SHARDS`` are created, when a first record with a specific 
 ``partition key`` is inserted.
 
-In the following example - which represents a very simple time-series use-case 
+In the following example - which represents a very simple time series use-case
 - we added another column ``part`` that automatically generates the current 
 month upon insertion from the ``ts`` column. The ``part`` column is further used 
 as the ``partition key``.
@@ -132,12 +132,12 @@ Then, to calculate the number of shards, you should consider that the size of ea
 shard should roughly be between 5 - 100 GB, and that each node can only manage
 up to 1000 shards.
 
-Time-series example
+Time series example
 -------------------
 
 To illustrate the steps above, let's use them on behalf of an example. Imagine
 you want to create a *partitioned table* on a *three-node cluster* to store
-time-series data with the following assumptions:
+time series data with the following assumptions:
 
 - Inserts: 1.000 records/s
 - Record size: 128 byte/record

docs/build.json

@@ -1,5 +1,5 @@
 {
   "schemaVersion": 1,
   "label": "docs build",
-  "message": "2.1.1"
+  "message": "2.1.2"
 }

docs/conf.py

@@ -15,6 +15,8 @@
     r"https://cratedb.com/wp-content/uploads/2018/11/copy_from_population_data.zip",
     # Forbidden by Stack Overflow.
     r"https://stackoverflow.com/.*",
+    # HTTPSConnectionPool(host='aka.ms', port=443): Read timed out.
+    r"https://aka.ms/vs/.*",
 ]
 
 if "sphinx.ext.intersphinx" not in extensions:

docs/domain/document/index.md

@@ -9,8 +9,11 @@ Storing documents in CrateDB provides the same development convenience like the
 document-oriented storage layer of Lotus Notes / Domino, CouchDB, MongoDB, and
 PostgreSQL's `JSON(B)` types.
 
+- [](inv:crate-reference#type-object)
 - [](inv:cloud#object)
+- [CrateDB Objects]
 - [Unleashing the Power of Nested Data: Ingesting and Querying JSON Documents with SQL]
 
 
+[CrateDB Objects]: https://youtu.be/aQi9MXs2irU?feature=shared
 [Unleashing the Power of Nested Data: Ingesting and Querying JSON Documents with SQL]: https://youtu.be/S_RHmdz2IQM?feature=shared

docs/domain/industrial/index.md

@@ -5,7 +5,7 @@
 # Industrial Data
 
 Learn how to use CrateDB in industrial / IIoT / Industry 4.0 scenarios within
-engineering, manufacturing, and other operational domains.
+engineering, manufacturing, production, and other operational domains.
 
 In the realm of Industrial IoT, dealing with diverse data, ranging from
 slow-moving structured data, to high-frequency measurements, presents unique
@@ -15,24 +15,110 @@ 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.
 
-Today's warehouses are complex systems with a very high degree of automation.
-The key to the successful operation of these warehouses lies in having a
-holistic view on the entire system based on data from various components like
-sensors, PLCs, embedded controllers and software systems.
 
+(rauch)=
+## Rauch Insights
+
+::::{info-card}
+
+:::{grid-item}
+:columns: 8
+
+{material-outlined}`data_exploration;2em`   **Rauch: High-Speed Production Lines**
+
+_Scaling a high-speed production environment with CrateDB._
+
+Rauch is filling 33 cans per second and how that adds up to 400 data records
+per second which are being processed, stored, and analyzed. In total, they are
+within the range of one to ten billion records persisted in CrateDB.
+
+- [Rauch: High-Speed Production Lines]
+
+The use-case of Rauch demonstrates why traditional databases weren't capable to
+deal with so many data records and unstructured data. The benefits of CrateDB
+made Rauch choose it over other databases, such as PostgreSQL compatibility,
+the support for unstructured data, and its excellent customer support.
+
+:Industry: {tags-secondary}`Food` {tags-secondary}`Packaging` {tags-secondary}`Production`
+:Tags: {tags-primary}`SCADA` {tags-primary}`MDE` {tags-primary}`Data Historian` {tags-primary}`Industrial IoT` {tags-primary}`PLC`
+:::
+
+:::{grid-item}  
+:columns: 4
+
+<iframe width="240" src="https://www.youtube-nocookie.com/embed/gJPmJ0uXeVs?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+**Date:** 28 Jun 2022 \
+**Speaker:** Arno Breuss
+:::
 
+::::
 
+
+(tgw)=
 ## TGW Insights
 
+
+::::{info-card}
+
+:::{grid-item}
+:columns: 8
+
+{material-outlined}`inventory;2em` &nbsp; **TGW: Data acquisition in high-speed logistics**
+
+_Storing, querying, and analyzing industrial IoT data and metadata without
+much hassle._
+
+Today's warehouses are complex systems with a very high degree of automation.
+
+TGW Logistics Group implements key factors to the successful operation of these
+warehouses, by having a holistic view on the entire system acquiring data from
+various components like sensors, PLCs, embedded controllers, and software
+systems.
+
+- [TGW: Fixing data silos in a high-speed logistics environment]
+
+TGW states that all these components can be seen as "data silos",
+distributed across the entire site, each of them storing just some pieces of
+information in various data structures and different ways to access it.
+
 After trying multiple database systems, TGW Logistics moved to CrateDB for
-its ability to aggregate different data formats and ability to query this
-information without much hassle.
-
+its ability to aggregate different data formats and the ability to query this
+information without further ado.
+
+:Industry: {tags-secondary}`Logistics` {tags-secondary}`Shipping`
+:Tags: {tags-primary}`SCADA` {tags-primary}`MDE` {tags-primary}`Data Historian` {tags-primary}`Industrial IoT` {tags-primary}`PLC`
+:::
+
+:::{grid-item} &nbsp;
+:columns: 4
+
+<iframe width="240" src="https://www.youtube-nocookie.com/embed/6dgjVQJtSKI?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+**Date:** 22 Jun 2022 \
+**Speakers:** Alexander Mann, Jan Weber
+:::
+
+::::
+
+
+
+::::{info-card}
+
+:::{grid-item}
+:columns: 8
+
+{material-outlined}`dashboard;2em` &nbsp; **TGW: Challenges in storing and analyzing industrial data**
+
+_Not All Time-Series Are Equal: Challenges in Storing and Analyzing Industrial Data._
+
 In the second presentation, you will learn how TGW leverages CrateDB to build
-digital twins of physical warehouses around the world.
+digital twins of physical warehouses around the world, by using its unique set
+of features suitable for storing and querying complex industrial big data with
+high variety, unstructured features, and at different data frequencies.
 
-- [Fixing data silos in a high-speed logistics environment]
-- [Challenges of Storing and Analyzing Industrial Data]
+- [CrateDB: Challenges in industrial data]
+- [TGW: Storing and analyzing real-world industrial data]
 
 **What's inside**
 
@@ -47,6 +133,31 @@ digital twins of physical warehouses around the world.
 - Real-World Applications: Exploration of actual customer use cases to
   illustrate how CrateDB can be applied in various industrial scenarios.
 
+:Industry: {tags-secondary}`Logistics` {tags-secondary}`Shipping`
+:Tags: {tags-primary}`Data Historian` {tags-primary}`Industrial IoT` {tags-primary}`Digital Twin`
+:::
+
+:::{grid-item} &nbsp;
+:columns: 4
+
+<iframe width="240" class="speakerdeck-iframe" style="border: 0px; background: rgba(0, 0, 0, 0.1) padding-box; margin: 0px; padding: 0px; border-radius: 6px; box-shadow: rgba(0, 0, 0, 0.2) 0px 5px 40px; width: 100%; height: auto; aspect-ratio: 560 / 315;" frameborder="0" src="https://speakerdeck.com/player/acb78531a07e4238ac662539b0c23609" title=" Not all time-series are equal ​  Challenges of storing and analyzing industrial data" allowfullscreen="true" data-ratio="1.7777777777777777"></iframe>
+
+**Date:** 23 Nov 2022 \
+**Speaker:** Marija Selakovic
+
+
+<iframe width="240" src="https://www.youtube-nocookie.com/embed/ugQvihToY0k?si=J0w5yG56Ld4fIXfm" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+**Date:** 5 Oct 2023 \
+**Speakers:** Alexander Mann, Georg Traar
+:::
+
+::::
+
+
+
 
-[Challenges of Storing and Analyzing Industrial Data]: https://youtu.be/ugQvihToY0k?feature=shared
-[Fixing data silos in a high-speed logistics environment]:  https://youtu.be/6dgjVQJtSKI?feature=shared
+[CrateDB: Challenges in industrial data]: https://speakerdeck.com/cratedb/not-all-time-series-are-equal-challenges-of-storing-and-analyzing-industrial-data
+[Rauch: High-Speed Production Lines]: https://youtu.be/gJPmJ0uXeVs?feature=shared
+[TGW: Fixing data silos in a high-speed logistics environment]:  https://youtu.be/6dgjVQJtSKI?feature=shared
+[TGW: Storing and analyzing real-world industrial data]: https://youtu.be/ugQvihToY0k?feature=shared