[Launch] Cloud-centric docs, phase 1 (#2085)

Signed-off-by: Charis <[email protected]>
Signed-off-by: Rajakavitha Kodhandapani <[email protected]>
Signed-off-by: Lana Brindley <[email protected]>
Co-authored-by: loquacity <[email protected]>
Co-authored-by: Rajakavitha Kodhandapani <[email protected]>
Co-authored-by: Yannis Roussos <[email protected]>

.github/ISSUE_TEMPLATE/bug_report.md

@@ -38,4 +38,4 @@ _Use this template for reporting bugs in the docs._
 We welcome documentation contributions!
 
 *   For information about how to propose a change, see the [contributing guide](https://github.com/timescale/docs/blob/latest/CONTRIBUTING.md) in our GitHub repository.
-*   For information on style and word usage, see the [style guide](https://docs.timescale.com/timescaledb/latest/contribute-to-docs)
+*   For information on style and word usage, see the [style guide](https://docs.timescale.com/about/latest/contribute-to-docs)

.github/ISSUE_TEMPLATE/feedback.yml

@@ -32,4 +32,4 @@ body:
         We welcome documentation contributions!
 
         * For information about how to suggest a change, see the [contributing guide](https://github.com/timescale/docs/blob/latest/CONTRIBUTING.md) in our GitHub repository.
-        * For information on style and word usage, see the [style guide](https://docs.timescale.com/timescaledb/latest/contribute-to-docs).
+        * For information on style and word usage, see the [style guide](https://docs.timescale.com/about/latest/contribute-to-docs).

.github/ISSUE_TEMPLATE/rfc.md

@@ -27,5 +27,5 @@ assignees: ''
 
 We welcome documentation contributions!
 
-* For information about how to suggest a change, see the [contributing guide](https://github.com/timescale/docs/blob/latest/CONTRIBUTING.md) in our GitHub repository.
-* For information on style and word usage, see the [style guide](https://docs.timescale.com/timescaledb/latest/contribute-to-docs)
+*   For information about how to suggest a change, see the [contributing guide](https://github.com/timescale/docs/blob/latest/CONTRIBUTING.md) in our GitHub repository.
+*   For information on style and word usage, see the [style guide](https://docs.timescale.com/about/latest/contribute-to-docs)

.github/pull_request_template.md

@@ -8,25 +8,26 @@ Fixes #[insert issue link, if any]
 
 # Writing help
 
-For information about style and word usage, see the [style guide](https://docs.timescale.com/timescaledb/latest/contribute-to-docs/)
+For information about style and word usage, see the [style guide](https://docs.timescale.com/about/latest/contribute-to-docs/)
 
 # Review checklists
+
 Reviewers: use this section to ensure you have checked everything before approving this PR:
 
 ## Subject matter expert (SME) review checklist
 
-- [ ] Is the content technically accurate?
-- [ ] Is the content complete?
-- [ ] Is the content presented in a logical order?
-- [ ] Does the content use appropriate names for features and products?
-- [ ] Does the content provide relevant links to further information?
+*   [ ] Is the content technically accurate?
+*   [ ] Is the content complete?
+*   [ ] Is the content presented in a logical order?
+*   [ ] Does the content use appropriate names for features and products?
+*   [ ] Does the content provide relevant links to further information?
 
 ## Documentation team review checklist
 
-- [ ] Is the content free from typos?
-- [ ] Does the content use plain English?
-- [ ] Does the content contain clear sections for concepts, tasks, and references?
-- [ ] Have any images been uploaded to the correct location, and are resolvable?
-- [ ] If the page index was updated, are redirects required
+*   [ ] Is the content free from typos?
+*   [ ] Does the content use plain English?
+*   [ ] Does the content contain clear sections for concepts, tasks, and references?
+*   [ ] Have any images been uploaded to the correct location, and are resolvable?
+*   [ ] If the page index was updated, are redirects required
       and have they been implemented?
-- [ ] Have you checked the built version of this content?
+*   [ ] Have you checked the built version of this content?

.github/workflows/community-comment.yml

@@ -18,4 +18,4 @@ jobs:
             Thank you for the report. We welcome documentation contributions!
 
             * For information about how to propose a change, see the [contributing guide](https://github.com/timescale/docs/blob/latest/CONTRIBUTING.md) in our GitHub repository.
-            * For information on style and word usage, see the [style guide](https://docs.timescale.com/timescaledb/latest/contribute-to-docs)
+            * For information on style and word usage, see the [style guide](https://docs.timescale.com/about/latest/contribute-to-docs)

CONTRIBUTING.md

@@ -198,7 +198,7 @@ that reviewers check for in the pull request template.
 
 Timescale has comprehensive writing and style standards, that are constantly
 being updated and improved. For the current guidelines, see
-[contributing to documentation](https://docs.timescale.com/timescaledb/latest/contribute-to-docs/).
+[contributing to documentation](https://docs.timescale.com/about/latest/contribute-to-docs/).
 
 ## The Timescale documentation team
 

_code-samples/toolkit/two-step_aggregation.md

@@ -22,10 +22,10 @@ Interestingly, almost all Postgres aggregates do a version of this [under the ho
 
 So why do we make this calling convention explicit?
 
-1) It allows different accessor function calls to use the same internal state and not redo work.
-2) It cleanly distinguishes the parameters that affect the aggregate and those that only affect the accessor.
-3) It makes it explicit how and when aggregates can be re-aggregated or "stacked" on themselves with logically consistent results. This also helps them better integrate with [continuous aggregates](https://docs.timescale.com/latest/using-timescaledb/continuous-aggregates).
-4) It allows for better retrospective analysis of downsampled data in [continuous aggregates](https://docs.timescale.com/latest/using-timescaledb/continuous-aggregates).
+1. It allows different accessor function calls to use the same internal state and not redo work.
+2. It cleanly distinguishes the parameters that affect the aggregate and those that only affect the accessor.
+3. It makes it explicit how and when aggregates can be re-aggregated or "stacked" on themselves with logically consistent results. This also helps them better integrate with [continuous aggregates](/use-timescale/latest/continuous-aggregates).
+4. It allows for better retrospective analysis of downsampled data in continuous aggregates.
 
 That might have been gibberish to some, so let's unpack it a bit.
 

_partials/_2-step-aggregation.md

@@ -22,4 +22,4 @@ To learn more, see the [blog post on two-step
 aggregates][blog-two-step-aggregates].
 
 [blog-two-step-aggregates]: https://www.timescale.com/blog/how-postgresql-aggregation-works-and-how-it-inspired-our-hyperfunctions-design-2/
-[caggs]: /timescaledb/:currentVersion:/how-to-guides/continuous-aggregates/about-continuous-aggregates/
+[caggs]: /use-timescale/:currentVersion:/continuous-aggregates/about-continuous-aggregates/

_partials/_add-data-twelvedata-crypto.md

@@ -26,7 +26,7 @@ dataset and copy the data to your database.
     ```
 
 1.  At the `psql` prompt, use the `COPY` command to transfer data into your
-    TimescaleDB instance. If the `.csv` files aren't in your current directory,
+    Timescale instance. If the `.csv` files aren't in your current directory,
     specify the file paths in these commands:
 
     ```sql

_partials/_add-data-twelvedata-stocks.mdx → _partials/_add-data-twelvedata-stocks.md

@@ -27,7 +27,7 @@ dataset and copy the data to your database.
     ```
 
 1.  At the `psql` prompt, use the `COPY` command to transfer data into your
-    TimescaleDB instance. If the `.csv` files aren't in your current directory,
+    Timescale instance. If the `.csv` files aren't in your current directory,
     specify the file paths in these commands:
 
     ```sql

_partials/_caggs-intro.md

@@ -0,0 +1,19 @@
+Time-series data usually grows very quickly. And that means that aggregating the
+data into useful summaries can become very slow. Continuous aggregates makes
+aggregating data lightning fast.
+
+If you are collecting data very frequently, you might want to aggregate your
+data into minutes or hours instead. For example, if you have a table of
+temperature readings taken every second, you can find the average temperature
+for each hour. Every time you run this query, the database needs to scan the
+entire table and recalculate the average every time.
+
+Continuous aggregate views are refreshed automatically in the background as new
+data is added, or old data is modified. Timescale tracks these changes to the
+dataset, and automatically updates the view in the background. This does not add
+any maintenance burden to your database, and does not slow down `INSERT`
+operations.
+
+By default, querying continuous aggregates provides you with real-time data.
+Pre-aggregated data from the materialized view is combined with recent data that
+hasn't been aggregated yet. This gives you up-to-date results on every query.

_partials/_cloud-connect.md

@@ -36,5 +36,5 @@ psql][install-psql] section.
 
 </Procedure>
 
-[install-psql]: /timescaledb/:currentVersion:/how-to-guides/connecting/psql/
+[install-psql]: /use-timescale/:currentVersion:/connecting/psql/
 [tsc-portal]: https://console.cloud.timescale.com/

_partials/_cloud-installation.md

@@ -1,6 +1,6 @@
 <Procedure>
 
-### Installing Timescale Cloud
+### Creating your Timescale Cloud account
 
 1.  Sign up for a [Timescale Cloud account][sign-up] with your
     name and email address. You do not need to provide payment details to
@@ -16,9 +16,9 @@
     />
 
 <Highlight type="important">
-  Your Timescale Cloud trial is completely free for you to use for the first
-  thirty days. This gives you enough time to complete all our tutorials and run
-  a few test projects of your own.
+Your Timescale Cloud trial is completely free for you to use for the first
+thirty days. This gives you enough time to complete all the tutorials and run a
+few test projects of your own.
 </Highlight>
 
 </Procedure>

_partials/_cloud-intro.md

@@ -0,0 +1,5 @@
+Timescale Cloud is hosted high-performance PostgreSQL for time-series and
+analytics. It gives you the reliability of PostgreSQL, the time-series
+superpowers of Timescale, and the peace of mind of a fully managed service
+with automatic backup and restore, high availability via replication, seamless
+scaling and resizing, and much more.

_partials/_cloud-mst-comparison.md

@@ -0,0 +1,11 @@
+<Highlight
+  type="cloud"
+  header="Timescale Cloud or Managed Service for TimescaleDB?"
+>
+If your workload lives in AWS, use
+[Timescale Cloud](/getting-started/latest/),
+built and exclusively operated by Timescale, designed to offer maximum
+cost-effectiveness and performance for time-series data. If you need Azure or
+GCP, try
+[Managed Service for TimescaleDB](/mst/latest/) instead.
+</Highlight>

_partials/_cloudtrial.md

@@ -1,5 +1,5 @@
 <Highlight type="cloud" header="Try for free on Timescale Cloud" button="Try for free">
 Your Timescale Cloud trial is completely free for you to use for the first
-thirty days. This gives you enough time to complete all our tutorials and run a
+thirty days. This gives you enough time to complete all the tutorials and run a
 few test projects of your own.
 </Highlight>

_partials/_compression-intro.md

@@ -0,0 +1,11 @@
+Compressing your time-series data allows you to reduce your chunk size by more
+than 90%. This saves on storage costs, and keeps your queries operating at
+lightning speed.
+
+When you enable compression, the data in your hypertable is compressed chunk by
+chunk. When the chunk is compressed, multiple records are grouped into a single
+row. The columns of this row hold an array-like structure that stores all the
+data. This means that instead of using lots of rows to store the data, it stores
+the same data in a single row. Because a single row takes up less disk space
+than many rows, it decreases the amount of disk space required, and can also
+speed up your queries.

_partials/_create-hypertable-nyctaxis.md

@@ -1,10 +1,10 @@
 ## Create a hypertable
 
-Hypertables are the core of TimescaleDB. Hypertables enable TimescaleDB to work
-efficiently with time-series data. Because TimescaleDB is PostgreSQL, all the
+Hypertables are the core of Timescale. Hypertables enable Timescale to work
+efficiently with time-series data. Because Timescale is PostgreSQL, all the
 standard PostgreSQL tables, indexes, stored procedures and other objects can be
-created alongside your TimescaleDB hypertables. This makes creating and working
-with TimescaleDB tables similar to standard PostgreSQL.
+created alongside your Timescale hypertables. This makes creating and working
+with Timescale tables similar to standard PostgreSQL.
 
 <Procedure>
 
@@ -37,7 +37,7 @@ with TimescaleDB tables similar to standard PostgreSQL.
     ```
 
 1.  Convert the standard table into a hypertable partitioned on the `time`
-    column using the `create_hypertable()` function provided by TimescaleDB. You
+    column using the `create_hypertable()` function provided by Timescale. You
     must provide the name of the table and the column in that table that holds
     the timestamp data to use for partitioning:
 

_partials/_create-hypertable-twelvedata-crypto.md

@@ -1,11 +1,11 @@
 
 ## Create a hypertable
 
-Hypertables are the core of TimescaleDB. Hypertables enable TimescaleDB to work
-efficiently with time-series data. Because TimescaleDB is PostgreSQL, all the
+Hypertables are the core of Timescale. Hypertables enable Timescale to work
+efficiently with time-series data. Because Timescale is PostgreSQL, all the
 standard PostgreSQL tables, indexes, stored procedures, and other objects can be
-created alongside your TimescaleDB hypertables. This makes creating and working
-with TimescaleDB tables similar to standard PostgreSQL.
+created alongside your Timescale hypertables. This makes creating and working
+with Timescale tables similar to standard PostgreSQL.
 
 <Procedure>
 
@@ -24,7 +24,7 @@ with TimescaleDB tables similar to standard PostgreSQL.
     ```
 
 1.  Convert the standard table into a hypertable partitioned on the `time`
-    column using the `create_hypertable()` function provided by TimescaleDB. You
+    column using the `create_hypertable()` function provided by Timescale. You
     must provide the name of the table and the column in that table that holds
     the timestamp data to use for partitioning:
 

_partials/_create-hypertable-twelvedata-stocks.md

@@ -1,11 +1,11 @@
 
 # Create a hypertable
 
-Hypertables are the core of TimescaleDB. Hypertables enable TimescaleDB to work
-efficiently with time-series data. Because TimescaleDB is PostgreSQL, all the
+Hypertables are the core of Timescale. Hypertables enable Timescale to work
+efficiently with time-series data. Because Timescale is PostgreSQL, all the
 standard PostgreSQL tables, indexes, stored procedures and other objects can be
-created alongside your TimescaleDB hypertables. This makes creating and working
-with TimescaleDB tables similar to standard PostgreSQL.
+created alongside your Timescale hypertables. This makes creating and working
+with Timescale tables similar to standard PostgreSQL.
 
 <Procedure>
 
@@ -24,7 +24,7 @@ with TimescaleDB tables similar to standard PostgreSQL.
     ```
 
 1.  Convert the standard table into a hypertable partitioned on the `time`
-    column using the `create_hypertable()` function provided by TimescaleDB. You
+    column using the `create_hypertable()` function provided by Timescale. You
     must provide the name of the table and the column in that table that holds
     the timestamp data to use for partitioning:
 
@@ -41,7 +41,7 @@ with TimescaleDB tables similar to standard PostgreSQL.
 
 <Highlight type="note">
 When you create a hypertable, it is automatically partitioned on the time column
-you provide as the second parameter to `create_hypertable()`. Also, TimescaleDB
+you provide as the second parameter to `create_hypertable()`. Also, Timescale
 automatically creates an index on the time column. However, you'll often filter
 your time-series data on other columns as well. Using indexes appropriately helps
 your queries perform better.
@@ -72,7 +72,7 @@ there is one other table of data called `company`.
     );
     ```
 
-1.  You now have two tables within your TimescaleDB database. One hypertable
+1.  You now have two tables within your Timescale database. One hypertable
     named `stocks_real_time`, and one normal PostgreSQL table named `company`.
 
 </Procedure>

_partials/_data_model_metadata.md

@@ -1,5 +1,5 @@
 <Highlight type="note">
 You might also notice that the metadata fields are missing. Because this is a
 relational database, metadata can be stored in a secondary table and `JOIN`ed at
-query time. Learn more about [TimescaleDB's support for `JOIN`s](#joins-with-relational-data).
+query time. Learn more about [Timescale's support for `JOIN`s](#joins-with-relational-data).
 </Highlight>

_partials/_deprecated.md

@@ -1,5 +1,5 @@
 <Highlight type="deprecation">
-This section describes a feature that is deprecated on TimescaleDB. We strongly
+This section describes a feature that is deprecated on Timescale. We strongly
 recommend that you do not use this feature in a production environment. If you
 need more information, [contact the support
 team](https://www.timescale.com/support).

_partials/_grafana-connect.md

@@ -5,7 +5,7 @@ You can use Grafana to visualize queries directly from your Timescale database.
 
 Before you begin, make sure you have:
 
-*   Installed TimescaleDB. For more information, see the
+*   Installed Timescale. For more information, see the
     [installation documentation][install].
 *   Installed a self-managed Grafana account, or signed up for
     [Grafana Cloud][install-grafana].
@@ -81,4 +81,4 @@ a few test projects of your own.
 </Tabs>
 
 [install-grafana]: https://grafana.com/get/
-[install]: /install/:currentVersion:/installation-cloud/
+[install]: /getting-started/latest/

_partials/_grafana-viz-prereqs.md

@@ -1,7 +1,7 @@
 Before you begin, make sure you have:
 
 *   Installed [Grafana][install-grafana] version 8.5 or higher
-*   Installed [TimescaleDB][install-timescale]
+*   Installed [Timescale][install-timescale]
 *   Imported the stock trade data from the [Getting Started Tutorial][gsg-data]
 
 If you are new to Grafana, see the [Grafana tutorials][grafana-tutorials]
@@ -18,7 +18,7 @@ The examples in this section use these variables and Grafana functions:
     function when aggregating data.
 
 [install-grafana]: https://grafana.com/get/
-[install-timescale]: /install/:currentVersion:/
+[install-timescale]: /getting-started/latest/
 [gsg-data]: /getting-started/:currentVersion:/add-data/
-[grafana-tutorials]: /timescaledb/:currentVersion:/tutorials/grafana/
+[grafana-tutorials]: /tutorials/:currentVersion:/grafana/
 [variables-tutorial]: https://youtu.be/Fq9xsvHPsSQ

_partials/_hypertables-intro.md

@@ -1,9 +1,15 @@
-Hypertables are PostgreSQL tables with special features that make it easy to
-work with time-series data. You interact with them just as you would with
-regular PostgreSQL tables. But behind the scenes, hypertables automatically
-partition your data into chunks by time.
+Hypertables are PostgreSQL tables that automatically partition your data by
+time. You interact with hypertables in the same way as regular PostgreSQL
+tables, but with extra features that makes managing your time-series data much
+easier.
 
-In TimescaleDB, hypertables exist alongside regular PostgreSQL tables. Use
+In Timescale, hypertables exist alongside regular PostgreSQL tables. Use
 hypertables to store time-series data. This gives you improved insert and query
 performance, and access to useful time-series features. Use regular PostgreSQL
 tables for other relational data.
+
+With hypertables, Timescale makes it easy to improve insert and query
+performance by partitioning time-series data on its time parameter. Behind the
+scenes, the database performs the work of setting up and maintaining the
+hypertable's partitions. Meanwhile, you insert and query your data as if it all
+lives in a single, regular PostgreSQL table.