docs: remove references to Synapse and Redshift backends

Synapse and Redshift are no longer offered as transformation backends.
This removes their mentions from the overview, transformations, mappings,
tutorial, architecture guide, and project limits pages.

Writer/connector documentation for Synapse and Redshift is preserved
as those are external database targets, not Keboola backends.

Co-Authored-By: Jakub Kotek <jakub.kotek@keboola.com>

Author: 2 people

management/project/limits/index.md

@@ -120,11 +120,7 @@ The platform limits may be **soft** limits or **hard** limits. They are also lik
 development continues and often can be mitigated by a good project design. Contact us for advice if you are 
 concerned about any of them!
 
-For example, the [Redshift backend](/storage/#backend-properties) allows the maximum table cell size of 64kB. This
-is a hard limit and nothing can be done about it as long as Redshift is a hard requirement (the Snowflake backend 
-can take larger cells). 
-
-As another example, you should not have more than 200 tables in a single bucket. This is a soft limit related to
+For example, you should not have more than 200 tables in a single bucket. This is a soft limit related to
 how we believe the Storage component should be used. Nothing prevents you from exceeding that limit but the 
 component performance may degrade. 
 

overview/index.md

@@ -11,7 +11,7 @@ Designed for data engineers, analysts, and scientists, Keboola **simplifies data
 
 Key features of Keboola:
 - **Data Integration:** Effortlessly extract data from various sources like databases, cloud services, and APIs. Load it seamlessly into destinations of your choice for comprehensive analysis.
-- **Data Storage:** Use Keboola's robust data warehousing (Snowflake, BigQuery, Redshift, Synapse, etc.) for secure and accessible data storage. 
+- **Data Storage:** Use Keboola's robust data warehousing (Snowflake, BigQuery, etc.) for secure and accessible data storage. 
 - **Data Manipulation:** With our extensive toolset, clean, enrich, and transform your data using SQL, Python, R, and more directly within Keboola.
 - **Automation:**  Automate your data workflows end-to-end with Keboola's intuitive Flows, saving time and reducing manual errors.
 
@@ -23,7 +23,7 @@ Keboola supports various deployment models to suit your specific needs:
 
 - **Fully Managed:** Let us handle everything for you.
 - **Multi-Tenant:** Let us fully manage and maintain all resources.
-- **Multi-Tenant with BYO Database:** Use your data storage (Snowflake, BigQuery, Redshift, Synapse, etc.) while we manage the rest.
+- **Multi-Tenant with BYO Database:** Use your data storage (Snowflake, BigQuery, etc.) while we manage the rest.
 - **Single-Tenant:** Deploy Keboola in your cloud environment (AWS, Azure, GCP) for maximum control and security.
 
 ## Keboola Architecture
@@ -47,13 +47,13 @@ to gather data from various sources. They can connect to APIs of external servic
 - [Table Storage](https://help.keboola.com/storage/tables/), where all data tables are organized into buckets, further categorized into in and out stages.
 
 This component acts as a middle layer that works with various [backend](/transformations/#backends) database systems like 
-[Snowflake](https://www.snowflake.com/), [Redshift](https://aws.amazon.com/redshift/), [BigQuery](https://cloud.google.com/bigquery/),
-[Synapse](https://azure.microsoft.com/en-us/services/synapse-analytics/), [and others](https://help.keboola.com/transformations/#backends). It provides a key Storage API for working with data, 
+[Snowflake](https://www.snowflake.com/), [BigQuery](https://cloud.google.com/bigquery/),
+[and others](https://help.keboola.com/transformations/#backends). It provides a key Storage API for working with data, 
 making it easier to connect with other parts of the system and third-party applications.
 
 ### Transformations & Workspaces
 [Transformations](/transformations/) allow you to manipulate data in your project. They are the tasks you want to perform and enable you to write custom scripts
-in [SQL](https://en.wikipedia.org/wiki/SQL) (Snowflake, Redshift, BigQuery, etc.), dbt, [Python](https://www.python.org/about/), 
+in [SQL](https://en.wikipedia.org/wiki/SQL) (Snowflake, BigQuery, etc.), dbt, [Python](https://www.python.org/about/), 
 and [R](https://www.r-project.org/about.html). 
 
 All transformations operate on a copy of Storage data in an isolated environment — a [workspace](/workspace/), guaranteeing safety for your

transformations/index.md

@@ -54,20 +54,18 @@ in Storage. This means, for example, that you can rename tables in Storage witho
 There are a number of staging options that influence the transformation script code, too. 
 Typically, you will create an SQL transformation that works with data in a Snowflake database or a Python script 
 that works with CSV files on a "local" disk (local from the script's perspective). However, it is possible to have 
-a Python script that works with data in a Synapse database or with data on Azure Blob Storage (ABS).
+a Python script that works with CSV files on Azure Blob Storage (ABS).
 The transformations are very flexible, though all the combinations might not be available in all projects at all times.
 
 ## Backends
 The  **Transformation Script** is code that defines what happens with the data when the
 tables from the input mapping are taken, modified, and produced into the tables referenced in the output mapping.
 
 A backend is the engine running the transformation script. It is a database server
-([Amazon Redshift](https://aws.amazon.com/redshift/),
-[Snowflake](https://www.snowflake.com/),
+([Snowflake](https://www.snowflake.com/),
 [Exasol](https://www.exasol.com/),
 [Teradata](https://www.teradata.com/),
-[Microsoft Synapse](https://azure.microsoft.com/en-us/services/synapse-analytics/) on Azure Stack), 
-[BigQuery](https://cloud.google.com/bigquery), 
+[BigQuery](https://cloud.google.com/bigquery)), 
 or a language interpreter 
 ([Python](https://www.python.org/about/),
 [R](https://www.r-project.org/about.html)).
@@ -100,9 +98,8 @@ and play with your arbitrary transformation scripts on copies of your tables
 without affecting data in your Storage or your transformations. You can convert a workspace to a transformation 
 and vice versa.
 
-For Redshift and Synapse, you'll get a separate database
-to which the data from input mapping can be loaded. You'll obtain database credentials, which you can
-use with a database client of your choice. You can do the same for Snowflake. In addition, we provide access
+You'll obtain database credentials, which you can
+use with a database client of your choice for Snowflake. In addition, we provide access
 to the [Snowflake web interface](https://docs.snowflake.com/en/user-guide/ui-snowsight-gs). Therefore, you can
 develop transformations without downloading and installing a database client.
 
@@ -271,4 +268,4 @@ When triggered
 
 With the [read-only input mapping](/transformations/mappings/#read-only-input-mapping) feature, you can access all buckets (your own or linked) in transformations. Your transformation user
 has read-only access to buckets (and their tables), so you can access such data. So, there is no need to specify standard input mapping 
-for your transformations. The name of the backend object (database, schema, etc.) depends on the backend you use, and it contains the bucket ID (not the bucket name).    
+for your transformations. The name of the backend object (database, schema, etc.) depends on the backend you use, and it contains the bucket ID (not the bucket name).      

transformations/mappings/index.md

@@ -63,8 +63,7 @@ Depending on the transformation types, you can either build your transformations
 with database tables or with CSV files. Furthermore, the CSV files can be placed locally with the transformation
 script or they can be placed on a remote storage such as [Amazon S3](https://aws.amazon.com/s3/) or 
 [Azure Blob Storage](https://azure.microsoft.com/en-us/services/storage/blobs/).
-The supported database types are [Snowflake](https://www.snowflake.com/), 
-[Redshift](https://aws.amazon.com/redshift/), and [Synapse](https://azure.microsoft.com/en-us/services/synapse-analytics/).
+The supported database type is [Snowflake](https://www.snowflake.com/).
 
 {: .image-popup}
 ![Table Input mapping](/transformations/mappings/table-input-mapping.png)
@@ -246,8 +245,7 @@ Depending on the transformation backend, the table output mapping process can do
 the project [Storage tables](/storage/tables/).
 - In case of **File Staging** --- import the specified *CSV files* into project [Storage tables](/storage/tables/).
 
-The supported staging database types are as follows: [Snowflake](https://www.snowflake.com/), 
-[Redshift](https://aws.amazon.com/redshift/), and [Synapse](https://azure.microsoft.com/en-us/services/synapse-analytics/). 
+The supported staging database type is [Snowflake](https://www.snowflake.com/).
 The supported staging for CSV files is a storage local to the transformation.
 
 {: .image-popup}

tutorial/ad-hoc/index.md

@@ -283,7 +283,7 @@ For more information about workspaces (including disk and memory limits), see th
 
 ## Final Note
 This is the end of our stroll around Keboola. On our walk, we missed quite a few things:
-Applications, Python and R transformations, Redshift and Snowflake features, to name a few.
+Applications, Python and R transformations, Snowflake features, to name a few.
 However, teaching you everything was not really the point of this tutorial.
 We wanted to show you how Keboola can help in connecting different systems together.
 

tutorial/onboarding/architecture-guide/index.md

@@ -18,7 +18,7 @@ storage, and processes. It provides a structured framework for distributing task
 Each project includes the following key components:
 
 1. **Storage**
-   - Relational databases such as Snowflake, Redshift, Synapse, Exasol, and others
+   - Relational databases such as Snowflake, Exasol, and others
    - Object storage options like S3 or Azure Blob Storage
 2. **Components**
    - Data sources and destinations for loading data into Keboola Storage and exporting to databases, services, or applications