crate · amotl · Nov 27, 2024 · Nov 23, 2024 · Nov 26, 2024 · Nov 26, 2024
diff --git a/docs/integrate/etl.md b/docs/integrate/etl.md
@@ -76,32 +76,6 @@ journey. Spend time where it counts.
 :::
 
 
-## Apache Flink
-
-```{div}
-:style: "float: right"
-[![](https://flink.apache.org/flink-header-logo.svg){w=180px}](https://flink.apache.org/)
-```
-
-[Apache Flink] is a framework and distributed processing engine for stateful
-computations over unbounded and bounded data streams, written in Java.
-
-Flink has been designed to run in all common cluster environments, perform
-computations at in-memory speed and at any scale. It received the [2023 SIGMOD
-Systems Award].
-
-> Apache Flink greatly expanded the use of stream data-processing.
-
-![](https://flink.apache.org/img/flink-home-graphic.png){h=200px}
-
-:::{dropdown} **Managed Flink**
-A few companies are specializing in offering managed Flink services.
-
-- [Aiven] offers their managed [Aiven for Apache Flink] solution.
-- [Immerok Cloud]'s offering is being converged into [Flink managed by Confluent].
-:::
-
-
 ## Apache Hop
 
 ```{div}
@@ -184,62 +158,6 @@ worldwide across every industry.
 ![](https://github.com/crate/crate-clients-tools/assets/453543/ccfa4ac7-0d60-432f-b952-2b50789cd325){h=120px}
 
 
-## dbt
-
-```{div}
-:style: "float: right"
-[![](https://www.getdbt.com/ui/img/logos/dbt-logo.svg){w=180px}](https://www.getdbt.com/)
-```
-
-[dbt] is an open source tool for transforming data in data warehouses using Python and
-SQL. It is an SQL-first transformation workflow platform that lets teams quickly and
-collaboratively deploy analytics code following software engineering best practices
-like modularity, portability, CI/CD, and documentation.
-
-> dbt enables data analysts and engineers to transform their data using the same
-> practices that software engineers use to build applications.
-
-With dbt, anyone on your data team can safely contribute to production-grade data
-pipelines.
-
-The idea is that data engineers make source data available to an environment where
-dbt projects run, for example with [Debezium](#debezium) or with [Airflow](#apache-airflow).
-Afterwards, data analysts can run their dbt projects against this data to produce models
-(tables and views) that can be used with a number of [BI tools](#bi-tools).
-
-![](https://www.getdbt.com/ui/img/products/what-is-dbt-main-image.png){h=120px}
-![](https://www.getdbt.com/ui/img/products/what-is-dbt-deploy.svg){h=120px}
-![](https://www.getdbt.com/ui/img/products/what-is-dbt-eliminate-silos.svg){h=120px}
-
-:::{dropdown} **Managed dbt**
-```{div}
-:style: "float: right"
-[![](https://www.getdbt.com/ui/img/hero-dbt-cloud-features-2x5.png){w=180px}](https://www.getdbt.com/product/dbt-cloud/)
-```
-
-With [dbt Cloud], you can ditch time-consuming setup, and the struggles
-of scaling your data production. dbt Cloud is a full-suite service that is built for
-scale.
-
-- Start building data products quickly using the dbt Cloud IDE with integrated security
-  and governance controls.
-- Schedule, deploy, and monitor your data products using the scalable and reliable dbt
-  Cloud Scheduler.
-- Help your data teams discover and reuse data products using hosted docs or integrations
-  with the powerful Discovery API.
-- Extend your workflow beyond dbt Cloud with 30+ seamless integrations covering a range
-  of use cases across the Modern Data Stack, from observability and data quality to
-  visualization, reverse ETL, and much more.
-- Ship more high-quality data and scale your development like the 1000s of companies that
-  use dbt Cloud. They’ve used its convenient and collaboration-friendly interface to
-  eliminate the bottlenecks that keep growth limited.
-
-```{div}
-:style: "clear: both"
-```
-:::
-
-
 ## Debezium
 
 ```{div}
@@ -386,13 +304,9 @@ an SSIS Catalog database to store, run, and manage packages.
 ```
 
 
-[2023 SIGMOD Systems Award]: https://sigmod.org/2023-sigmod-systems-award/
-[Aiven]: https://aiven.io/
-[Aiven for Apache Flink]: https://aiven.io/flink
 [Aiven for Apache Kafka]: https://aiven.io/kafka
 [Amazon Managed Streaming for Apache Kafka (MSK)]: https://aws.amazon.com/msk/
 [Apache Airflow]: https://airflow.apache.org/
-[Apache Flink]: https://flink.apache.org/
 [Apache Hop]: https://hop.apache.org/
 [Apache Kafka]: https://kafka.apache.org/
 [Apache Kafka on Azure]: https://azuremarketplace.microsoft.com/marketplace/consulting-services/canonical.0001-com-ubuntu-managed-kafka
@@ -404,14 +318,10 @@ an SSIS Catalog database to store, run, and manage packages.
 [CrateDB and Apache Kafka]: https://cratedb.com/integrations/cratedb-and-kafka
 [CrateDB and Kestra]: https://cratedb.com/integrations/cratedb-and-kestra
 [CrateDB and Node-RED]: https://cratedb.com/integrations/cratedb-and-node-red
-[dbt]: https://www.getdbt.com/
-[dbt Cloud]: https://www.getdbt.com/product/dbt-cloud/
 [Debezium]: https://debezium.io/
 [DoubleCloud Managed Service for Apache Kafka]: https://double.cloud/services/managed-kafka/
-[Flink managed by Confluent]: https://www.datanami.com/2023/05/17/confluents-new-cloud-capabilities-address-data-streaming-hurdles/
 [FlowFuse]: https://flowfuse.com/
 [FlowFuse Cloud]: https://app.flowforge.com/
-[Immerok Cloud]: https://web.archive.org/web/20230602085618/https://www.immerok.io/product
 [Introduction to FlowFuse]: https://flowfuse.com/webinars/2023/introduction-to-flowforge/
 [Kestra]: https://kestra.io/
 [Meltano]: https://meltano.com/

diff --git a/docs/integrate/ml.md b/docs/integrate/ml.md
@@ -19,34 +19,6 @@ for MLOps and Vector database operations.
 ::::
 
 
-## LangChain
-
-```{div}
-:style: "float: right; font-size: 4em; margin-left: 0.3em"
-🦜️🔗
-```
-
-[LangChain] is a framework for developing applications powered by language models,
-written in Python, and with a strong focus on composability. As a language model
-integration framework, LangChain's use-cases largely overlap with those of language
-models in general, including document analysis and summarization, chatbots, and
-code analysis.
-
-LangChain supports retrieval-augmented generation (RAG), which is a technique for
-augmenting LLM knowledge with additional, often private or real-time, data, and mixing
-in "prompt engineering" as the process of structuring text that can be interpreted and
-understood by a generative AI model. A prompt is natural language text describing the
-task that an AI should perform.
-
-The [LangChain adapter for CrateDB] provides support to use CrateDB as a vector store
-database, to load documents using LangChain's DocumentLoader, and also supports
-LangChain's conversational memory subsystem.
-
-```{div}
-:style: "clear: both"
-```
-
-
 ## MLflow
 
 ```{div}
@@ -129,8 +101,6 @@ A modular design invites extensions to expand and enrich functionality.
 ```
 
 
-[LangChain]: https://python.langchain.com/
-[LangChain adapter for CrateDB]: https://github.com/crate-workbench/langchain
 [MLflow]: https://mlflow.org/
 [mlflow-cratedb]: https://pypi.org/project/mlflow-cratedb/
 [MLflow adapter for CrateDB]: https://github.com/crate/mlflow-cratedb

diff --git a/docs/integrate/visualize.md b/docs/integrate/visualize.md
@@ -17,55 +17,6 @@ Guidelines about data analysis and visualization with CrateDB.
 ::::
 
 
-(apache-superset)=
-(preset)=
-(superset)=
-## Apache Superset / Preset
-
-```{div}
-:style: "float: right"
-[![](https://cratedb.com/hs-fs/hubfs/[email protected]?width=604&height=216&[email protected]){w=180px}](https://superset.apache.org/)
-
-[![](https://github.com/crate/crate-clients-tools/assets/453543/9d07da87-8aff-4569-bf2a-0a16bf89f4bc){w=180px}](https://preset.io/)
-```
-
-[Apache Superset] is an open-source modern data exploration and visualization
-platform, written in Python.
-
-[Preset] offers a managed, elevated, and enterprise-grade SaaS for open-source
-Apache Superset.
-
-![](https://superset.apache.org/img/hero-screenshot.jpg){h=200px}
-![](https://github.com/crate/crate-clients-tools/assets/453543/0f8f7bd8-2e30-4aca-bcf3-61fbc81da855){h=200px}
-
-```{seealso}
-[CrateDB and Superset]
-```
-
-:::{dropdown} **Managed Superset**
-```{div}
-:style: "float: right"
-[![](https://github.com/crate/crate-clients-tools/assets/453543/9d07da87-8aff-4569-bf2a-0a16bf89f4bc){w=180px}](https://preset.io/)
-```
-
-[Preset Cloud] is a fully-managed, open-source BI for the modern data stack,
-based on Apache Superset.
-
-- **Hassle-free setup:** There is no need to install or maintain software with Preset.
-  Get the latest version of Superset in a secure, reliable, and scalable SaaS experience.
-- **Up-to-date Superset, always:** Access all the latest features of Superset
-  released and thoroughly tested every two weeks.
-- **One-click to deploy multiple workspaces:** Give each team in your organization
-  a separate Superset workspace to protect sensitive data.
-- **Control user roles and access:** Easily assign roles and fine-tune data access
-  using RBAC and row-level security (RLS).
-
-```{div}
-:style: "clear: both"
-```
-:::
-
-
 ## Cluvio
 
 ```{div}
@@ -200,10 +151,8 @@ with none of the work or hidden costs that come with self-hosting.
 :::
 
 
-[Apache Superset]: https://superset.apache.org/
 [Cluvio]: https://www.cluvio.com/
 [CrateDB and Grafana]: https://cratedb.com/integrations/cratedb-and-grafana
-[CrateDB and Superset]: https://cratedb.com/integrations/cratedb-and-apache-superset
 [CrateDB and Metabase]: https://cratedb.com/integrations/cratedb-and-metabase
 [Explo]: https://www.explo.co/
 [Explo Explore]: https://www.explo.co/products/explore