removes content that is not a tutorial, how to, reference or dlt rela…

…te knowledge from docs

docs/website/blog/2023-05-26-structured-data-lakes-through-schema-evolution-next-generation-data-platform.md

@@ -108,5 +108,5 @@ To try out schema evolution with `dlt`, check out our [colab demo.](https://cola
 ### Want more?
 
 - Join our [Slack](https://join.slack.com/t/dlthub-community/shared_invite/zt-1slox199h-HAE7EQoXmstkP_bTqal65g)
-- Read our [docs on implementing schema evolution](https://dlthub.com/docs/reference/explainers/schema-evolution)
+- Read our [schema evolution blog post](https://dlthub.com/docs/blog/schema-evolution)
 - Stay tuned for the next article in the series: *How to do schema evolution with* `dlt` *in the most effective way*

docs/website/docs/reference/explainers/schema-evolution.md → docs/website/blog/2023-06-10-schema-evolution.md

@@ -1,7 +1,12 @@
 ---
-title: Schema evolution
-description: Schema evolution with dlt
-keywords: [schema evolution, schema versioning, data contracts]
+slug: schema-evolution
+title: "Schema Evolution"
+authors:
+  name: Adrian Brudaru
+  title: Schema Evolution
+  url: https://github.com/adrianbr
+  image_url: https://avatars.githubusercontent.com/u/5762770?v=4
+tags: [data engineer shortage, structured data, schema evolution]
 ---
 
 # Schema evolution
@@ -131,10 +136,10 @@ business-logic tests, you would still need to implement them in a custom way.
 ## The implementation recipe
 
 1. Use `dlt`. It will automatically infer and version schemas, so you can simply check if there are
-   changes. You can just use the [normaliser + loader](../../general-usage/pipeline.md) or
-   [build extraction with dlt](../../general-usage/resource.md). If you want to define additional
-   constraints, you can do so in the [schema](../../general-usage/schema.md).
-1. [Define your slack hook](../../running-in-production/running.md#using-slack-to-send-messages) or
+   changes. You can just use the [normaliser + loader](https://dlthub.com/docs/general-usage/pipeline.md) or
+   [build extraction with dlt](https://dlthub.com/docs/general-usage/resource.md). If you want to define additional
+   constraints, you can do so in the [schema](https://dlthub.com/docs/general-usage/schema.md).
+1. [Define your slack hook](https://dlthub.com/docs/running-in-production/running.md#using-slack-to-send-messages) or
    create your own notification function. Make sure the slack channel contains the data producer and
    any stakeholders.
-1. [Capture the load job info and send it to the hook](../../running-in-production/running#inspect-save-and-alert-on-schema-changes).
+1. [Capture the load job info and send it to the hook](https://dlthub.com/docs/running-in-production/running#inspect-save-and-alert-on-schema-changes).

docs/website/docs/build-a-pipeline-tutorial.md

@@ -413,8 +413,6 @@ These governance features in `dlt` pipelines contribute to better data managemen
 compliance adherence, and overall data governance, promoting data consistency, traceability, and
 control throughout the data processing lifecycle.
 
-Read more about [schema evolution.](reference/explainers/schema-evolution.md)
-
 ### Scaling and finetuning
 
 `dlt` offers several mechanism and configuration options to scale up and finetune pipelines:

docs/website/docs/dlt-ecosystem/deployments/index.md

@@ -0,0 +1,17 @@
+---
+title: Deployments
+description: dlt can run on almost any python environment and hardware
+keywords: [dlt, running, environment]
+---
+import DocCardList from '@theme/DocCardList';
+
+# Deployments
+`dlt` runs wherever Python runs. Below you see walkthroughs for Airflow, GCP Cloud Functions, AWS Lambda and GitHub Actions but our users run us on
+* in any notebook environment including Colab
+* any orchestrator including Kestra, Prefect or Dagster
+* local laptops with `duckdb` or `weaviate` as destinations
+* Github codespaces and other devcontainers
+* regular VMs from all major providers: AWS, GCP or Azure
+* in containers via Docker, docker-compose and Kubernetes
+
+<DocCardList />