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/blog/2023-06-15-automating-data-engineers.md

@@ -122,5 +122,5 @@ Not only that, but doing things this way lets your team focus on what they do be
 2. Notify stakeholders and producers of data changes, so they can curate it.
 3. Don’t explore json with data engineers - let analyst explore structured data.
 
-Ready to stop the pain? Read [this explainer on how to do schema evolution with dlt](/docs/reference/explainers/schema-evolution).
+Ready to stop the pain? Read [this explainer on how to do schema evolution with dlt](https://dlthub.com/docs/blog/schema-evolution).
 Want to discuss? Join our [slack](https://join.slack.com/t/dlthub-community/shared_invite/zt-1n5193dbq-rCBmJ6p~ckpSFK4hCF2dYA).

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 +1,18 @@
+---
+title: Deployments
+description: dlt can run on almost any python environment and hardware
+keywords: [dlt, running, environment]
+---
+import DocCardList from '@theme/DocCardList';
+
+# Deployments
+Where Python runs, `dlt` will run too. Besides deployments you see below, our users report that they ran us successfully on:
+* notebook environments including Colab
+* orchestrators including Kestra, Prefect or Dagster
+* AWS Lambda and other serverless
+* local laptops with `duckdb` or `weaviate` as destinations
+* Github codespaces and other devcontainers
+* regular VMs from all major providers: AWS, GCP or Azure
+* containers via Docker, docker-compose and Kubernetes
+
+<DocCardList />