Docs: Deploy dlt on dagster

docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster.md

@@ -0,0 +1,158 @@
+---
+title: Deploy with Dagster
+description: How to deploy a pipeline with Dagster
+keywords: [how to, deploy a pipeline, Dagster]
+---
+
+# Deploy with Dagster
+
+## Introduction to Dagster
+
+Dagster is an orchestrator designed for developing and maintaining data assets, such as
+tables, data sets, machine learning models, and reports. Dagster ensures these processes are
+reliable and focuses on using software-defined assets (SDAs) to simplify complex data management,
+enhance the ability to reuse code, and provide a better understanding of data.
+
+To read more, please refer to Dagster’s
+[documentation.](https://docs.dagster.io/getting-started?_gl=1*19ikq9*_ga*NTMwNTUxNDAzLjE3MDg5Mjc4OTk.*_ga_84VRQZG7TV*MTcwOTkwNDY3MS4zLjEuMTcwOTkwNTYzNi41Ny4wLjA.*_gcl_au*OTM3OTU1ODMwLjE3MDg5Mjc5MDA.)
+
+### Dagster Cloud Features
+
+Dagster Cloud offers enterprise-level orchestration service with serverless or hybrid deployment
+options. It incorporates native branching and built-in CI/CD to prioritize the developer experience.
+It enables scalable, cost-effective operations without the hassle of infrastructure management.
+
+### Dagster deployment options: **Serverless** versus **Hybrid**:
+
+The *serverless* option fully hosts the orchestration engine, while the *hybrid* model offers
+flexibility to use your computing resources, with Dagster managing the control plane. Reducing
+operational overhead and ensuring security.
+
+For more info, please refer to the Dagster Cloud [docs.](https://dagster.io/cloud)
+
+### Using Dagster for Free
+
+Dagster offers a 30-day free trial during which you can explore its features, such as pipeline
+orchestration, data quality checks, and embedded ELTs. You can try Dagster using its open source or
+by signing up for the trial.
+
+## Building Data Pipelines with `dlt`
+
+`dlt` is an open-source Python library that allows you to declaratively load data sources into
+well-structured tables or datasets through automatic schema inference and evolution. It simplifies 
+building data pipelines with support for extract and load processes.
+
+**How does `dlt` integrate with Dagster for pipeline orchestration?**
+
+`dlt` integrates with Dagster for pipeline orchestration, providing a streamlined process for
+building, enhancing, and managing data pipelines. This enables developers to leverage `dlt`'s
+capabilities for handling data extraction and load and Dagster's orchestration features to efficiently manage and monitor data pipelines.
+
+### Orchestrating `dlt` pipeline on Dagster
+
+Here's a concise guide to orchestrating a `dlt` pipeline with Dagster, using the project "Ingesting
+GitHub issues data from a repository and storing it in BigQuery" as an example. 
+
+More details can be found in the article
+[“Orchestrating unstructured data pipelines with dagster and dlt."](https://dagster.io/blog/dagster-dlt)
+
+**The steps are as follows:**
+1. Create a `dlt` pipeline. For more, please refer to the documentation:
+[Creating a pipeline.](https://dlthub.com/docs/walkthroughs/create-a-pipeline)
+
+1. Set up a Dagster project, configure resources, and define the asset as follows:
+
+   1. To create a Dagster project:
+      ```sh
+      mkdir dagster_github_issues  
+      cd dagster_github_issues  
+      dagster project scaffold --name github-issues  
+      ```
+
+   1. Define `dlt` as a Dagster resource:
+      ```py
+      from dagster import ConfigurableResource  
+      from dagster import ConfigurableResource  
+      import dlt  
+
+      class DltResource(ConfigurableResource):  
+          pipeline_name: str  
+          dataset_name: str  
+          destination: str  
+
+          def create_pipeline(self, resource_data, table_name):  
+
+              # configure the pipeline with your destination details  
+              pipeline = dlt.pipeline(  
+                  pipeline_name=self.pipeline_name, 
+                                destination=self.destination, 
+                                dataset_name=self.dataset_name  
+              )  
+
+              # run the pipeline with your parameters  
+              load_info = pipeline.run(resource_data, table_name=table_name)  
+
+              return load_info  
+      ```
+   1. Define the asset as:
+      ```py
+      @asset  
+      def issues_pipeline(pipeline: DltResource):  
+
+          logger = get_dagster_logger()  
+          results = pipeline.create_pipeline(github_issues_resource, table_name='github_issues')  
+          logger.info(results)  
+      ```
+      > For more information, please refer to
+      > [Dagster’s documentation.](https://docs.dagster.io/getting-started/quickstart)
+
+1. Next, define Dagster definitions as follows:
+   ```py
+   all_assets = load_assets_from_modules([assets])  
+   simple_pipeline = define_asset_job(name="simple_pipeline", selection= ['issues_pipeline'])  
+
+   defs = Definitions(  
+       assets=all_assets,  
+       jobs=[simple_pipeline],  
+       resources={  
+           "pipeline": DltResource(  
+               pipeline_name = "github_issues",  
+               dataset_name = "dagster_github_issues",  
+               destination = "bigquery",  
+               table_name= "github_issues"  
+           ),  
+       }  
+   ) 
+   ```
+
+1. Finally, start the web server as:
+
+   ```sh
+   dagster dev
+   ```
+
+:::info 
+For the complete hands-on project on “Orchestrating unstructured data pipelines with dagster and
+`dlt`", please refer to [article](https://dagster.io/blog/dagster-dlt). The author offers a
+detailed overview and steps for ingesting GitHub issue data from a repository and storing it in
+BigQuery. You can use a similar approach to build your pipelines.
+:::
+
+### Additional Resources
+
+- A general configurable `dlt` resource orchestrated on Dagster:
+  [dlt resource](https://github.com/dagster-io/dagster-open-platform/blob/5030ff6828e2b001a557c6864f279c3b476b0ca0/dagster_open_platform/resources/dlt_resource.py#L29).
+
+- Configure `dlt` pipelines for Dagster:
+  [dlt pipelines](https://github.com/dagster-io/dagster-open-platform/tree/5030ff6828e2b001a557c6864f279c3b476b0ca0/dagster_open_platform/assets/dlt_pipelines).
+
+- Configure MongoDB source as an Asset factory:
+  > Dagster provides the feature of
+  > [@multi_asset](https://github.com/dlt-hub/dlt-dagster-demo/blob/21a8d18b6f0424f40f2eed5030989306af8b8edb/mongodb_dlt/mongodb_dlt/assets/__init__.py#L18)
+  > declaration that will allow us to convert each collection under a database into a separate
+  > asset. This will make our pipeline easy to debug in case of failure and the collections
+  > independent of each other.
+
+:::note 
+These are external repositories and are subject to change. 
+:::

docs/website/sidebars.js

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+            'walkthroughs/deploy-a-pipeline/deploy-with-dagster',
           ]
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