From 1fb77733a127f20d882c325e9bef639584dd433f Mon Sep 17 00:00:00 2001 From: dat-a-man <98139823+dat-a-man@users.noreply.github.com> Date: Thu, 21 Mar 2024 11:38:50 +0000 Subject: [PATCH] Updated --- .../deploy-a-pipeline/deploy-with-kestra.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md index 6f938c28d4..09ddea6ac8 100644 --- a/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md +++ b/docs/website/docs/walkthroughs/deploy-a-pipeline/deploy-with-kestra.md @@ -9,9 +9,9 @@ keywords: [how to, deploy a pipeline, Kestra] ## Introduction to Kestra [Kestra](https://kestra.io/docs) is an open-source, scalable orchestration platform that enables -all engineers to manage business-critical workflows declaratively in code. By applying  +engineers to manage business-critical workflows declaratively in code. By applying  infrastructure as code best practices to data, process, and microservice orchestration, you -can build reliable workflows and manage them. +can build and manage reliable workflows. Kestra facilitates reliable workflow management, offering advanced settings for resiliency, triggers, real-time monitoring, and integration capabilities, making it a valuable tool for data @@ -21,7 +21,7 @@ engineers and developers. Kestra provides a robust orchestration engine with features including: -- Workflows are accessible through a user interface, event-driven +- Workflows accessible through a user interface, event-driven automation, and an embedded visual studio code editor. - It also offers embedded documentation, a live-updating topology view, and access to over 400 plugins, enhancing its versatility. @@ -32,17 +32,17 @@ To know more, please refer to [Kestra's documentation.](https://kestra.io/docs) ## 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 by providing functionality to support the entire extract and load -process. +**`dlt`** is an open-source Python library that allows you to declaratively load data sources +into well-structured tables or datasets. It does this through automatic schema inference and evolution. +The library simplifies building data pipeline by providing functionality to support the entire extract +and load process. ### How does `dlt` integrate with Kestra for pipeline orchestration? To illustrate setting up a pipeline in Kestra, we’ll be using the following example: [From Inbox to Insights AI-Enhanced Email Analysis with dlt and Kestra.](https://kestra.io/blogs/2023-12-04-dlt-kestra-usage) -It demonstrates automating a workflow to load data from Gmail to BigQuery using the `dlt`, +The example demonstrates automating a workflow to load data from Gmail to BigQuery using the `dlt`, complemented by AI-driven summarization and sentiment analysis. You can refer to the project's github repo by clicking [here.](https://github.com/dlt-hub/dlt-kestra-demo)