<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>AWS Serverless on Jaehyeon Kim</title><link>https://jaehyeon.me/tags/aws-serverless/</link><description>Recent content in AWS Serverless on Jaehyeon Kim</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Copyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.</copyright><lastBuildDate>Wed, 28 Sep 2022 00:00:00 +0000</lastBuildDate><atom:link href="https://jaehyeon.me/tags/aws-serverless/index.xml" rel="self" type="application/rss+xml"/><item><title>Data Build Tool (dbt) for Effective Data Transformation on AWS – Part 1 Redshift</title><link>https://jaehyeon.me/blog/2022-09-28-dbt-on-aws-part-1-redshift/</link><pubDate>Wed, 28 Sep 2022 00:00:00 +0000</pubDate><guid>https://jaehyeon.me/blog/2022-09-28-dbt-on-aws-part-1-redshift/</guid><description>The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services - Redshift, Glue, EMR and Athena. In part 1 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Redshift Serverless. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.
Part 1 Redshift (this post) Part 2 Glue Part 3 EMR on EC2 Part 4 EMR on EKS Part 5 Athena Motivation In our experience delivering data solutions for our customers, we have observed a desire to move away from a centralised team function, responsible for the data collection, analysis and reporting, towards shifting this responsibility to an organisation&amp;rsquo;s lines of business (LOB) teams.</description><enclosure url="https://jaehyeon.me/blog/2022-09-28-dbt-on-aws-part-1-redshift/featured.png" length="97234" type="image/png"/></item></channel></rss>