Parse, don’t validate| lexi-lambda.github.io
Data engineering project for beginners, using AWS Redshift, Apache Spark in AWS EMR, Postgres and orchestrated by Apache Airflow.| www.startdataengineering.com
Trying to incorporate testing in a data pipeline? This post is for you. In this post, we go over 4 types of tests to add to your data pipeline to ensure high-quality data. We also go over how to prioritize adding these tests, while developing new features.| www.startdataengineering.com
Frustrated that hiring managers are not reading your Github projects? then this post is for you. In this post, we discuss a way to impress hiring managers by hosting a live dashboard with near real-time data. We will also go over coding best practices such as project structure, automated formatting, and testing to make your code professional. By the end of this post, you will have deployed a live dashboard that you can link to your resume and LinkedIn.| www.startdataengineering.com
Working with a dataset that is too large to fit in memory? Then this post is for you. In this post, we will write memory efficient data pipelines using python generators. We also cover the common generator patterns you will need for your data pipelines.| www.startdataengineering.com
Unable to find practical examples of idempotent data pipelines? Then, this post is for you. In this post, we go over a technique that you can use to make your data pipelines professional and data reprocessing a breeze.| www.startdataengineering.com