Setting up data infra is one of the most complex parts of starting a data engineering project. Overwhelmed trying to set up data infrastructure with code? Or using dev ops practices such as CI/CD for data pipelines? In that case, this post will help! This post will cover the critical concepts of setting up data infrastructure, development workflow, and sample data projects that follow this pattern. We will also use a data project template that runs Airflow, Postgres, & Metabase to demonstrate how each concept works.
Frustrated trying to pry data pipeline requirements out of end users? Is scope creep preventing you from delivering data projects on time? You assume that the end-users know (and communicate) exactly what they want, but that is rarely the case! Adhoc feature/change requests throw off your delivery timelines.
You want to deliver on time and make an impact, but you are interrupted constantly! Requirements gathering is rough, but it doesn't have to be!
We go over five steps you can follow, to work with the end-user to define requirements, validate data before wasting engineering hours, deliver iteratively, and handle new feature/change requests without context switching.
Do you feel overworked and underpaid? Are you a data engineer labeled as a data analyst, with a data analyst pay? Are you struggling to prepare efficiently for data engineering interviews? You want to work on problems that interest you, be able to earn a good salary and be acknowledged for your skills. But, landing such a job can seem daunting and almost impossible. In this post, we will go over 5 steps to help you land a data engineering role that pays well and enables you to work on interesting problems
Struggling with setting up a local development environment for your python data projects? Then this post is for you! In this post, you will learn how to set up a local development environment for data projects using docker. By the end of this post, you will know how to set up your local development environment the right way with docker. You will be able to increase developer ergonomics, increase development velocity and reduce bugs.
Confused by all the "data lake vs data warehouse" articles? Struggling to understand what the differences between data lakes and warehouses are? Then this post is for you. We go over what data lakes and warehouses are. We also cover the key points to consider when choosing your lake and warehouse tools.
Struggling to come up with a data engineering project idea? Overwhelmed by all the setup necessary to start building a data engineering project? Don't know where to get data for your side project? Then this post is for you. We will go over the key components, and help you understand what you need to design and build your data projects. We will do this using a sample end-to-end data engineering project.
Worried about introducing data pipeline bugs, regressions, or introducing breaking changes? Then this post is for you. In this post, you will learn what CI is, why it is crucial to have data tests as part of CI, and how to create a CI pipeline that automatically runs data tests on pull requests using Github Actions.
So you know how it can be overwhelming to choose the right tools for your data pipeline? What if you knew the core components involved in any data pipeline and can always pick the right tools for your data pipeline? Now you can! Use this framework to choose the best tool for your data pipeline.
Worried about setting up end-to-end tests for your data pipelines? Wondering if they are worth the effort? Then, this post is for you. In this post, we go over some techniques to set up end-to-end tests. We will also see which components to prioritize while testing.