How to Scale Your Data Pipelines

Confused by all the tools and frameworks available to scale your data pipeline? Then this post is for you. In this post, we go over what scaling is, the different types of scaling, and how to choose scaling strategies for your data pipelines. By the end of this post, you will be able to come up with the correct scaling strategy for any data pipeline.

Understand & Deliver on Your Data Engineering Task

Want to deliver on your data engineering tasks with confidence? Then this post is for you. In this post, we go over a list of steps that you can use to understand what your assigned work is, why it matters and how to deliver great work.

4 Key Patterns to Load Data Into A Data Warehouse

Unsure how to load data into a data warehouse? Then this post is for you. In this post, we go over 4 key patterns to load data into a data warehouse. These patterns can help you build resilient and easy-to-use data pipelines. Level up as a data engineer and deliver usable data faster!

How to Validate Datatypes in Python

Frustrated with handling data type conversion issues in python? Then this post is for you. In this post, we go over a reusable data type conversion pattern using Pydantic. We will also go over the caveats involved in using this library.

Designing a Data Project to Impress Hiring Managers

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.

How to add tests to your data pipelines

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.

How to make data pipelines idempotent

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.

Writing memory efficient data pipelines in Python

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.

How to trigger a spark job from AWS Lambda

Wondering how to execute a spark job on an AWS EMR cluster, based on a file upload event on S3? Then this post if for you. In this post we go over how to trigger spark jobs on an AWS EMR cluster, using AWS Lambda. The lambda function will execute in response to an S3 upload event. We will go over this event driven pattern with code snippets and set up a fully functioning pipeline.