Data Integration Guide: Techniques, Technologies, and Tools
Centralizing data can be super value-added but it shouldn't be sought after JUST BECAUSE it's seen as value-added. There should always be an explicitly defined goal for what your data teams want to do with that centralized data BEFORE significant devops or engineering effort is spent to make it happen. - John Forstmeier
Python Libraries Data Scientists Should Know in 2022
While this article will seem basic to Python users, I have met quite a few data professionals in my short career in the data field that want to learn Python and do not know where to start. I like that this article outlines some of the most important Python libraries and talks about why you should care about them. This would be a great article to share with someone wanting to break into the data field or learn Python. - Steven Johnson
What Is Active Metadata, and Why Does It Matter?
Active metadata was an entirely new buzzword for me, but it articulates many of the points I've been emphasizing for the past few years. By ensuring that metadata is automatically evaluated and present across your tools, you can more quickly activate that metadata to create powerful solutions. - Blake Burch
ELT for the Data Consumer
While obviously pitching itself as the solution, this piece from the Rasgo blog raises a noticeable pain point in the modern data stack despite data integration/centralization. While this has been a good development for data providers, data consumers now need specialized understanding and a reliance on those providers in order to generate useful insights. - John Forstmeier
4 Techniques To Deal With Missing Data in Datasets
If all raw data sets were clean and complete, it would be a dream... and would probably result in reduced job security for data scientists everywhere! The reality is that raw data is rarely clean and needs expert processing. So, for you experts out there, Egor offers some quick and simple techniques to clean up and work with missing data. - Katt Baum
Run Your Data Team like a Company That Needs To Scale
I thought Emily had a fresh perspective on how you should think about managing your data team. Try to put on your "marketing hat" or your "operations hat" to ask deeper questions and isolate specific aspects of your data team that need to be improved. - Blake Burch
The Many Hats of a Data Analyst
After I graduated with my degree in Analytics last year, I began to apply for jobs and felt quite overwhelmed with the infinite amount of titles and job descriptions. To make the matters worse, I felt like the title of specific roles such as a data analyst could mean completely different things on almost every job description. I feel like the industry could use a bit more standardization in what a role and its responsibilities are. I feel like Ilan does a great job of breaking down what these specific roles mean to him. - Steven Johnson