An Exploration Of The Data Engineering Requirements For Bioinformatics

Data Engineering Podcast

Tobias Macey에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Tobias Macey 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

2+ y ago 55:09

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Summary

Biology has been gaining a lot of attention in recent years, even before the pandemic. As an outgrowth of that popularity, a new field has grown up that pairs statistics and compuational analysis with scientific research, namely bioinformatics. This brings with it a unique set of challenges for data collection, data management, and analytical capabilities. In this episode Jillian Rowe shares her experience of working in the field and supporting teams of scientists and analysts with the data infrastructure that they need to get their work done. This is a fascinating exploration of the collaboration between data professionals and scientists.

Announcements

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Your host is Tobias Macey and today I’m interviewing Jillian Rowe about data engineering practices for bioinformatics projects

Interview

Introduction
How did you get involved in the area of data management?
How did you get into the field of bioinformatics?
Can you describe what is unique about data needs in bioinformatics?
What are some of the problems that you have found yourself regularly solving for your clients?
When building data engineering stacks for bioinformatics, what are the attributes that you are optimizing for? (e.g. speed, UX, scale, correctness, etc.)
Can you describe a typical set of technologies that you implement when working on a new project?
- What kinds of systems do you need to integrate with?
What are the data formats that are widely used for bioinformatics?
- What are some details that a data engineer would need to know to work effectively with those formats while preparing data for analysis?
What amount of domain expertise is necessary for a data engineer to work in life sciences?
What are the most interesting, innovative, or unexpected solutions that you have seen for manipulating bioinformatics data?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on bioinformatics projects?
What are some of the industry/academic trends or upcoming technologies that you are tracking for bioinformatics?

Contact Info

LinkedIn
jerowe on GitHub
Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?