|
Description:
|
|

Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment, reproducibility, and other areas. The programming language Golang presents an appealing alternative for data scientists.
Daniel Whitenack transitioned from doing most of his data science work in Python to writing code in Golang. In this episode, Daniel explains the workflow of a data scientist and discusses why Go is useful. We also talk about the blurry line between data science and data engineering, and how Pachyderm is useful for versioning and reproducibility. Daniel works at Pachyderm, and listeners who are more curious about it can check out the episode I did with Pachyderm founder Joe Doliner.
Transcript
Sponsors
Dice.com helps you manage your career in tech. Dice.com has a huge index of tech job opportunities that it has developed from 20 years in the business of connecting tech professionals with job opportunities. To check out Dice and support Software Engineering Daily, go to dice.com/sedaily.
Exaptive simplifies your data application development. Exaptive is a data application studio that is optimized for rapid development of rich applications. Go to exaptive.com/sedaily to get a free trial and start building applications today.
Couchbase is a document database with the flexibility of NoSQL and the power of SQL. With Couchbase Server, you can build a fast, powerful NoSQL database that scales. Running Couchbase in containers on Kubernetes, Mesos, or OpenShift is easy, and at developer.couchbase.com you can find tutorials on how to build out your Couchbase deployment.
The post Go Data Science with Daniel Whitenack appeared first on Software Engineering Daily. |