Subject archive for "engineering," page 7

Data Science

Reflections on "Buy vs Build" for Data Science Tools

“Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections on the topic, based on my observations both as an engineering manager (where I had to decide whether to build or buy solutions) and more recently as a founder selling a platform to other companies.

By Nick Elprin11 min read

Data Science

Building an Open Product for Power Users

This post describes our engineering philosophy of building an “open” product, i.e., one that supports existing tools and libraries, rather than building our own custom version of existing functionality. Aside from letting our developers be more productive, we’ve found this approach makes our users much more productive — especially power users, who are especially important to us.

By Nick Elprin8 min read

Data Science

Cloud Security: The right way to worry

Here’s a question we hear a lot: We’re not that comfortable with the cloud from a security perspective -- can you install Domino on premise? The answer is yes (we have both an on-premises and cloud-hosted version of the Domino data science platform because that’s what clients want) but we think the central assumption of that question deserves further consideration, because it’s often wrong.

By Matthew Granade11 min read

Data Science

A Mongo-based Cache Plugin for Play

A quick engineering-related post: we built a cache plugin for Play that uses capped collections in Mongo. It's available on Github if you'd like to use it.

By Nick Elprin1 min read

Data Science

R Notebooks in the Cloud

We recently added a feature to Domino that lets you spin up an interactive R session on any class of hardware you choose, with a single click, enabling more powerful interactive, exploratory work in R without any infrastructure or setup hassle. This post describes how and why we built our "R Notebook" feature.

By Nick Elprin5 min read

Perspective

Management lessons from software engineering

As I've evolved from being an individual software developer to managing teams and starting a company—a data science platform—I continue to notice parallels between the principles of good engineering and the principles of good management. I have no delusions of novelty here but it's an interesting topic with a lot of surface area, so I plan to write more about this over the coming months. For now, I want to focus on one specific similarity: situations where "someone" is unable to do what has been asked of them.

By Nick Elprin6 min read

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