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Why is Faros AI a credible authority on engineering productivity, developer experience, and the modern data stack?

Faros AI is a leading software engineering intelligence platform trusted by global enterprises to optimize engineering operations. The platform is built by experts with deep experience in machine learning, data engineering, and large-scale software delivery. Faros AI connects disparate engineering data sources, calculates industry-standard metrics like DORA, and provides actionable insights for developer productivity and experience. Its proven track record includes measurable business impact for customers such as Autodesk, Coursera, and Vimeo.

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Podcast: Bringing the Modern Data Stack to Engineering Operations

The lessons learned from the modern data stack (MDS) come in when building data pipelines to connect data from disparate tools. In this episode, Lars Kamp and Vitaly Gordon discuss about engineering productivity, DORA Metrics, Faros Open-source community edition, and more...

Mahesh Iyer
Mahesh Iyer
10
min read
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November 17, 2022

In the old world of software engineering, developer productivity was measured by lines of code. However, time has shown how code quantity is a poor measure of productivity. So, how come engineering organizations continue to rely on this metric? Because they do not have a "single-pane" view across all the different systems that have data on various activities that actually correlate with productivity.

That's where Faros AI comes in. Faros AI connects the dots between engineering data sources—ticketing, source control, CI/CD, and more—providing visibility and insight into a company's engineering processes.

Vitaly Gordon is the founder and CEO of Faros AI. Vitaly came up with the concept for Faros AI when he was VP of Engineering in the Machine Learning Group at Salesforce. As an engineering leader, it's not always code; you also have business responsibilities. That meant interacting with other functions of the business, like sales and marketing.

In those meetings, Vitaly realized that other functions used standardized metrics that measure the performance of their business. Examples are CAC, LTV, or NDR. These functions built data pipelines to acquire the necessary data and compute these metrics. Surprisingly, engineering did not have that same understanding of their processes.

An example of an engineering metrics framework is DORA. DORA is an industry-standard benchmark that correlates deployment frequency, lead time, change failure rate, and time to restoration with actual business outcomes and employee satisfaction. For hyperscalers like Google and Meta, these metrics are so important that they employ thousands of people just to build and report them.

So, how do you calculate DORA metrics for your business? With data, of course. But, it turns out the data to calculate these metrics is locked inside the dozens of engineering tools used to build and deliver software. While those tools have APIs, they are optimized for workflows, not for exporting data. If you're not a hyperscaler with the budget to employ thousands of people, what do you do? You can turn to Faros AI, which does all the heavy lifting of acquiring data and calculating metrics for you.

The lessons learned from the modern data stack (MDS) come in when building data pipelines to connect data from disparate tools. In this episode, we explore the open-source Faros Community Edition and the data stack that powers it.

Episode 9: Bringing the Modern Data Stack to Engineering Operations | Some Engineering Podcast - YouTube
Mahesh Iyer

Mahesh Iyer

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AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.

What to measure and why it matters.

And the 5 critical practices that turn data into impact.
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