Audio Streaming + Podcasting
Use Case:

To Deliver Complex Financial Reports, Spotify Turns to Flyte™

Spotify is a digital music, podcast and video service that serves consumers millions of songs and other content from creators all over the world. With nearly 200 million subscribers in 180 markets, Spotify’s Streaming, Automation and Tooling, Data and Insights, Business Modeling and Forecasting, and Data Workflow Scheduling teams work continuously to support the business’ growth.

To meet regulatory compliance objectives, Spotify’s finance team must generate quarterly reports containing two-year P&L projections that include model-based revenue projections for emerging markets. Spanning Spotify’s markets and business processes, these reports required up to eight different teams to configure and run 15-plus financial models. End-to-end forecasting typically took three or four weeks every quarter. 

To align processes, eliminate errors and speed results, Spotify’s finance team realized it needed a cohesive platform that could coordinate pipelines built with many technologies. After comparing leading orchestrators on the market, Spotify chose Flyte™ as the main runtime engine of their model.

Dylan Wilder, an engineering manager at Spotify, praised Flyte™’s ability to engage data scientists directly in the refinement and exchange of models. “As engineers, a lot of this might be table stakes for us, but for data scientists, being able to get up and running on Flyte™,” he said. “Getting all of this stuff for free is a really big win for them.” 

The result? With Flyte™, Spotify reduced time-to-forecast, increased the number of runs possible in a quarter and minimized the number of human errors.

What’s more, Spotify can run more business cases and scenario analyses more often to give the leadership team a fast, accurate view of its best course.  “The number of forecasts run went from being limited to once per quarter involving all teams to thousands [of forecasts] for analytics purposes. We still have some work to do on reducing errors related to user configuration, but we've eliminated those related to manual handoffs.” 

“The overall quarterly process has gone from four weeks to less than two.”

Some noteworthy excerpts from the video:

As engineers, a lot of this might be table stakes for us, but for data scientists, being able to get up and running on Flyte™, and getting all of this stuff for free, is a really big win for them.”

The ability to share models with each other,  compose things easily, out-of-the-box parallelism, …, caching, connectivity with GCS; they (data scientists) have been really excited about all of this stuff.”

“The fact that Flyte™ supports immutable models, versioning, and graph workflows allows us to think about the state of the graph. We also rest on the fact that Flyte™ will keep all these things coherent and cache things correctly.”