The article that OP posted has a link to the following article, perhaps it may be more comprehensible:

Tulip: Schematizing Meta’s data platform

>* We’re sharing Tulip, a binary serialization protocol supporting schema evolution.

• Tulip assists with data schematization by addressing protocol reliability and other issues simultaneously.
• It replaces multiple legacy formats used in Meta’s data platform and has achieved significant performance and efficiency gains.

>There are numerous heterogeneous services, such as warehouse data storage and various real-time systems, that make up Meta’s data platform — all exchanging large amounts of data among themselves as they communicate via service APIs. As we continue to grow the number of AI- and machine learning (ML)–related workloads in our systems that leverage data for tasks such as training ML models, we’re continually working to make our data logging systems more efficient.

>Schematization of data plays an important role in a data platform at Meta’s scale. These systems are designed with the knowledge that every decision and trade-off can impact the reliability, performance, and efficiency of data processing, as well as our engineers’ developer experience.

>Making huge bets, like changing serialization formats for the entire data infrastructure, is challenging in the short term, but offers greater long-term benefits that help the platform evolve over time.

 

Supporting info:

• https://en.wikipedia.org/wiki/Serialization

• https://en.wikipedia.org/wiki/Schema_evolution

• https://en.wikipedia.org/wiki/API

They’ve built a new tool that lets them log data from their AI servers more efficiently. I’m sure it helps make things easier for the engineers at Meta, but I don’t think it has much wider impact outside of the data science community.