When your pipelines process millions of complex data points every day to feed predictive models, staying up-to-date with your technology stack is a strategic necessity. As the company's volume of data and number of machine learning workloads scaled up, Pine59 decided to modernize its monorepo, which contains hundreds of directed acyclic graphs (DAGs).
As the company's volume of data and number of machine learning workloads scaled up, Pine59 decided to modernize its monorepo, which contains hundreds of directed acyclic graphs (DAGs).
Operating large data pipelines requires an orchestration layer that scales smoothly as workloads expand.
When your pipelines process millions of complex data points every day to feed predictive models, staying up-to-date with your technology stack is a strategic necessity.
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Operating large data pipelines requires an orchestration layer that scales smoothly as workloads expand. When your pipelines process millions of complex data points every day to feed predictive models, staying up-to-date with your technology stack is a strategic necessity. One of the company's most data-intensive metrics, Daily Foot Traffic, computes data for as many as 14 million distinct locations in a single job.
Proactive modernization for growth Pine59 has long relied on a shared monorepo with code and tooling spanning multiple projects to run its metric production pipelines.
The initial results were unambiguous: the Gen 3 environment...
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