18/10/2025 –, Track 05 - E05, A02
Idioma: Español
Lithops is a Python multi-cloud distributed computing framework.
It allows you to run unmodified local python code at massive scale in the main serverless computing platforms.
Lithops delivers the user’s code into the cloud without requiring knowledge of how it is deployed and run.
Moreover, its multicloud-agnostic architecture ensures portability across cloud providers and on-premise deployments.
In particular, Lithops offers compute and storage backends for most public Clouds (AWS, Google, Azure, IBM, Oracle,
Alibaba), HPC Supercomputers (LithopsHPC), and on-premise deployments (OpenShift, OpenNebula, k8s).
Lithops is well suited for highly-parallel programs (parallel map) with little or no need for communication between
processes (i.e. Monte Carlo simulations). However, Lithops is especially useful for parallel data processing
where many functions read or write in parallel from Object Storage.
In this talk, you will learn how to run parallel python code over different Cloud backends with minimum effort and zero changes in code.
The magic of lithops is the burstability and elasticity of Cloud resources, enabling to launch 1000 processes in parallel in less than 100ms.
Data Science and Data Engineering (analytics, visualization, pipelines, data engineering, notebooks...)
Temáticas adicionales:DevOps, Cloud and Infrastructure (SRE, systems management, CI/CD, Kubernetes, cloud providers...), Python Core and Package Development (Python core, library development, typing, compatibility...)
Nivel de la propuesta:Intermediate (it is necessary to understand the related bases to go into detail)
Daniel Alejandro Coll Tejeda is a Software Engineer and Cloud Researcher at the Cloud and Distributed Systems Lab (URV), with extensive experience in developing and researching solutions across major cloud platforms including AWS, IBM Cloud, and GCP. His work has involved leveraging powerful tools like Lithops for serverless computing and Kubernetes for container orchestration, focusing on their practical application to solve complex scientific and data processing challenges. Daniel is passionate about pushing the boundaries of cloud technology and holds a degree in Computer Engineering from Universitat Rovira i Virgili.
Building on this deep cloud expertise, Daniel is currently one of the main creators of PyRun, a platform designed to democratize scalable cloud computing for Python users. PyRun simplifies running Python workloads—from data processing to AI—on your own cloud account by automating infrastructure management, runtime configuration, and seamlessly integrating frameworks like Lithops and Dask.