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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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The Training Polars - Process Billions of Lines in Rust training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training Polars - Process Billions of Lines in Rust training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training Polars - Process Billions of Lines in Rust training is carried out through:
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Dive into advanced Polars expressions to transform massive datasets, configure lazy DataFrames to defer calculations until the final scan, manipulate columns with ultra-performant vectorized functions in Rust, perform practical exercises on multi-GB enterprise logs, benchmark against Pandas to measure x100 speed gains, produce modular production-ready pipelines with complete documentation and code review by the expert trainer.
Optimize complex joins and groupings on billions of lines with Polars streaming, integrate with Apache Arrow for Kafka-Spark interoperability, develop real-time analysis cases on enterprise IoT data, test Dockerized deployments with performance monitoring, build a scalable ongoing project deliverable at the end of the session, apply indexing and partitioning techniques to reduce query times by 90%, leave with certifying skills to boost your professional data pipelines.
Target audience
Data engineers, data scientists, expert BI analysts in massive data processing for career transition
Prerequisites
Advanced mastery of Rust or Python, experience with Pandas or PySpark, skills in distributed data processing
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