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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 Google Cloud Dataproc - Optimize Managed Spark and Hadoop 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 Google Cloud Dataproc - Optimize Managed Spark and Hadoop 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 Google Cloud Dataproc - Optimize Managed Spark and Hadoop training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Rapid creation of optimized Dataproc clusters for Spark and Hadoop via GCP Console and gcloud CLI, advanced configuration of custom images with pre-installed libraries, launching initial Spark jobs on massive datasets to test automatic scaling, practical exercises on tuning workers and preemptibles to reduce costs by 40%, production of a first complete ETL pipeline with integrated monitoring via Stackdriver, real enterprise cases to validate real-time performance.
Implementation of complex Spark SQL queries on Dataproc with massive joins and window functions, integration of Hadoop Hive for federated queries on data lakes, development of PySpark scripts for distributed machine learning with partition and cache tuning, setup of dynamic autoscaling and preemption policies for load peaks, automated deployment via Cloud Build and Terraform for smooth CI/CD, completion of a main project on real enterprise data with performance metrics and optimization report.
Target audience
Data engineers, Big Data architects, Data DevOps professionals seeking to advance their skills on Dataproc
Prerequisites
Advanced expertise in Apache Spark, Hadoop YARN, and Google Cloud Platform fundamentals
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