Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Formation Apache Spark - Traiter big data distribué efficacement training are: Bases en Python ou Scala, SQL avancé, notions Hadoop ou bases big data. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Apache Spark - Traiter big data distribué efficacement training is available in présentiel. The format can be adapted to your needs (inter, intra, custom).

Formation Apache Spark - Traiter big data distri... | Learni

The story of Learni

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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Methods, approaches, and teaching resources

The Formation Apache Spark - Traiter big data distribué efficacement 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.

Teaching resources provided

To ensure the quality of the Formation Apache Spark - Traiter big data distribué efficacement training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and 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.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Formation Apache Spark - Traiter big data distribué efficacement training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Maîtriser RDD et DataFrames Spark pour analyses professionnelles

Développer pipelines ETL scalables en entreprise

Implémenter Spark SQL pour requêtes massives certifiantes

Concevoir applications Spark Streaming temps réel

Optimiser performances Spark clusters production

Intégrer Spark MLlib compétences machine learning distribué

Training program

Module 1Fondamentaux Spark : installation cluster et RDD avancés (Spark Shell, YARN, exercices ETL)

Installation environnement Spark standalone et YARN, prise en main Spark Shell pour exécuter jobs distribués, manipulation RDD transformations actions sur datasets téraoctets, exercices pratiques création pipelines ETL cas entreprise réels, debug erreurs partitioning shuffle, production premier job Spark traitant logs serveurs massifs, code review formateur valorisant bonnes pratiques scalabilité.

Module 2Spark SQL DataFrames : requêtes optimisées datasets structurés (Catalog, UDF, joins broadcast)

Exploration DataFrames API fluide versus RDD, Spark SQL requêtes SQL pures sur Hive tables, création vues temporaires UDF fonctions personnalisées, optimisation joins broadcast Catalyst optimizer, cas concrets jointures datasets clients transactions milliards lignes, exercices partitioning bucketing Hive, génération rapports agrégés temps record, intégration PySpark Scala projets professionnels.

Module 3Spark Streaming ML : flux temps réel optimisation (Kafka, MLlib, tuning clusters)

Configuration Spark Streaming micro-batches Kafka sources, traitement flux IoT logs temps réel fenêtres tumbling, implémentation MLlib pipelines classification régression distribuée, tuning performances garbage collection spill disk, monitoring UI Spark clusters Kubernetes, projet fil rouge déploiement application complète production, tests scalabilité charge élevée, documentation déploiement DevOps.