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 - Optimiser le traitement big data training are: Maîtrise des RDD et DataFrames Spark de base, Python ou Scala avancé, SQL et clusters distribués. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Apache Spark - Optimiser le traitement big data training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Apache Spark - Optimiser le traitement... | 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 - Optimiser le traitement big data 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 - Optimiser le traitement big data 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 - Optimiser le traitement big data 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 Spark SQL et DataFrames pour des analyses big data performantes en entreprise

Optimiser les performances d'applications Spark sur clusters scalables

Implémenter Spark Streaming pour le traitement de flux temps réel certifiant

Développer des pipelines machine learning avec MLlib professionnels

Intégrer Spark à Kafka et Hadoop dans des environnements production

Déployer et monitorer des jobs Spark pour des compétences certifiantes

Training program

Module 1Spark : DataFrames avancés et optimisation performances (Spark SQL, Catalyst, tuning cluster)

Configuration immédiate d'un cluster Spark distribué en mode cloud, plongée dans Spark SQL pour des queries complexes sur datasets téraoctets, maîtrise du Catalyst Optimizer pour accélérer les joins et agrégations, création d'UDF personnalisées en Python/Scala, exercices pratiques sur cas d'entreprise réels comme l'analyse de logs massifs, tuning des paramètres mémoire et exécuteurs pour des gains de 50% en vitesse, production de livrables optimisés prêts pour production.

Module 2Spark : Streaming temps réel et MLlib en entreprise (Kafka, pipelines ML, monitoring Ganglia)

Intégration Spark Streaming avec Kafka pour ingérer des flux IoT en temps réel, développement de micro-batches fault-tolerant sur volumes élevés, construction de pipelines MLlib complets avec feature engineering distribué et modèles scalables, évaluation cross-validation sur clusters, déploiement automatisé via Spark Submit et monitoring avancé avec Spark UI/Ganglia, ateliers collaboratifs sur projets fil rouge d'entreprise, génération de dashboards prédictifs et plans de scaling pour ROI immédiat.