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 des données massives en cluster training are: Connaissances en Python ou Scala, SQL avancé et bases de Hadoop ou Spark. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Formation Apache Spark - Traiter des données massives en cluster training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Formation Apache Spark - Traiter des données mas... | 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 des données massives en cluster 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 des données massives en cluster 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 des données massives en cluster 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 les RDD et DataFrames pour des traitements big data professionnels

Développer des pipelines ETL scalables avec Spark en entreprise

Implémenter Spark SQL pour des requêtes optimisées sur datasets massifs

Concevoir des applications Spark Streaming en temps réel certifiantes

Optimiser les performances Spark pour réduire les coûts cloud

Déployer des clusters Spark sécurisés et monitorés

Acquérir des compétences Spark pour booster la productivité data

Training program

Module 1Fondamentaux Apache Spark : architecture distribuée et RDD (Scala, PySpark, outils cluster)

Installation et configuration d'un cluster Spark local ou cloud via Databricks, exploration des concepts clés comme le driver, executors et SparkContext, manipulation pratique des RDD pour transformations et actions sur datasets volumineux, exercices sur des cas réels d'entreprise avec jointures et agrégations, création de votre premier job Spark résilient aux pannes, code review par le formateur pour une mise en production immédiate.

Module 2Apache Spark SQL et DataFrames : ETL avancés (optimisations Catalyst, Hive integration)

Plongée dans Spark SQL pour des requêtes SQL expressives sur DataFrames, construction de pipelines ETL complets avec lecture/écriture Parquet, JSON et bases NoSQL, utilisation du Dataset API pour type-safety et performances, cas pratiques sur data warehouses d'entreprise, tuning des partitions et caching pour accélérer les jobs, production de rapports analytiques scalables avec UDF personnalisées, validation de vos ETL via tests unitaires intégrés.

Module 3Apache Spark Streaming et optimisation : MLlib, GraphX en production (tuning, monitoring)

Développement d'applications streaming avec Structured Streaming pour IoT et logs en temps réel, intégration Kafka et sources multiples, optimisation avancée des jobs via Tungsten et whole-stage codegen, utilisation MLlib pour machine learning distribué sur clusters, monitoring avec Spark UI et Prometheus, déploiement sécurisé avec Kerberos et SSL, finalisation de votre projet fil rouge avec métriques de performance, plan d'action pour scaler en entreprise.