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 Advanced Apache Spark Training - Optimize Your Big Data Jobs training are: Mastery of Python or Scala, solid foundations in Apache Spark (RDD, DataFrames), advanced SQL, Linux environment.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Advanced Apache Spark Training - Optimize Your Big Data Jobs training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Advanced Apache Spark Training - Optimize Your B... | 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 Advanced Apache Spark Training - Optimize Your Big Data Jobs 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 Advanced Apache Spark Training - Optimize Your Big Data Jobs 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 Advanced Apache Spark Training - Optimize Your Big Data Jobs 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 program

Module 1Day 1: Spark Performance Optimization (tuning, partitions, executors)

Dive into advanced Apache Spark optimization, configure partitions and executors on terabyte datasets through practical exercises, analyze DAG stages with Spark UI, reduce costly shuffles, produce before/after benchmarks to measure x10 gains, integrate persistent caches to accelerate iterations.

Module 2Day 2: Spark SQL and Catalyst Optimizer (complex queries, UDF)

Build advanced SQL pipelines with Apache Spark, write massive joins and window functions on DataFrames, leverage Catalyst to predict and optimize execution plans, create scalable UDFs in Python/Scala, test on real TPC-DS cases, generate materialized views to boost daily performance.

Module 3Day 3: Spark Structured Streaming (real-time, fault-tolerance)

Develop continuous streaming applications with Apache Spark, integrate Kafka and real-time sources via live exercises, manage exactly-once semantics and watermarks, scale on dynamic clusters, simulate failure scenarios to test resilience, produce Kafka-Spark dashboards for production monitoring.

Module 4Day 4: Machine Learning with MLlib (pipelines, hyperparameter tuning)

Build distributed ML models on Apache Spark, chain transformers and estimators in MLlib pipelines, apply CrossValidator for automatic tuning, integrate TensorFrames for deep learning, train on million-record datasets via hands-on labs, deploy PMML models for scalable real-time scoring.

Module 5Day 5: Deployment, Monitoring and Best Practices (YARN, Kubernetes)

Deploy Spark apps on YARN and Kubernetes through live coding, configure Kerberos security and dynamic allocators, monitor with Ganglia/Prometheus via exercises, debug OOM and spills on real cases, synthesize best practices in a deliverable checklist, prepare for immediate production certification.