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 Real-Time Big Data training are: Mastery of Spark Core, Scala or Python programming, advanced SQL, basics of Hadoop and distributed clusters.. A preliminary interview validates your eligibility.

Is the training available in person?

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

Advanced Apache Spark Training - Optimize Real-T... | 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 Real-Time 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 Advanced Apache Spark Training - Optimize Real-Time 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 Advanced Apache Spark Training - Optimize Real-Time 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

Optimize Spark job performance with advanced caching and partitioning.

Implement Spark Streaming for real-time data stream processing.

Master Spark SQL for complex queries on massive datasets.

Develop ML pipelines with Spark MLlib and GraphX.

Manage and deploy Spark clusters on YARN or Kubernetes.

Debug and monitor Spark applications in production.

Integrate Spark with Kafka and other Big Data tools.

Training program

Module 1Day 1: Spark Optimization - DataFrames, caching, partitioning (Spark SQL, TPC-DS dataset exercises)

Dive into advanced DataFrame optimization in Spark, test strategic caching and dynamic partitioning on massive real-world datasets, complete practical exercises to reduce execution times by 70%, generate custom performance reports, while mastering Catalyst Optimizer configurations to boost your daily jobs.

Module 2Day 2: Spark Streaming - Real-Time Processing (DStreams, Structured Streaming, Kafka integration)

Build fault-tolerant streaming pipelines with Structured Streaming, integrate Kafka to ingest continuous streams, simulate micro-batch scenarios on real IoT data, develop scalable applications handling millions of events per second, generate real-time alerts, and validate deliverables through integrated unit tests.

Module 3Day 3: Spark SQL and MLlib - Advanced Queries, Distributed Machine Learning (UDFs, ML pipelines)

Execute complex Spark SQL queries with UDFs and analytic windows on petabytes of data, train distributed MLlib models on clusters, optimize pipelines for batch prediction, apply GraphX to real-world social graphs, produce interactive dashboards, and measure impact on prediction accuracy.

Module 4Day 4: Deployment and Monitoring - YARN/Kubernetes Clusters, Debugging (Spark UI, Prometheus)

Deploy Spark applications on YARN and Kubernetes in production mode, configure monitoring with Spark UI and Prometheus, debug failing jobs using advanced logs and profilers, simulate cluster failures to test resilience, finalize a capstone project with a deployable deliverable ready for enterprise environments.