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 Training Apache Spark - Process Your Big Data in 4 Days training are: Basics in Python programming, knowledge of SQL and big data concepts.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Apache Spark - Process Your Big Data in 4 Days training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Apache Spark - Process Your Big Data in... | 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 Training Apache Spark - Process Your Big Data in 4 Days 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 Training Apache Spark - Process Your Big Data in 4 Days 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 Training Apache Spark - Process Your Big Data in 4 Days 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 access terms and deadlines

Training objectives

Understand Apache Spark's distributed architecture

Install and configure a Spark environment

Manipulate RDDs for fast transformations

Use DataFrames and Spark SQL for structured analyses

Execute distributed jobs on local clusters

Optimize basic performance with caching

Apply Spark to a concrete Big Data case

Training program

Module 1Introduction to Apache Spark: architecture and installation (PySpark, SparkSession)

Dive into the basics of Apache Spark, install the environment via Docker and PySpark, create your first SparkSession, explore the master-worker architecture with interactive exercises, launch Spark shells on real datasets, and generate your first execution logs to visualize distributed processing, while gaining immediate confidence.

Module 2RDDs: transformations and actions (map, filter, reduce)

Master RDDs, Spark's resilient core, apply map, filter, and reduce on massive CSV files, chain lazy transformations and trigger actions, test on real cases like web log analysis, debug with Spark UI, and generate ready-to-use statistical deliverables to process millions of lines effortlessly.

Module 3DataFrames and Spark SQL: optimized queries (joins, aggregations)

Move to performant DataFrames, load JSON or Parquet data, write native SQL queries, perform joins and groupBy on real e-commerce datasets, optimize with Catalyst, visualize results via Pandas, and export to CSV or NoSQL databases, turning your analyses into actionable insights in just a few clicks.

Module 4Advanced jobs and final project: caching, optimization, practical case

Execute complete jobs on a mini-cluster, apply caching and partitioning to accelerate, integrate Spark with Jupyter, solve an end-to-end project on IoT data, measure performance gains, share deliverables via Git, and leave with a concrete portfolio ready to scale in production.