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 Databricks Training - Master Large-Scale Data Pipelines training are: Basics in Python or SQL, knowledge of Spark, experience with large-scale data processing.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Databricks Training - Master Large-Scale Data Pipelines training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Databricks Training - Master Large-Scale Data Pi... | 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 Databricks Training - Master Large-Scale Data Pipelines 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 Databricks Training - Master Large-Scale Data Pipelines 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 Databricks Training - Master Large-Scale Data Pipelines 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 program

Module 1Databricks Fundamentals: Installation and Collaborative Notebooks (Spark, Delta Lake)

Dive into the Databricks universe by creating your first workspace, configure interactive Spark clusters, write notebooks in Python and SQL to explore massive datasets, complete hands-on exercises on Delta Lake to manage ACID tables, produce your first deliverables like an interactive dashboard, and discover tips for seamless team collaboration.

Module 2Data Engineering: Advanced ETL Pipelines (Delta Live Tables, Spark SQL)

Build robust ETL pipelines with Delta Live Tables, process large data streams using Spark SQL and DataFrames, apply complex transformations on real-world cases like IoT log ingestion, test data quality in real-time, generate automated reports, and validate your skills with a complete ETL project to take away.

Module 3Machine Learning and Streaming: MLflow, Structured Streaming (Models, Jobs)

Deploy ML models with MLflow by logging experiments and artifacts, integrate Structured Streaming to analyze real-time data like Kafka streams, train ML pipelines on GPU clusters, monitor performance live, collaborate on shared runs, and produce a production-ready predictive model.

Module 4Optimization and Automation: Workflows, Cluster Tuning (Best Practices, Security)

Master Databricks Workflows to orchestrate multi-step jobs, optimize clusters with Auto Scaling and Photon Engine, apply tuning techniques on real benchmarks, secure environments with Unity Catalog, simulate production deployments, and conclude with a complete case study and certifiable final deliverable.