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 - Mastering the Lakehouse for Data Pros training are: Basic knowledge of Python and SQL, familiarity with structured data concepts. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Databricks Training - Mastering the Lakehouse for Data Pros training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Databricks Training - Mastering the Lakehouse for… | 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 - Mastering the Lakehouse for Data Pros 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 - Mastering the Lakehouse for Data Pros 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 - Mastering the Lakehouse for Data Pros 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

Master Databricks fundamentals for professional analyses

Develop collaborative notebooks in a certified enterprise environment

Implement Spark on Databricks to process massive volumes

Design data pipelines with Delta Lake and Unity Catalog

Optimize data workflows for scalable competencies

Deploy Lakehouse projects tailored to business needs

Training program

Module 1Databricks Fundamentals: Setup and First Notebooks (Workspace, Clusters)

Discovery of the Databricks platform through creation of a professional workspace account, configuration of initial Spark clusters for fast data processing, hands-on experience with interactive notebooks using Python and SQL, practical exercises on importing real enterprise datasets, simple exploratory analyses with integrated visualizations, production of a first basic dashboard to validate immediate learning outcomes.

Module 2Data Exploration in Databricks: Manipulation and Advanced SQL (Queries, Joins)

Deep dive into Databricks notebooks for distributed querying with Spark SQL, DataFrame manipulation via the Python pyspark API, exercises on cleaning and transforming large datasets from concrete business cases, use of Magic Commands for seamless execution, real-time collaboration on shared notebooks, generation of team-ready interactive reports, skill consolidation through a personalized ongoing project.

Module 3Spark on Databricks: Distributed ETL (DataFrames, RDD, Optimizations)

Intensive training on the Spark engine in Databricks for scalable ETL, advanced DataFrame transformations with aggregations and window functions, practical exercises on automated data pipelines for terabyte-scale volumes, cluster tuning for optimal performance, enterprise use cases such as log or sales processing, development of scheduled batch jobs, delivery of reusable scripts tested in real-world conditions.

Module 4Delta Lake in Databricks: Data Reliability (ACID, Versioning, Z-Order)

Mastery of Delta Lake for transactional data lakes on Databricks, implementation of ACID tables with merge and upsert operations on live datasets, time travel exercises for audits and rollbacks, storage optimization using Z-Order and compaction, integration with Unity Catalog for secure governance, hands-on projects with sensitive enterprise data, production of reliable pipelines with built-in monitoring for immediate ROI.

Module 5Databricks Workflows: Jobs and MLflow Intro (Orchestration, Deployment)

Finalization with Databricks workflows using Jobs Scheduler for end-to-end automations, introduction to MLflow for tracking simple models, exercises on deploying complete Lakehouse pipelines, monitoring of performance and cluster costs, business cases such as scaled sales predictions, collaborative code review and live debugging, conclusion with presentation of the ongoing project to certify production-ready professional skills.