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 Great Expectations - Ensuring Data Quality in Production training are: Advanced mastery of Python, SQL, ETL pipelines (Airflow, Spark), and datastore management. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Great Expectations - Ensuring Data Quality in Production training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Great Expectations - Ensuring Data Qual... | 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 Great Expectations - Ensuring Data Quality in Production 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 Great Expectations - Ensuring Data Quality in Production 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 Great Expectations - Ensuring Data Quality in Production 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 Great Expectations 2026 to validate professional data pipelines

Develop custom and scalable expectations for enterprise use

Implement CI/CD integration with Great Expectations for secure deployments

Design advanced checkpoints and real-time monitoring

Optimize data quality in hybrid cloud environments

Deploy certified automated test suites for data teams

Analyze and correct data drifts using expert tools

Training program

Module 1Great Expectations 2026: Advanced Configuration and Expert Architecture (Datastores, Profilers)

Dive into the installation and expert configuration of Great Expectations 2026 on cloud environments like AWS S3 or Snowflake, configure multiple datastores with pandas and Spark, launch automated profilers on massive datasets to detect schemas and anomalies, perform practical exercises on a real ETL pipeline, produce your first validation suite with generated documentation, and optimize performance for terabyte-scale volumes in enterprise settings.

Module 2Great Expectations: Custom Expectations and Complex Validations (Regex, ML Metrics)

Create custom expectations using advanced Python, integrate regex validations for structured formats, implement ML metrics like drift detection via scikit-learn, test on real-world data quality cases in finance and healthcare, develop reusable scripts for data teams, perform interactive runs with Jupyter for rapid iteration, and generate detailed HTML reports proving professional data compliance.

Module 3Great Expectations: Pipeline Integration and CI/CD (Airflow, GitHub Actions)

Integrate Great Expectations 2026 into orchestrators like Airflow or Dagster via custom DAGs, configure batch and streaming checkpoints for continuous monitoring, automate tests in GitHub Actions or Jenkins with success badges, apply to an enterprise red thread project with realistic simulated data, debug validation failures live, and deploy scalable runtimes on Kubernetes for high availability in production.

Module 4Great Expectations: Production Monitoring, Scalability, and Data Governance (Alerts, Audits)

Set up advanced monitoring with Slack/Teams alerts on data drifts, analyze logs and metrics via interactive Data Docs, scale on Spark clusters for petabyte data, audit GDPR compliance with privacy-focused expectations, finalize the red thread project with a custom dashboard, export certified deliverables for data governance committees, and prepare best practices for enterprise rollout with measurable ROI.

Evaluation method

Expert multiple-choice quiz to assess learning outcomes at the end of the training
Continuous evaluation through advanced exercises and debugging
Defense of a complete data pipeline with Great Expectations