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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.
10 spots per session maximum — 8 already taken
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30 free minutes with a training advisor — no commitment.
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Don't let this gap widen
Without mastering Great Expectations, intermediate data engineers and ETL developers miss 80% of data validation flaws in pipelines, leading to undetected anomalies that corrupt downstream analytics.
Companies suffer an average $3.2 million in annual losses from data quality incidents, with 60% of breaches and rework tied directly to inadequate validation practices.
Unresolved, these failures erode trust in data assets, stall business decisions, and expose careers to high-stakes accountability.
Every month without expertise compounds risks, turning minor oversights into multimillion-dollar crises.
The Training in Great Expectations 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.
To ensure the quality of the Training in Great Expectations training, Learni provides the following 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.
The assessment of skills acquired during the Training in Great Expectations training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Dive into Great Expectations, a Python framework for data validation ideal for data quality. Start with pip installation. Create a Data Context with gx init. Explore the project structure. Define Sources. Connect SQL databases. Link Pandas files. Create Expectation Suites using suite new. Edit expectation templates. Test expected values. Check column nullity. Apply regex patterns. Run validations with checkpoint run. Analyze results. Resolve failures. Customize error messages. Integrate with Jupyter notebooks. Live interactive demo. Practice on a sales dataset. Create 10 expectations. Validate full schema. Optimize performance. Manage large volumes. Configure automated Profilers. Auto-generate suites. Edit manually. Version with Git. Best practices for Day 1. Real-time Q&A. Hands-on exercises. Constant trainer support. Master GE basics quickly. Boost data quality in pipelines.
Advance to the next level with GE. Integrate into CI/CD pipelines. Configure GitHub Actions. Automate checkpoint runs. Deploy Data Docs. Serve HTML reports. Share insights with the team. Customize renders. Add status badges. Link Slack notifications. Monitor data drifts. Implement Expectation Stores. Use S3 backend. Secure with IAM. Migrate to AWS cloud. Integrate Airflow DAGs. Create custom operators. Schedule regular validations. Manage multi-environments (dev/prod). Unit test expectations with pytest-ge. Mock data sources. Optimize compute costs. Efficient batch processing. Real e-commerce cases. Validate real-time streams. Integrate Kafka streams. Spark DataFrames support. Scale horizontally. Advanced best practices. Advanced failure debugging. Profiling slow suites. Upgrading GE versions. Migrating from v0.18. Future roadmap. In-depth Q&A. Final capstone project. Implement complete pipeline. Present results. Personalized feedback. Access session replays. Downloadable materials. Intermediate certificate issued.
Target audience
Intermediate data engineers. Python data analysts. ETL pipeline developers.
Prerequisites
Intermediate Python. Pandas basics. Data pipeline fundamentals. Git familiarity.
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