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 Iceberg - Managing Massive Data Lakes training are: Solid experience in Spark or Hive, advanced SQL and distributed storage like S3 or HDFS. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Apache Iceberg - Managing Massive Data Lakes training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Apache Iceberg - Managing Massive Data ... | 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 Iceberg - Managing Massive Data Lakes 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 Iceberg - Managing Massive Data Lakes 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 Iceberg - Managing Massive Data Lakes 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

Master Apache Iceberg for scalable professional data lakes

Develop Iceberg tables with schema evolution in enterprise settings

Implement ACID transactions and time travel for certifiable skills

Optimize query performance on massive datasets

Integrate Iceberg with Spark, Trino and Flink in production environments

Design robust data pipelines with automated maintenance

Deploy certifiable lakehouse solutions for the enterprise

Training program

Module 1Advanced Fundamentals of Apache Iceberg and Lakehouse Architecture

Review of Iceberg concepts for modern data lakes. Installation and configuration on Spark. Creation of Iceberg tables with metadata. Management of dynamic partitions. Hands-on practice on real datasets. Professional exercises on schema evolution. Start of the red-thread project: simulation of an enterprise data lake. Integration with S3. Verification of initial snapshots. Basic time travel. Real-world versioning cases. Manifest optimization. Support for Parquet/ORC formats. Preparation of distributed environments.

Module 2Data Management and Transactions in Apache Iceberg

Implementation of ACID transactions in Apache Iceberg. Merge, update and delete on massive tables. Management of schema evolution without downtime. Practical exercise: migration from Hive to Iceberg. Red-thread project: streaming data ingestion. Control of read isolation. Branching and tagging for testing. Resolution of concurrency conflicts. Implementation of manual compaction. Analysis of metadata with Iceberg CLI. Enterprise case: audit of existing tables. Integration with Kafka for CDC. Validation of constraints. Performance tuning for DML operations.

Module 3Advanced Integrations of Apache Iceberg with Big Data Ecosystems

Connecting Apache Iceberg to Spark, Trino and Flink. Federated queries across multiple engines. Configuration of catalogs like Hive Metastore or REST. Red-thread project: cross-engine queries. Optimization of pushdown predicates. Exercise: joins on terabyte datasets. Streaming with Spark Structured Streaming. Integration with AWS Glue or Azure Purview. Security: encryption and Ranger. Monitoring with Prometheus. Professional cases: legacy data warehouse migration. Performance benchmarks. Fine-tuning of readers/writers. Management of hidden partitioning formats.

Module 4Optimization and Production Deployment of Apache Iceberg

Apache Iceberg optimization strategies in production. Automatic compaction and manifest rewrites. Tuning file sizes for storage costs. Red-thread project: full pipeline deployment. Monitoring and alerting on metadata. Horizontal scaling with Kubernetes. CI/CD for Iceberg tables. Exercise: diagnosis of slow performance. Enterprise best practices: backup/restore. Integration with Databricks or EMR. Cloud cost audit. Project defense: production lakehouse simulation. Final quiz and competency certification. Post-training resources.

Evaluation method

Multiple-choice quiz to validate learning outcomes on Apache Iceberg at the end of the training
Continuous assessment through professional exercises and a red-thread project
Defense of the lakehouse pipeline before the expert trainer