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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.
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The Training Apache Iceberg - Optimizing ACID Transactional 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.
To ensure the quality of the Training Apache Iceberg - Optimizing ACID Transactional Data Lakes 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 Apache Iceberg - Optimizing ACID Transactional Data Lakes training is carried out through:
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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.
Discover advanced ACID transaction mechanisms with Apache Iceberg through hands-on workshops on Spark, implement upsert merges and snapshots for time travel, analyze real cases of failed transaction rollbacks, generate historical version reports on large datasets, integrate freshness constraints for professional pipelines, test fault resilience on simulated clusters, produce production-ready deliverables for enterprise.
Dive into Apache Iceberg performance optimization with exercises on hidden partitioning and file expiration, configure advanced integrations with Hive Metastore and Trino for federated queries, measure 50% latency gains via benchmarks on real data lakes, develop automated maintenance scripts for schema evolution, simulate Kubernetes cluster deployments, analyze compaction and rewriting metrics, validate certified deliverables for data engineering teams.
Target audience
Data engineers, Big Data architects, Spark developers, and data lake managers seeking professional upskilling on scalable table formats
Prerequisites
Advanced proficiency in Spark, complex SQL, Hadoop or data lakes, knowledge of partitioning and Parquet/ORC formats
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