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DENG-251: Building an Open Data Lakehouse Using Apache Iceberg( Building an Open Data Lakehouse Using Apache Iceberg) Course Overview

DENG-251: Building an Open Data Lakehouse Using Apache Iceberg( Building an Open Data Lakehouse Using Apache Iceberg) Course Overview

Embark on a transformative journey with our DENG-251: Building an Open Data Lakehouse Using Apache Iceberg course. Over three days of instructor-led training, you'll dive deep into Apache Iceberg's architecture, mastering table formats that scale to petabytes. You'll gain essential skills like managing table snapshots, implementing schema evolution, and mastering data migration strategies from Hive to Iceberg. This intermediate-level course, best suited for Data Engineers and Hive SQL Developers, requires prior knowledge of HDFS, Hive, and Spark. Whether you're on the Cloudera Data Platform in a private or public cloud, enhance your data warehousing capabilities with cutting-edge practices in data lakehouse design.

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  • Live Training (Duration : 32 Hours)
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Target Audience for DENG-251: Building an Open Data Lakehouse Using Apache Iceberg( Building an Open Data Lakehouse Using Apache Iceberg)

"DENG-251: Building an Open Data Lakehouse Using Apache Iceberg" is designed for IT professionals skilled in Cloudera ecosystems, Hive, and Spark, aiming to leverage Apache Iceberg for advanced data management and analytics.


  • Data Engineers


  • Hive SQL Developers


  • Kafka Streaming Engineers


  • Data Scientists


  • Cloudera Data Platform (CDP) Administrators


  • IT professionals in analytics and data management sectors


  • Technical team leads and project managers involved in data projects


  • Consultants and architects designing data solutions on Cloudera platforms




Learning Objectives - What you will Learn in this DENG-251: Building an Open Data Lakehouse Using Apache Iceberg( Building an Open Data Lakehouse Using Apache Iceberg)?

Introduction to Course Learning Outcomes: In DENG-251, participants will learn to design and manage large-scale analytics using Apache Iceberg, mastering advanced operations and migrations for optimized data lakehouse architecture.

Learning Objectives and Outcomes:

  • Understand the benefits and functionalities of Iceberg, including snapshots.
  • Configure and manage both external and managed tables.
  • Implement optimized data management strategies using copy-on-write and merge-on-read.
  • Utilize rollbacks and time travel features for data version control.
  • Navigate schema evolution and partition management effectively.
  • Master the creation and merging of table branches and tags using the Write-Audit-Publish (WAP) method.
  • Efficiently conduct table maintenance tasks and address common data migration challenges.
  • Gain proficiency in migrating tables from Hive to Iceberg with minimal disruption.
  • Deepen understanding of Iceberg’s logical architecture and metadata layers.
  • Learn to handle hidden partitions and utilize Iceberg’s advanced catalog features for robust data management.

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