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

Certainly! Here are the minimum prerequisites required for students to successfully undertake the DENG-251: Building an Open Data Lakehouse Using Apache Iceberg course:

• General understanding of HDFS (Hadoop Distributed File System) operations and principles.
• Experience with Apache Hive, including knowledge of SQL development within Hive.
• Familiarity with Apache Spark, specifically Spark 3.3.x, for data processing tasks.
• Basic familiarity with data warehouse or data lake technology environments.

These prerequisites ensure that participants have the foundational knowledge needed to fully benefit from the advanced topics covered in the course.

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.