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Apache Iceberg: From Zero to Production Data Lakehouse

IntermediateSkill Level
Updated 06/2026
Build production-ready Apache Iceberg lakehouses: model, migrate, and maintain tables at scale.
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SnowflakeData Engineering
4 hr
19 videos
44 Exercises
2,200 XP
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Course Description

Data lakes promised cheap, flexible storage at scale, but they left engineers fighting broken queries, painful schema changes, and no reliable way to update data. Apache Iceberg fixes that by bringing database-grade reliability to the data lake. In this course, you'll build production-ready Iceberg lakehouses from the ground up: model and partition data for fast queries, migrate existing tables without downtime, evolve schemas and partitions safely with Git-like workflows, and keep everything performant through compaction, snapshot management, and smart write strategies. By the end, you'll be ready to design and operate a modern lakehouse with confidence.

What you'll learn

  • Differentiate Apache Iceberg's open table format and metadata architecture from traditional data lakes, and explain how it delivers database-style reliability and file skipping.
  • Determine how to model and partition Iceberg tables with hidden partitioning so queries prune the files they don't need.
  • Identify the right strategy for migrating existing Parquet, ORC, Hive, or database data into Iceberg without rewriting everything.
  • Assess when to use Write-Audit-Publish, branching, and tagging to evolve schemas and partitions safely on live tables.
  • Evaluate write strategies, concurrency, and maintenance operations such as compaction and snapshot expiration for keeping production Iceberg tables fast at scale.

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Prerequisites

Introduction to Modern Data Engineering with Snowflake
1

Apache Iceberg Fundamentals

In this chapter, learn how to set up an Apache Iceberg lakehouse and model data with hidden partitioning so your queries skip the files they don't need.
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2

Taking Advantage of Apache Iceberg Tables

In this chapter, learn how to migrate existing data into Iceberg and safely evolve schemas and partitions using Git-like workflows such as Write-Audit-Publish, branching, and tagging.
Start Chapter
3

Operating and Optimizing Apache Iceberg

In this final chapter, learn how to keep production Iceberg tables fast at scale through smart write strategies, safe concurrency, and maintenance operations like compaction and snapshot expiration.
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Apache Iceberg: From Zero to Production Data Lakehouse
Course
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