Apache Iceberg Fundamentals
Published 6/2025
Created by Pradeep H C
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 50 Lectures ( 3h 28m ) | Size: 1.47 GB
with snowflake.
What you'll learn
Iceberg fundamentals
Problem with current datawarehouses
Create datalake using snowflake and iceberg
Understand parquet file format
Requirements
No prior experience required
Description
This course is broadly divided into 8 sections,Why Iceberg:This will help you understand the significance of Iceberg and the challenges associated with traditional data warehouse architectures.Iceberg environment setup:We'll set up a Spark environment with Iceberg in GitHub Codespaces. This will serve as a playground where you can run Iceberg commands and experiment hands-on.Parquet file format:We'll dive deep into the Parquet file format to build a strong foundation. Understanding Parquet is essential because Iceberg is built on top of Apache Parquet and leverages its structure for efficient storage and querying.Iceberg features:We'll explore key Iceberg features such as hidden partitioning, schema evolution, and time travel to understand how it addresses common limitations in traditional data lakes.Iceberg concepts:We'll explore concepts like Copy-on-Write (COW), Merge-on-Read (MOR), and snapshot isolation to gain a deeper, more concrete understanding of how Iceberg manages data and ensures consistency.Iceber with snowflake:We'll configure Iceberg with Snowflake and explore how Iceberg integrates with it, helping us understand the foundational concepts of using Iceberg within the Snowflake ecosystem.Datalake with snowflake Iceberg:We'll build a sample data lake using Snowflake Iceberg and also demonstrate how to query Iceberg tables from Spark for cross-platform interoperability.By the end of this course, you'll have a solid understanding of the Iceberg table format-its advantages, use cases, and how to build an efficient data lake using Iceberg.
Who this course is for
Data engineers
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