Apache Iceberg + Snowflake: End-to-End Data Lake Guide

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.