Apache Iceberg Fundamentals: Course #1 - Introduction

Welcome to the first installment of our Apache Iceberg 101 Course! This comprehensive series of lessons will introduce you to the Apache Iceberg data lakehouse table format and equip you with the knowledge necessary to effectively utilize it.

Data lakehouses are a powerful tool for storing and managing data, combining advantages from both data warehouses and data lakes. Data warehouses are designed for structured data, whereas data lakes focus on unstructured data. Data lakehouses, however, provide a unified platform that can handle both structured and unstructured data while maintaining high performance.

Apache Iceberg is an open-source table format that enables users to store large amounts of data in a single table. It is built on top of existing frameworks such as Apache Avro, Parquet, and ORC, which provide reliability and scalability. Additionally, it provides several features such as versioning, schema enforcement, and query optimization that make it particularly well-suited for use in a data lakehouse environment.

In this course series we will explore the core concepts of Apache Iceberg in depth. We’ll cover topics such as its architecture and design principles; its compatibility with existing frameworks; and how to use it for optimal performance when working with large amounts of data. By the end of this course series you should have a solid understanding of Apache Iceberg and be able to confidently use it in your own projects.

At Dremio we specialize in providing software solutions that allow users to work with their own data lakehouses quickly and easily. Our Subsurface product is specifically designed to help users create powerful Data Lakehouse environments using Apache Iceberg tables for storage. With Subsurface you can easily manage your tables through an intuitive graphical user interface (GUI).

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Hi Alex, do we have join functionality in iceberg sql commands? Basically I want to read data from 2 different tables joined on a single column value.