Introduction to Apache Iceberg

Apache Iceberg offers organizations a way to build their own Lakehouse, addressing the advantages and challenges of modern data storage solutions.

Introduction

The rise of Data Lakes, easily accessible via cloud platforms such as GCP, Azure, and AWS, has enabled numerous organizations to economically store their unstructured data. However, Data Lakes come with several challenges, including:

• Inconsistent reads when combining batch and streaming data or adding new data.
• Complexity in modifying existing data (e.g., to comply with GDPR).
• Performance issues when managing numerous small files.
• Lack of ACID (Atomicity, Consistency, Isolation, Durability) transaction support.
• Absence of schema enforcement and evolution capabilities.

To mitigate these challenges, Apache Iceberg was developed by Netflix in 2017. This table format provides an extra abstraction layer that supports ACID transactions, time travel, and more, while accommodating various data types and workloads. Its primary goal is to establish a protocol for effectively managing and organizing all files within a table. In addition to Iceberg, other notable open table formats include Hudi and Delta Lake.

For instance, while Apache Iceberg and Delta Lake share many features, Iceberg also supports additional file formats such as ORC and Avro. Conversely, Delta Lake enjoys strong backing from Databricks and the open-source community, offering a broader range of APIs.

Over the years, Netflix has open-sourced Apache Iceberg, and several companies, including Snowflake and Dremio, have invested in its development.

Architecture of Apache Iceberg

Each Apache Iceberg table utilizes a three-layer architecture:

1. Iceberg Catalog: Maps table names to locations and supports atomic operations for updating references as needed.
2. Metadata Layer: Contains all enrichment information regarding the constituent files for each snapshot/transaction, including table schema and partitioning configurations.
3. Data Layer: Associated with the raw data files.

For more in-depth information about Apache Iceberg's architecture, additional resources are available.

Advantages of Apache Iceberg

Key benefits of using Apache Iceberg include:

• Schema Evolution: Seamlessly add, drop, rename, reorder, and update data without unintended side effects, all without rewriting the entire table.
• Time Travel: Quickly switch between different table versions and easily compare changes, enabling rollbacks in case of errors.
• Hidden Partitioning: Automatically manage partitioning to skip unnecessary files, enhancing query performance.

Disadvantages of Apache Iceberg

However, there are also some drawbacks:

• Metadata Overhead: Extensive use of metadata to unlock many capabilities can lead to increased storage requirements.
• Learning Curve: Effective utilization of Apache Iceberg requires solid domain knowledge, although this can be partially mitigated by using managed tools like Dremio or Snowflake.

Building Your Data Lakehouse

Apache Iceberg serves as a foundational element for organizations looking to establish their own Data Lakehouse. The core concept of a Lakehouse is to merge the advantages of Data Lakes and Data Warehouses. Data Lakes provide flexibility in handling both structured and unstructured data with low storage costs, while Data Warehouses excel in query performance and ACID compliance.

To build a Data Lakehouse, five key components are necessary: Storage Layer, File Format, Table Format, Catalog, and Lakehouse Engine. Organizations can choose technologies that best fit their use cases, including computational engines like Spark, Flink, Trino, Impala, or Dremio.

This approach eliminates the need for organizations to maintain two separate systems: a Data Lake for staging raw data and a Data Warehouse for business intelligence operations, ensuring a "Single Source of Truth" that minimizes data duplication and boosts user confidence in the data.

Moreover, to foster a "data as a product" culture within large organizations, the Data Lakehouse can be further designed to align with a Data Mesh strategy, tailored to specific business domains.

Conclusion

In conclusion, Apache Iceberg is an excellent tool for modernizing your data infrastructure, though it is not a one-size-fits-all solution. It is particularly well-suited for large datasets and distributed systems, potentially adding overhead for smaller datasets or real-time operations.

If you wish to learn more about utilizing Apache Spark as a computational engine to power your Data Lakehouse, further information is available in this article.

Chapter 1: Getting Started with Apache Iceberg

In this chapter, we will explore the foundational concepts of Apache Iceberg and how to set it up for your data projects.

This video provides a hands-on introduction to Apache Iceberg, covering the setup and overview essential for beginners.

Chapter 2: Fundamentals of Apache Iceberg

This chapter dives deeper into the core principles and features of Apache Iceberg, helping you understand its functionalities.

This video presents the fundamentals of Apache Iceberg, offering insights into its capabilities and applications.