What Are Apache Iceberg Tables? An Explainer

First open sourced in 2018, Apache Iceberg tables are now a cornerstone technology for petabyte-scale lakehouses at firms including Netflix, which developed the technology. Let’s take a closer look at Apache Iceberg tables to see what they offer and why they’ve caught on so quickly.

What Are Iceberg Tables?

Iceberg tables are an open source table format designed for large-scale data analytics. The Iceberg project, managed by the Apache Foundation, provides an architecture for managing data and doing real-time analytics in petabyte-scale data lakehouses. This is especially helpful in environments where data is frequently updated and queried.

A key feature of Iceberg tables is a metadata layer that resides alongside the data files in a storage repository, such as Amazon S3 or Oracle Cloud Infrastructure (OCI) Object Storage. The metadata keeps track of data files and their locations—a setup that helps Iceberg scale. The tables are held separately from the data itself, which means that changes to the metadata in those tables, be it adding new partitions or updating file locations, do not require rewriting the actual data files.

This architecture also frees up data to be analyzed and updated by different data management frameworks and query engines, such as open source Apache Spark, Trino, or an enterprise cloud service such as Oracle Autonomous AI Database.

Iceberg Tables Explained

Apache Iceberg tables work by maintaining a clear separation between data and the metadata that describes it. Iceberg tables store metadata about a data file’s location, size, partitions, and more. The data itself is stored in files, typically in formats like those found in Apache Parquet, ORC, or Avro, which are optimized for efficient reading and writing in different scenarios. For example, Apache Iceberg comes with tools to help you specify the data to be indexed as Parquet files for batch analytic workloads, or Avro for transaction-focused workloads.

One advantage of Iceberg tables is that metadata is organized into snapshots, which are point-in-time views of the table that remain fixed over time. Snapshots let you view and query the data as it existed any time in the past for which a snapshot exists, an operation known as “time travel.” Snapshots help facilitate auditing, debugging, and data recovery operations.

To manage snapshots and metadata, Iceberg tables contain a metadata file hierarchy. The root metadata file contains a list of snapshots and their corresponding metadata. Each snapshot metadata file includes a manifest list describing a collection of manifest files. These manifest files, in turn, list the data files and provide additional metadata, such as partition information and statistics about the data in each file. When you perform operations such as inserting, updating, or deleting data, Iceberg creates new data files and updates the metadata files to reflect these changes. This is important for data consistency over time because the operation updates the metadata without modifying the existing data files. As a result, the data files themselves stay consistent because information about their table schemas, locations, and updates is recorded in the Iceberg table layer.

The metadata layer also supports partitioning and filtering, which help optimize query performance by reducing the amount of data that needs to be scanned for each query.

Another key advantage of Iceberg tables, especially for complex data lakehouses, is ACID transactions, which help ensure that all operations on the table are atomic, consistent, isolated, and durable. This is a feature common to traditional relational tables in data warehouses.

Iceberg tables can run on the cloud storage of your choice, including Hadoop HFS, Amazon S3, OCI Object Storage, and many other common cloud storage services.

And because Iceberg tables are held separately from the compute power and data management structure that queries them, data sets can scale to any size necessary using lower-cost cloud storage. This includes the petabyte-scale data sets found in extreme analytics architectures, retail and streaming recommendation engines, and some generative AI operations.

Another benefit of Iceberg tables is that they allow you to apply the processing engine of your choice to analysis tasks, whether it’s an open source query engine such as Trino, or a data management enterprise framework such as Oracle AI Database. You can even use several different query engines against the same Iceberg tables.

Data security in Iceberg table architectures is achieved via a mix of native features provided by the Apache Foundation and those provided by cloud storage vendors and the data management systems accessing and querying the Iceberg table. The result will be a combination of data encryption, access controls, data masking, and other data governance features, as well as replication and synchronization capabilities for recovery from failures or attacks.

Benefits of Iceberg Tables

Apache Iceberg can transform how organizations manage massive data sets by bringing the reliability and features of a SQL database to the low-cost scale of a data lake. By introducing ACID transactions, Iceberg allows multiple users to read and write simultaneously without fear of partial results or corruption impacting data integrity. In addition, Iceberg’s intelligent metadata layer can dramatically speed up performance by skipping irrelevant data files. Here’s a rundown of the top benefits.

• ACID transactions: Apache Iceberg supports ACID (atomic, consistent, isolated, durable) transactions, helping ensure that data operations are reliable and consistent, even in concurrent environments.
• Time travel: Iceberg allows you to query data as it existed at any point in time for which a snapshot exists, which is incredibly useful for data versioning, auditing, and debugging.
• Schema evolution: Because Iceberg tables store schema changes in metadata files, you can easily evolve your table schema over time by adding, dropping, or renaming columns—without breaking existing queries or data pipelines. That’s because preexisting queries will simply query the schema that existed at the time they were made.
• Partition evolution: Iceberg supports dynamic partitioning, meaning you can add or change partitions without rewriting the entire data set. This helps optimize query performance and storage efficiency.
• A growing ecosystem: Iceberg integrates with a wide range of data processing frameworks and engines, including Spark, Flink, and Trino. It also integrates with cloud-based data warehouses such as BigQuery, Amazon Redshift, Snowflake, and Oracle AI Database, making it a versatile choice for many data architectures.

Challenges of Using Iceberg Tables

Apache Iceberg tables do pose certain challenges that users should be aware of.

• Iceberg requires a good understanding of its metadata management and the underlying file formats, which can present a steep learning curve for anyone new to the architecture.
• Another challenge is the storage requirements, which can be significant given the need to maintain multiple versions of data for time travel and snapshot features.
• And while Iceberg has broad and growing ecosystem support, not all data processing tools and platforms offer integrations yet, which can limit its usability in certain environments.

Query Iceberg Tables with Oracle Autonomous AI Database

If you’re running mission-critical data warehouse and data lake operations, Oracle Autonomous AI Database can be the simplest way to support all your data types, transactional workloads, and analytics.

You can analyze data within Autonomous AI Database or externally in data lakes or cloud storage using the same tools, processes, and access methods. This covers any cloud object storage, including OCI Object Storage, Amazon S3, Azure Blob Storage, and Google Cloud Storage.

With Autonomous AI Database, your business can consolidate its data onto a single platform that includes integrated machine learning and LLMs, as well as native support for JSON, relational tables, graph and spatial data, vectors, and more. Through integrations, you can also manage any data lake file type, including Parquet, Avro, JSON, CVS, C, and XLSX, as well as any major data lake table structure, including Delta tables or, of course, Iceberg tables.

Iceberg tables are an increasingly popular format that bridges the gap between traditional data lakes and real-time analytics. Created to handle an early petabyte-scale data lake, it has now found a growing user base of similarly large and complex data lakehouses. Look for more cloud service providers to get on board and start helping their clients take full advantage of this timely technological advancement.