Mastering Apache Iceberg for Scalable Data Lake Management

As data volumes continue to grow exponentially, organizations are moving toward more open, flexible, and scalable data lake architectures. But what exactly is a data lake architecture?

A data lake architecture is a system for storing vast amounts of raw data in its native format—structured, semi-structured, or unstructured—until it’s needed for analytics. Unlike traditional databases, data lakes can handle everything from real-time streams to batch files, making them ideal for big data and machine learning workflows. However, traditional data lake solutions often come with their own set of challenges—like slow performance, difficulty managing schema changes, and tight coupling with specific processing engines. That’s where Apache Iceberg comes in.

Apache Iceberg is a modern, open-source table format designed to overcome many of the common pain points associated with traditional data lakes. With its robust metadata handling, built-in support for schema evolution, and compatibility with multiple processing engines like Apache Spark and Flink, Iceberg is transforming the way teams manage and analyze big data. In this guide, we’ll take a closer look at what Apache Iceberg is, explore its key features and architecture, and share some practical tips for implementing it in your environment. We’ll also answer common questions about handling schema evolution and integrating Iceberg with engines like Apache Spark—so you can get up and running smoothly.

• Familiarity with Apache Spark and Hive or similar distributed computing platforms.
• Understanding data lake architecture. This includes knowledge of file formats (such as Parquet and ORC), storage systems (e.g., HDFS and S3), and partitioning strategies.
• Ability to write SQL queries and create tables while performing operations such as INSERT, UPDATE, and ALTER.
• Ensure Apache Spark 3.x is installed and operational alongside your Spark version’s appropriate Iceberg runtime package.
• Configure Hive Metastore, AWS Glue system, or a compatible catalog to manage Iceberg table metadata.

Apache Iceberg is an open-source table format created for managing large analytic datasets. The Apache Software Foundation developed it to manage the challenges of data storage and querying massive volumes of data within data lakes. The team behind Iceberg aimed to create a more reliable, consistent, and efficient way to manage table metadata, track file location, and manage schema changes. This is particularly important as more organizations use cloud data lakes to manage massive datasets.

The fundamental features of Apache Iceberg highlight why it stands as the preferred standard for managing big data.

Schema Evolution Iceberg supports table schema evolution by allowing columns to be added, removed, renamed, or reordered without altering the existing data files. It achieves this by assigning a unique ID to each column and tracking schema changes in the metadata.

Partitioning and Partition Evolution Iceberg tables support partitioning through one or multiple keys (like date, category, etc.) to improve query performance. Iceberg offers exclusive support for hidden partitioning and partition evolution. Hidden partitioning allows tables to track partition values internally. This enables query engines to perform automatic partition pruning without user intervention to add partition filters.

Format-agnostic Iceberg works with various file formats despite its common association with Parquet to support different data ingestion strategies.

ACID Transactions Iceberg ensures transactional safety during data lake operations which provides ACID properties commonly found in data warehouses and advanced transactional systems.

Time Travel and Data Versioning Each iceberg snapshot is retained until you actively choose to expire it. Time-travel queries enable access to table data from any prior snapshot or timestamp. For example, you might run the command,

*SELECT * FROM my_table* *FOR TIMESTAMP AS OF ‘2025-01-01 00:00:00*’

to view your data from the beginning of 2025.

Performance Optimizations
Iceberg is built for big data performance. The metadata tree which contains manifests enables Iceberg to avoid full table scans by pruning unnecessary files and partitions for a specific query.

At a high level, the Apache Iceberg architecture consists of several key components:

Metadata Layer: This layer consists of several files that maintain comprehensive information about the table’s structure and state:​

• Metadata File (metadata.json): This keeps track of the current schema, partition specifications, snapshots, and references to the manifest list for the most recent snapshot. ​
• Manifest List: It points to the relevant manifest files, offering a reliable snapshot of the table at any time.
• Manifest Files: Data file listings with statistical information such as record counts, column min/max values, and metadata for each file.

Data Layer: This layer comprises the actual data files. It stores data using columnar formats such as Parquet, ORC, and Avro.

When a query is executed on an Iceberg table, the system follows these steps:​

• Metadata Retrieval: The query engine retrieves the current metadata.json file from the catalog.
• Snapshot Identification: It looks at the latest snapshot or a specific one if we use the time-travel features.
• Manifest Pruning: The query engine scans the manifest list to remove irrelevant manifest files using query predicates.
• Data Access: The system reads necessary data files specified by the relevant manifest files and applies filters to extract the required data.

Comparison: Apache Iceberg vs. Hudi vs. Delta Lake

Iceberg is often compared to other open table formats like Apache Hudi and Delta Lake. All three aim to bring ACID transactions and reliability to data lakes but differ in their approach and features:

Key Differences Summary:

• Iceberg: It emphasizes independence from metadata, allows for robust schema and partition evolution, and offers impressive pruning via statistics. It’s adaptable across different engines.
• Hudi: Offers mature support for Merge-on-Read, making it ideal for fast updates and upserts. It also supports built-in indexing capabilities. However, it can be a bit complex to set up.
• Delta Lake: It features strong integration with Spark (especially when using Databricks), and operates on a straightforward transaction log system. The open-source version does not support some advanced features of the Databricks runtime, like Partition evolution and advanced Z-Ordering.

Choosing between Iceberg, Hudi, or Delta Lake should be based on particular use cases, your current technological environment, and priority features (e.g., update frequency vs. schema flexibility).

We will show how to use Apache Iceberg with Spark (via Spark SQL) to create and handle Iceberg tables. Apache Iceberg enables seamless integration with Spark through its DataSource V2 API. This allows users to run standard Spark SQL commands to manage Iceberg tables after appropriate configuration.

Prerequisites for Apache Iceberg

• Make sure you have Spark 3.x: Your first step should be to verify that Spark 3.x is installed on your computer.
• Iceberg Spark Runtime Package: Get the Iceberg connector JAR file that aligns with your Spark and Iceberg version numbers.
• Include the JAR in Spark: Include the Iceberg connector JAR in your classpath when starting Spark(through spark-shell or spark-sql). To include other packages or dependencies, you can use the —packages option.

Use this command to start Spark-SQL with Iceberg version 1.2.1 and Spark 3.3.

spark-sql –packages org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.2.1

–packages org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.2.1: This option indicates the Maven coordinates for the Iceberg runtime package that’s compatible with Spark 3.3 and Scala 2.12, version 1.2.1. You can find this package in the Maven Central Repository.

Step 1: Configure the Spark Catalog for Iceberg

To configure Spark to use Iceberg’s catalog, you can configure it in spark-defaults.conf or through command-line –conf options. Here’s an example:

spark-sql –packages org.apache.iceberg:iceberg-spark-runtime-3.3_2.12:1.2.1 –conf spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog –conf spark.sql.catalog.local.type=hadoop –conf spark.sql.catalog.local.warehouse=/tmp/iceberg_warehouse –conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions

• spark.sql.catalog.local: This establishes a Spark catalog named local which uses Iceberg’s SparkCatalog.
• spark.sql.catalog.local.type=hadoop: This setting instructs Iceberg to handle metadata within a filesystem compatible with Hadoop.
• spark.sql.catalog.local.warehouse: Specifies the warehouse directory (e.g., /tmp/iceberg_warehouse).
• spark.sql.extensions: This enables some Iceberg-specific SQL extensions (e.g., MERGE, DELETE).

Tables created under the local catalog will be saved in the directory defined as your warehouse path. Note: You must configure the catalog first; otherwise, Spark might just create a Hive table by default.

Step 2: Create an Iceberg Table and Insert Data

Let’s proceed by creating a sample Iceberg table and inserting some records:

CREATE TABLE local.learning.employee ( id INT, name STRING, age INT ) USING iceberg; — Insert records into the table INSERT INTO local.learning.employee VALUES (1, ‘Adrien’, 29), (2, ‘Patrick’, 35), (3, ‘Paul’, 41);

Through the above commands, we have created an employee table in the learning namespace of our local catalog. The USING iceberg clause tells Spark to use the Iceberg data source, which is essential for managing the table properly. All the data and metadata for this table will be stored in the specified warehouse directory as an Iceberg directory.

Step 3: Perform Updates and Schema Evolution

Suppose we want to update one of our employee records (changing Patrick’s name) and also add a new column to track email addresses. We can achieve this using SQL UPDATE and ALTER TABLE statements in Iceberg:

— Update Patrick’s name to Flobert UPDATE local.learning.employee SET name = ‘Flobert’ WHERE id = 2; — Alter the table to add a new email column ALTER TABLE local.learning.employee ADD COLUMNS (email STRING); — Insert a new record that includes the new email field INSERT INTO local.learning.employee VALUES (4, ‘David’, 30, ‘david@company.com’)

In the background:

• UPDATE operation: It generates a new data file for the partition with Patrick’s updated name. It also marked the old file section as removed in the metadata.
• Alter Table Operation: The ALTER TABLE ADD COLUMNS updated the table’s schema in the metadata and assigned a new ID to the email column without modifying the existing files.
• Insert Operation: This operation inserts David’s record, which includes the new email field.

This approach allows us to handle schema changes efficiently and minimizes the need for costly table rewrites.

Iceberg demonstrates exceptional performance through its efficient management of metadata:

• Manifest Files: Instead of one massive file with all data, Iceberg splits the metadata into smaller manifest files, each describing subsets of data.
• Parallel Operations: This design enables queries to skip over entire metadata files and read only the relevant partitions or subsets.
• Partition Pruning: Iceberg keeps track of min/max statistics at the file level, allowing it to prune partitions or data files that don’t fit the query conditions.

This strategy becomes essential when working in environments that contain millions of data files. It eliminates the need to scan large metadata files and avoids complex rewriting processes each time new data arrives.

Organizations today operate across multiple cloud platforms by using services from AWS, Azure, and Google Cloud. Apache Iceberg operates independently of object storage systems. It will allow you to:

• Store data to object storage services(such as S3, GCS, and ADLS) provided by major cloud platforms.
• Handle table metadata through centralized Hive Metastore systems, AWS Glue catalogs, or other catalogs.
• Execute Spark or Presto in any cloud platform of your choice to access the same Iceberg tables for reading and writing operations.

This flexibility helps you avoid being locked into one provider while taking advantage of each cloud’s strengths—better compute discounts, advanced AI services, or compliance features based on your region.

The robust nature of complex schema evolution brings some challenges. The following table presents the key considerations for Apache Iceberg schema evolution.

Teams should implement updates incrementally, conduct comprehensive testing across all consuming engines, and use Iceberg’s metadata history to track changes.

This section presents typical issues faced during Apache Iceberg integration with Spark or Hive. You can review each of them and consult official documentation whenever necessary:

Frequent checks of integration systems and logs allow early detection of conflicts. This will help to maintain smooth operations while minimizing downtime.

What is Apache Iceberg?

Apache Iceberg is an open-source table format designed to manage large-scale analytic data sets. Apache Iceberg is like a smart organizer for big data stored in data lakes.

When you store huge amounts of data in files (like Parquet or ORC) in cloud storage or HDFS, it can get messy and hard to manage—especially when data keeps changing or growing. Iceberg helps organize this data in a structured, efficient, and reliable way so that tools like Apache Spark, Flink, and Trino can work with it faster and more accurately.

Think of Iceberg as a table format, kind of like how Excel organizes data in rows and columns. But unlike traditional formats, Iceberg keeps track of metadata (data about the data), supports schema changes easily, and allows for features like time travel (seeing past versions of data), incremental reads, and ACID transactions (to make sure data stays consistent).

How does Apache Iceberg improve data lake performance?

Iceberg improves performance by storing metadata in more compact manifests that allow effective partition pruning. The system optimizes performance by restricting queries to relevant manifests and data files, which minimizes I/O overhead. Snapshots enable consistent data access during reads and writes while preventing concurrency-related issues.

How does Iceberg handle schema evolution?
Schema evolution is version-based. Whenever you modify a schema, Iceberg generates a new snapshot pointing to the updated schema. Older snapshots stay unchanged, so queries against previous data remain valid without needing to rewrite any historical files.

What’s the difference between Apache Iceberg, Delta Lake, and Hudi?

• Iceberg emphasizes engine-agnostic metadata management and optimizing performance for large-scale data.
• Delta Lake is focused on ACID transactions, works closely with Databricks, and allows time-travel queries.
• Hudi is designed to process data incrementally and supports near real-time analytics with its advanced upsert features.

Can I use Apache Iceberg with Apache Spark?

Absolutely! Apache Iceberg can integrate with Spark, making it easy to read, write, and manage Iceberg tables through Spark SQL or the DataFrame API.

What are the key benefits of using Apache Iceberg in data lakes?

Some of the main benefits of using Apache Iceberg in data lakes include support for ACID transactions, lightweight yet powerful metadata management, snapshot isolation, smooth schema evolution, and compatibility across different engines.

What are the most common use cases for Apache Iceberg?

Apache Iceberg can handle various data management tasks. This includes batch analytics, incremental data processing, offloading data warehousing tasks, powering machine learning feature stores, and managing IoT data.

Apache Iceberg is becoming a go-to technology for organizations trying to tackle the challenges coming with modern data lakes. Its open and scalable design, which works with different engines, gives data teams the freedom to manage schema changes, deal with performance issues, and maintain consistency.

Apache Iceberg establishes the foundation for high-performance data lakes in single-cloud and multi-cloud environments. Using the best practices and troubleshooting tips from this guide will help you maximize Iceberg’s capabilities for your data analytics needs.

To deepen your understanding of Apache-based technologies and how they can integrate into various infrastructures, take a look at the following articles:

While these resources primarily focus on the Apache HTTP server, the underlying concepts of open-source collaboration, configuration management, and system troubleshooting can be applied to your work with Apache Iceberg.