Apache Iceberg on AWS, Azure, Databricks and Snowflake

As organizations strive for greater data flexibility and performance, apache iceberg has emerged as the industry standard for open table formats. Unlike traditional data lakes that struggle with schema evolution and ACID compliance, Iceberg provides a high-performance table layer that allows multiple engines to read and write to the same data simultaneously. This solves the problem of data silos and vendor lock-in, enabling a true lakehouse architecture where your data remains portable across any cloud or tool.

At its core, Apache Iceberg acts as a management layer sitting between your physical data files (usually Parquet) and your compute engines. It tracks the state of the table through metadata files, allowing for features like time travel, partition evolution, and transactional integrity that were previously only possible in proprietary data warehouses.

Why Apache Iceberg is the Foundation of the Modern Lakehouse

The shift toward open standards is driven by the need for interoperability. In the past, choosing a data platform often meant committing to a specific storage format that was difficult to move. Apache Iceberg breaks this cycle. Because it is an open-source project with broad community support, it allows an organization to use Databricks for ETL, Snowflake for BI, and AWS Athena for ad-hoc queries, all pointing to the exact same files.

This apache iceberg guide highlights how the format addresses three critical pain points:

1. Schema Evolution: You can add, drop, or rename columns without rewriting the entire table.
2. Hidden Partitioning: Users do not need to know how the data is partitioned to write efficient queries; Iceberg handles the relationship between the logical columns and physical layout.
3. Snapshot Isolation: Readers always see a consistent view of the data, even while writes are occurring, preventing corrupted results during high-frequency ingestion.

Implementing Apache Iceberg Across Cloud Providers

Every major cloud provider and data platform has integrated Iceberg support, but the implementation details vary. Choosing the right architecture depends on your existing ecosystem and performance requirements.

AWS: The Glue Catalog and S3

On AWS, Iceberg tables are typically stored in Amazon S3 and cataloged in AWS Glue. AWS has made Iceberg a first-class citizen, offering native support in Amazon Athena, EMR, and Redshift Spectrum. For teams using AWS, Iceberg replaces the traditional Hive Metastore approach, providing faster metadata operations and better scalability for petabyte-scale datasets.

Azure: OneLake and Fabric

Microsoft has integrated Iceberg support into Azure Data Lake Storage (ADLS) Gen2 and Synapse Analytics. With the launch of Microsoft Fabric, Iceberg is often used alongside Delta Lake, with Shortcuts allowing users to query Iceberg tables directly within the Fabric ecosystem. Azure users benefit from the tight integration with Power BI, which can read Iceberg metadata to optimize report performance.

Databricks: UniForm Compatibility

While Databricks historically championed Delta Lake, they now support Iceberg through Universal Format (UniForm). UniForm allows Databricks to generate Iceberg metadata automatically when writing Delta tables. This means you can leverage the performance optimizations of the Databricks engine while ensuring your data is accessible to any tool that speaks Iceberg.

Snowflake: Polaris Catalog and Iceberg Tables

Snowflake has embraced Iceberg to allow customers to manage data stored outside of Snowflake's proprietary storage. You can create "Iceberg Tables" in Snowflake that point to your own S3 or Azure containers. Recently, Snowflake launched the Polaris Catalog, an open-source catalog for Iceberg that further enhances interoperability by providing a centralized point of truth for multiple engines.

Technical Comparison: Iceberg vs. Alternatives

Step-by-Step: Setting Up an Apache Iceberg Table

Follow these steps to deploy your first Iceberg table using Spark and a REST catalog, which is the most portable configuration.

1. Define the Catalog Configuration: Configure your Spark session to use the Iceberg SparkCatalog. Point it to your metadata storage (like AWS Glue or a REST server).
2. Create the Table: Use standard SQL syntax to define your table. Example: CREATE TABLE prod.db.events (id bigint, data string, event_time timestamp) USING iceberg PARTITIONED BY (days(event_time)).
3. Ingest Data: Use INSERT INTO or MERGE INTO statements. Iceberg will handle the ACID transactions and ensure the metadata is updated atomically.
4. Evolve the Schema: If your requirements change, run ALTER TABLE prod.db.events RENAME COLUMN data TO payload. This operation is instantaneous as it only updates metadata.
5. Expire Snapshots: To manage storage costs, regularly run the expireSnapshots procedure to remove old data files that are no longer needed for time travel.

Common Pitfalls to Avoid

Implementing an apache iceberg architecture is not without challenges. One common mistake is neglecting maintenance. While Iceberg manages files efficiently, small file problems can still occur during frequent streaming writes. You must schedule regular "compaction" jobs to rewrite small files into larger, more efficient ones.

Another pitfall is inconsistent catalog access. If one team writes to a table using the Glue Catalog and another tries to read it using a different metadata service without synchronization, they will see inconsistent data. Standardizing on a single catalog like Snowflake Polaris or AWS Glue is essential for a unified data strategy.

Key Performance Indicators (KPIs) for Iceberg Success

To measure the health and efficiency of your Iceberg implementation, track these metrics:

• Query Latency: Compare the speed of Iceberg queries against previous Hive or raw Parquet queries.
• Metadata Size: Monitor the growth of your metadata folder; excessive snapshots can slow down planning time.
• File Count: Track the ratio of data files to table size to identify when compaction is needed.
• Storage Savings: Measure the impact of Iceberg’s optimized file pruning on your cloud storage bill.

Frequently Asked Questions

Is Apache Iceberg better than Delta Lake?

Neither is objectively better; the choice depends on your stack. Iceberg offers superior hidden partitioning and is more engine-agnostic, making it ideal for multi-vendor environments. Delta Lake offers deeper integration within the Databricks ecosystem.

Does Snowflake support Apache Iceberg natively?

Yes, Snowflake supports Iceberg tables where the data resides in your own cloud storage. This allows you to use Snowflake's query engine without moving your data into Snowflake’s internal managed storage.

How does Iceberg handle data privacy like GDPR?

Iceberg supports DELETE and UPDATE commands with row-level granularity. This makes it much easier to perform "right to be forgotten" requests by deleting specific user records without needing to rewrite entire datasets manually.

Can I migrate existing Parquet tables to Iceberg?

Yes, Iceberg provides a "migrate" and "add_files" procedure that allows you to convert existing Parquet tables into Iceberg tables in place, without moving the underlying data files.

How Izan Technologies Can Help

Building a scalable data architecture requires more than just choosing the right format. At Izan Technologies, we specialize in helping enterprises deploy robust lakehouse environments using apache iceberg across AWS, Azure, and Databricks. As a certified partner for these platforms, we ensure your data strategy is future-proof and cost-effective.

When you partner with Izan Technologies, you maintain 100% ownership of the Intellectual Property (IP) we build. Whether it is custom ETL pipelines, catalog synchronization, or performance tuning, the code belongs to your organization.

If you are ready to eliminate data silos and embrace a truly open table format, explore our capabilities. You can view our expertise in action by visiting /demos to see a live DataStudio demo of how we integrate these technologies for our clients.