Apache Iceberg delivers modern data lake features, but adoption depends on existing architecture, team resources and tolerance for migration complexity.
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Published: 11 Aug 2025
Apache Iceberg has become a trendy platform, pitched as a modern table format designed to solve longstanding data lake limitations. Its growing adoption reflects demand for a consistent and flexible data infrastructure.
Features like schema evolution; atomicity, consistency, isolation and durability (ACID) guarantees; and time travel make it a strong candidate for cloud-native analytics, but the tradeoffs around cost, complexity and operational readiness can affect the value proposition.
Is it the remedy vendors claim, or is it just the latest object of hype for organizations to chase after?
Iceberg features and benefits
Apache Iceberg offers technical benefits that appeal to organizations working around the limits of traditional data lakes:
ACID transactions, consistency and schema evolution. These features go beyond the common data lake approach of using managed file formats, like Parquet or ORC, and a catalog like Hive Metastore.
Time travel. Iceberg supports rollback and point-in-time queries for analytics workloads and handling data quality problems.
Vendor agnosticism. Unlike proprietary formats tied to specific stacks, Iceberg enables teams to avoid lock-in across environments.
Major cloud providers and data platform vendors, such as Databricks and Snowflake, now support Iceberg, giving the format an even stronger ecosystem than it had only a few years ago.
Migration costs
Moving an organization to Iceberg comes with significant costs, especially for companies that have existing data infrastructures:
The most immediate cost is the migration itself. Converting existing data assets and pipelines into Iceberg format requires validation and takes up a lot of engineering time that would otherwise be used to support business priorities.
There's also the cost of operational reset. Teams need to be retrained to build comfort in the new operational model; retrain on new workflows; and reestablish documentation, monitoring and troubleshooting procedures.
Iceberg's ecosystem, while growing, still falls short of many well-established data management products. Organizations can still expect gaps, limitations or performance issues not seen in the original use cases during the evaluation process.
Who should migrate?
Apache Iceberg offers clear advantages in certain use cases. Organizations building a data platform from scratch can adopt Iceberg with no migration overhead.
However, those experiencing serious limitations with their current approach, such as consistency, schema evolution or query performance in a data lake setting, may find its features more aligned with their needs. Teams with strong internal data engineering resources or reliable external support are better equipped to overcome the adoption barriers.
Migration diverts engineering resources from more immediate, strategic business problems.
Who should use caution?
For many organizations, a full migration to Iceberg may be inadvisable, such as in the following situations:
If existing data infrastructure meets business requirements and data teams aren't facing critical pain points in consistency, schema evolution, data quality and performance, the traditional "if it ain't broke, don't fix it" argument should prevail.
Migration diverts engineering resources from more immediate, strategic business problems.
For data lakes built around optimizations for a specific vendor, it's possible that the generic approach of Iceberg reduces performance for existing workloads.
A third path
Between the full endorsement and the outright rejection, there is a more measured approach that makes the most sense for most organizations today:
Use Iceberg for new projects and new data ingestion pipelines, while leaving existing infrastructure intact.
Identify individual use cases where Iceberg's features and benefits are clearly superior to the existing approach. Build internal expertise through limited and focused implementations before making broader changes to the data platform.
Recognize that much of the push for organizations to move all their data to Iceberg comes from vendors with their own agendas, whether it is a migration service, cloud resources or new platform licenses they sell.
Organizations should consider Apache Iceberg based on their individual needs and constraints, long-term data strategy, and the pros and cons of migration rather than just following the industry's momentum. Alternatives such as federated search queries, semantic layers across all data sources and unified data governance may offer equal or greater value. Before committing to a broad Iceberg migration, a thorough cost-benefit analysis should be done.
Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.
Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.
