In the early 2010s, Apache Hadoop dominated the big data conversation. Organizations raced to adopt it, seeing it as the cornerstone for scalable, dis

Apache Iceberg: The Hadoop of the Modern Data Stack?

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2024-12-12 17:00:02

In the early 2010s, Apache Hadoop dominated the big data conversation. Organizations raced to adopt it, seeing it as the cornerstone for scalable, distributed storage and processing. Today, Apache Iceberg is emerging as a cornerstone for data lakes and lakehouses in the modern data stack.

And yet, for those who lived through the “Big Data era,” a deeper look reveals striking parallels between Iceberg’s trajectory and the story of Hadoop.

Hadoop’s rise was fueled by an urgent need for distributed file storage and processing, offering a solution to the “big data deluge.” Similarly, Iceberg addresses a core problem in data lakes: managing large, constantly evolving datasets with ACID compliance and schema evolution.

However, technology adoption often operates on a pendulum — the promise of solving a significant pain point drives rapid adoption, even when operational readiness lags. Hadoop’s meteoric rise led many organizations to implement it without understanding its complexities, often resulting in underutilized clusters or over-engineered architectures. Iceberg is walking a similar path. The ability to unify batch and streaming workloads, combined with features like schema evolution, has made it an alluring choice. Yet, Iceberg’s adoption often outpaces organizations’ abilities to operationalize it effectively.

The lesson here is timeless: Adoption should be driven by alignment with organizational maturity. Jumping on the Iceberg bandwagon without a clear data strategy can lead to technical debt and unmet expectations. For example, a fintech company might adopt Iceberg for ACID guarantees in their fraud detection pipelines but grapple with compaction strategies because their streaming setup generates too many small files.

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