First Hadoop, Now Apache Iceberg – Are We Repeating the Same Mistakes?

In the early 2010s, Hadoop was heralded as the future of big data. Enterprises rushed to build massive Hadoop clusters, anticipating unprecedented analytical power and cost efficiency. Fast forward to today, and Hadoop’s reign has largely diminished, with organizations pivoting to cloud-native architectures, data lakes, and modern analytical engines. Now, Apache Iceberg has taken center stage as the next big thing in open table formats, promising better scalability, schema evolution, and ACID compliance for big data workloads. But are we setting ourselves up for the same challenges that led to Hadoop’s decline?

The Hadoop Hype and Its Pitfalls

Hadoop’s rise was driven by its promise of distributed storage (HDFS) and scalable processing (MapReduce). It seemed like a dream solution for handling massive datasets. However, its limitations became apparent over time:

1. Operational Complexity – Running and maintaining Hadoop clusters required significant expertise. Organizations needed dedicated teams to manage cluster health, tuning, and upgrades.
2. Slow Query Performance – MapReduce, and later Hive, struggled with query latency compared to modern distributed query engines like Apache Presto, Trino, and Snowflake.
3. High Cost of Ownership – While Hadoop was initially pitched as cost-effective, maintaining on-premise infrastructure and handling replication overhead made it expensive.
4. Fragmented Ecosystem – The Hadoop ecosystem became a collection of loosely integrated projects (Pig, Hive, HBase, etc.), leading to dependency hell and operational inefficiencies.
5. Cloud-Native Disruption – Cloud-based data warehouses and lakehouses like Snowflake and Databricks eliminated the need for complex Hadoop clusters, offering easier scalability and better performance.

Hadoop’s demise wasn’t because it was fundamentally flawed—it was the operational model and the shift in technology paradigms that led to its decline.

Enter Apache Iceberg: A New Hope for Data Management?

Apache Iceberg has emerged as a modern open table format designed to address the limitations of traditional big data storage formats like Apache Hive tables. It offers:

• Schema Evolution Without Downtime – Unlike Hive tables, Iceberg allows for schema modifications without breaking existing queries.
• Hidden Partitioning – Improves query performance by avoiding partition column exposure in query logic.
• ACID Compliance – Supports transactions and concurrency without reliance on external locking mechanisms.
• Time Travel and Versioning – Enables users to access historical data versions, enhancing reproducibility.
• Compatibility with Multiple Query Engines – Works with Apache Spark, Trino, Flink, and others, reducing vendor lock-in.

These advantages make Iceberg an attractive option for modern data lakes. However, despite its technical strengths, there are warning signs that we might be heading toward a similar Hadoop-style overcommitment.

The Iceberg Challenges: Are We Repeating History?

1. Operational Complexity Remains a Barrier
   While Iceberg simplifies table management compared to Hive, running it at scale still requires significant expertise. Organizations must manage metadata files, catalog integrations, and version control across multiple engines. Without proper governance, metadata bloat can become a performance bottleneck.
2. Storage and Compute Still Need Careful Optimization
   Just like Hadoop, Iceberg assumes separation of storage and compute, but this doesn’t magically solve performance issues. Query tuning, partitioning strategies, and metadata maintenance are still necessary to avoid costly scans.
3. Fragmented Adoption Across Ecosystems
   Hadoop’s downfall was partly due to a fragmented ecosystem, and Iceberg is heading in a similar direction. While Snowflake, Databricks, and Delta Lake have their own proprietary advantages, Iceberg competes with other table formats like Delta Lake and Apache Hudi. If organizations invest heavily in Iceberg but face shifting industry preferences, they might end up with another technology lock-in issue.
4. The “Open Standard” Debate
   Iceberg positions itself as an open table format, but cloud vendors are building proprietary extensions on top of it. Just like Hadoop vendors (Cloudera, Hortonworks, MapR) created diverging implementations, cloud providers are modifying Iceberg to fit their ecosystem, potentially leading to compatibility issues down the line.
5. Skills Gap and Learning Curve
   A major reason Hadoop failed in many enterprises was the steep learning curve and lack of skilled professionals. Iceberg, while more developer-friendly, still requires data engineers to understand catalog configurations, metadata pruning, and integration with query engines. Organizations rushing into Iceberg without the right expertise may find themselves in a similar skills gap dilemma.

Lessons from Hadoop: Proceed with Caution

1. Avoid Vendor Lock-In Disguised as Open Source

One of Hadoop’s biggest mistakes was how vendors turned an open-source technology into a fragmented, competing landscape. If Iceberg follows the same path, enterprises could face interoperability challenges. Organizations should push for true standardization and ensure Iceberg implementations remain compatible across cloud platforms.

2. Optimize for Business Outcomes, Not Just Technology

Many organizations adopted Hadoop because it was the “hot new thing” rather than aligning it with business goals. Iceberg should be implemented where it truly adds value—such as improving data lake performance and governance—rather than being a default choice without evaluation.

3. Invest in Expertise and Governance

Just like Hadoop needed cluster administrators and MapReduce experts, Iceberg requires knowledgeable teams to manage metadata, storage efficiency, and query optimization. Investing in best practices from the start will prevent costly migrations later.

4. Stay Agile and Avoid Overcommitment

The Hadoop era saw enterprises betting their entire data strategy on it, only to shift to cloud-native architectures later. Organizations should adopt Iceberg incrementally, ensuring that it delivers value before making large-scale investments.

Conclusion

Apache Iceberg is undoubtedly a powerful evolution in open data architectures, addressing many of the problems that plagued Hadoop and Hive-based data lakes. However, if history has taught us anything, it’s that technological superiority alone does not guarantee long-term success. The industry must be mindful of the same pitfalls—operational complexity, vendor fragmentation, and overhyped expectations.

The real question is not whether Iceberg is better than Hadoop—it is whether we have learned from our past mistakes. If we apply those lessons wisely, Iceberg could avoid the fate of its predecessor and truly revolutionize the data landscape.