The blog post argues Apache Iceberg is poised to become a foundational technology in the modern data stack, similar to how Hadoop was for the previous generation. Iceberg provides a robust, open table format that addresses many shortcomings of directly querying data lake files. Its features, including schema evolution, hidden partitioning, and time travel, enable reliable and performant data analysis across various engines like Spark, Trino, and Flink. This standardization simplifies data management and facilitates better data governance, potentially unifying the currently fragmented modern data stack. Just as Hadoop provided a base layer for big data processing, Iceberg aims to be the underlying table format that different data tools can build upon.
The blog post "Apache Iceberg: The Hadoop of the Modern Data Stack?" explores the potential of Apache Iceberg to become a foundational technology within the evolving modern data stack, much like Hadoop was in the previous era of big data. The author draws parallels between the two technologies, highlighting how both address the challenges of managing large datasets but with differing approaches and philosophies tailored to their respective technological landscapes.
Hadoop, the author explains, rose to prominence by providing a distributed storage and processing framework suitable for the then-emerging needs of handling massive volumes of unstructured data. It became the bedrock for a complex ecosystem of tools built around its core functionalities of HDFS and MapReduce. However, this ecosystem, while powerful, became notorious for its operational complexity and steep learning curve.
Apache Iceberg, in contrast, focuses on providing a robust table format and metadata layer that sits atop existing storage systems like cloud object storage or even HDFS. This architectural choice allows Iceberg to leverage the scalability and cost-effectiveness of modern cloud storage while simultaneously addressing the limitations of traditional data lakes. The author argues that this approach offers several key advantages, including ACID properties for data reliability, schema evolution for adaptability, and time travel capabilities for data versioning and rollback. These features directly combat the data quality and governance issues that often plague traditional data lakes built directly on HDFS or cloud storage.
The blog post details how Iceberg achieves these functionalities through its unique design. Specifically, it maintains a manifest file that tracks the various data files comprising a table, along with schema information and partitioning details. This allows for efficient querying and data management, even as the underlying data scales and evolves. Furthermore, by supporting different file formats like Parquet and Avro, Iceberg offers flexibility in choosing the best format for specific use cases.
The analogy to Hadoop is further explored by discussing the potential for Iceberg to foster a new ecosystem of tools built around its core table format. The author suggests that this could lead to the emergence of specialized data warehousing solutions, data discovery tools, and other data management applications, all leveraging the solid foundation provided by Iceberg. This vision echoes the Hadoop ecosystem, but with a more streamlined and accessible approach.
The post concludes by acknowledging that Iceberg is still a relatively young project but shows immense promise. Its focus on open standards, its integration with modern cloud architectures, and its ability to address the shortcomings of traditional data lakes position it as a potential cornerstone of the modern data stack. While not claiming a definitive coronation, the author strongly suggests that Apache Iceberg has the potential to become as influential and foundational as Hadoop was in its prime, albeit through a different paradigm and with a more focused scope.
Summary of Comments ( 30 )
https://news.ycombinator.com/item?id=43277214
HN users generally disagree with the premise that Iceberg is the "Hadoop of the modern data stack." Several commenters point out that Iceberg solves different problems than Hadoop, focusing on table formats and metadata management rather than distributed compute. Some suggest that tools like dbt are closer to filling the Hadoop role in orchestrating data transformations. Others argue that the modern data stack is too fragmented for any single tool to dominate like Hadoop once did. A few commenters express skepticism about Iceberg's long-term relevance, while others praise its capabilities and adoption by major companies. The comparison to Hadoop is largely seen as inaccurate and unhelpful.
The Hacker News post "Apache iceberg the Hadoop of the modern-data-stack?" generated a moderate number of comments, mostly discussing the merits and drawbacks of Iceberg, its comparison to Hadoop, and its role within the modern data stack. There isn't overwhelming engagement, but enough comments exist to provide some diverse perspectives.
Several commenters pushed back against the article's comparison of Iceberg to Hadoop. They argue that Hadoop is a complex ecosystem encompassing storage (HDFS), compute (MapReduce, YARN), and other tools, while Iceberg primarily focuses on table formats and metadata management. They see Iceberg as more analogous to Hive's metastore, offering a standardized way to interact with data lakehouse architectures, rather than being a complete platform like Hadoop. One commenter pointed out that drawing parallels solely based on potential "vendor lock-in" is superficial and doesn't reflect the fundamental differences in their scope.
Some commenters expressed appreciation for Iceberg's features, highlighting its schema evolution capabilities, ACID properties, and support for different query engines. They noted its usefulness in managing large datasets and its potential to improve the reliability and maintainability of data pipelines. However, other comments countered that Iceberg's complexity could introduce overhead and might not be necessary for all use cases.
A recurring theme in the comments is the evolving landscape of the data stack and the role of tools like Iceberg within it. Some users discussed their experiences with Iceberg, highlighting successful integrations and the benefits they've observed. Others expressed caution, emphasizing the need for careful evaluation before adopting new technologies. The "Hadoop of the modern data stack" analogy sparked debate about whether such a centralizing force is emerging or even desirable in the current, more modular and specialized data ecosystem. A few comments touched on alternative table formats like Delta Lake and Hudi, comparing their features and suitability for different scenarios.
In summary, the comments section provides a mixed bag of opinions on Iceberg. While some acknowledge its potential and benefits, others question the comparison to Hadoop and advocate for careful consideration of its complexity and suitability for specific use cases. The discussion reflects the ongoing evolution of the data stack and the search for effective tools and architectures to manage the increasing volume and complexity of data.