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.
DeepSeek's smallpond extends DuckDB, the popular in-process analytical database, with distributed computing capabilities. It leverages a shared-nothing architecture where each node holds a portion of the data, allowing for parallel processing of queries across a cluster. Smallpond introduces a distributed query planner that optimizes query execution by distributing tasks and aggregating results efficiently. This empowers DuckDB to handle larger-than-memory datasets and significantly improves performance for complex analytical workloads. The project aims to make distributed computing accessible within the familiar DuckDB environment, retaining its ease of use and performance characteristics for larger-scale data analysis.
Hacker News commenters generally expressed excitement about the potential of combining DeepSeek's distributed computing capabilities with DuckDB's analytical power. Some questioned the performance implications and overhead of such a distributed setup, particularly concerning query planning and data transfer. Others raised concerns about the choice of Raft consensus, suggesting alternative distributed consensus algorithms might be more performant. Several users highlighted the value proposition for data lakes, allowing direct querying without complex ETL pipelines. The discussion also touched on the competitive landscape, comparing the approach to existing solutions like Presto and Spark, with some speculating on potential acquisition scenarios. A few commenters shared their positive experiences with DuckDB's speed and ease of use, further reinforcing the appeal of this integration. Finally, there was curiosity around the specifics of DeepSeek's technology and its impact on DuckDB's licensing.
Apache Iceberg is an open table format for massive analytic datasets. It brings modern data management capabilities like ACID transactions, schema evolution, hidden partitioning, and time travel to big data, while remaining performant on petabyte scale. Iceberg supports various data file formats like Parquet, Avro, and ORC, and integrates with popular big data engines including Spark, Trino, Presto, Flink, and Hive. This allows users to access and manage their data consistently across different tools and provides a unified, high-performance data lakehouse experience. It simplifies complex data operations and ensures data reliability and correctness for large-scale analytical workloads.
Hacker News users discuss Apache Iceberg's utility and compare it to other data lake table formats. Several commenters praise Iceberg's schema evolution features, particularly its handling of schema changes without rewriting the entire dataset. Some express concern about the complexity of implementing Iceberg, while others highlight the benefits of its open-source nature and active community. Performance comparisons with Hudi and Delta Lake are also brought up, with some users claiming Iceberg offers better performance for certain workloads while others argue it lags behind in features like time travel. A few users also discuss Iceberg's integration with various query engines and data warehousing solutions. Finally, the conversation touches on the potential for Iceberg to become a standard table format for data lakes.
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.