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.
Smallpond is a lightweight Python framework designed for efficient data processing using DuckDB and the Apache Arrow-based filesystem 3FS. It simplifies common data tasks like loading, transforming, and analyzing datasets by leveraging the performance of DuckDB for querying and the flexibility of 3FS for storage. Smallpond aims to provide a convenient and scalable solution for working with various data formats, including Parquet, CSV, and JSON, while abstracting away the complexities of data management and enabling users to focus on their analysis. It offers a Pandas-like API for familiarity and ease of use, promoting a more streamlined workflow for data scientists and engineers.
Hacker News commenters generally expressed interest in Smallpond, praising its simplicity and the potential combination of DuckDB and fsspec. Several noted the clever use of these existing tools to create a lightweight yet powerful framework. Some questioned the long-term viability of relying solely on DuckDB for complex ETL pipelines, citing performance limitations for very large datasets or specific transformation tasks. Others discussed the benefits of using Polars or DataFusion as alternative processing engines. A few commenters also suggested potential improvements, like adding support for streaming data ingestion and more sophisticated data validation features. Overall, the sentiment was positive, with many seeing Smallpond as a useful tool for certain data processing scenarios.
This blog post demonstrates how to build a flexible and cost-effective data lakehouse using AWS S3 for storage and leveraging the open-source Apache Iceberg table format. It walks through using Python and various open-source query engines like DuckDB, DataFusion, and Polars to interact with data directly on S3, bypassing the need for expensive data warehousing solutions. The post emphasizes the advantages of this approach, including open table formats, engine interchangeability, schema evolution, and cost optimization by separating compute and storage. It provides practical examples of data ingestion, querying, and schema management, showcasing the power and flexibility of this architecture for data analysis and exploration.
Hacker News users generally expressed skepticism towards the proposed "open" data lakehouse solution. Several commenters pointed out that while using open file formats like Parquet is a step in the right direction, true openness requires avoiding vendor lock-in with specific query engines like DuckDB. The reliance on custom Python tooling was also seen as a potential barrier to adoption and maintainability compared to established solutions. Some users questioned the overall benefit of this approach, particularly regarding cost-effectiveness and operational overhead compared to managed services. The perceived complexity and lack of clear advantages led to discussions about the practical applicability of this architecture for most users. A few commenters offered alternative approaches, including using managed services or simpler open-source tools.
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.