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.
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 ( 9 )
https://news.ycombinator.com/item?id=43092579
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.
The Hacker News post "Building an Open, Multi-Engine Data Lakehouse with S3 and Python" has generated a modest number of comments, primarily focusing on practical considerations and alternatives to the approach outlined in the article.
One commenter points out the potential cost implications of using multiple engines like Trino, Spark, and Dask, especially when considering the engineering overhead required to maintain such a complex system. They suggest that, for many use cases, a simpler solution involving a single engine and optimized data formats might be more cost-effective. This commenter also raises concerns about the lack of discussion on data governance, schema evolution, and other crucial aspects of data management in the original article.
Another comment highlights the performance implications of using Parquet files directly on S3 without a dedicated metadata layer like Apache Hive or Iceberg. They emphasize that while this setup might work for smaller datasets, it can become a significant bottleneck for larger datasets and more complex queries, echoing the concerns about scalability expressed in the previous comment. The commenter advocates for utilizing a table format like Iceberg or Delta Lake to improve query planning and overall performance.
A separate thread discusses the trade-offs between different query engines, with one commenter mentioning their preference for DuckDB, a newer analytical database management system, for its performance in certain analytical workloads. They acknowledge, however, that DuckDB's ecosystem is still developing and might not be as mature as those of Spark or Trino.
Finally, a user asks about the necessity of the custom Python layer described in the article, suggesting that existing tools like Apache Hudi might already provide similar functionalities. This comment underscores a common theme in the discussion: a preference for established, battle-tested solutions over potentially more complex custom implementations, especially when dealing with the intricacies of data lake management.
In summary, the comments on Hacker News express a cautious optimism towards the multi-engine approach described in the article. While acknowledging the potential flexibility of using different engines for specific tasks, commenters predominantly emphasize the practical challenges related to cost, complexity, and performance. They often suggest simpler alternatives and highlight the importance of features like data governance and efficient metadata management, which were not extensively covered in the original article.