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.
This blog post details the construction of an open, multi-engine data lakehouse architecture leveraging the flexibility of Amazon S3 for storage and the versatility of Python for data processing and orchestration. The author emphasizes the limitations of traditional data warehouses and data lakes, highlighting the need for a more adaptable and cost-effective solution. The data lakehouse paradigm aims to combine the best aspects of both, offering the structured query capabilities of a data warehouse with the scalability and schema flexibility of a data lake.
The core of the proposed architecture revolves around using S3 as the central data repository. Data is stored in an open format like Parquet, promoting interoperability between different processing engines. This approach avoids vendor lock-in and allows for choosing the most suitable tool for each task. The post specifically focuses on utilizing several open-source processing engines, including DuckDB, Apache Spark, and dbt.
The author demonstrates how to leverage Python to orchestrate the entire data pipeline. This includes data ingestion, transformation, and querying across different engines. Python acts as the glue, connecting these disparate components into a cohesive system. The post provides practical code examples showcasing how to interact with S3 using libraries like s3fs and pyarrow, load data into DuckDB and Spark, perform transformations, and ultimately query the processed data.
DuckDB is highlighted for its efficiency in handling analytical queries on datasets that fit within memory. Its ease of use within a Python environment makes it a powerful tool for exploring and analyzing data directly within the lakehouse. Apache Spark, on the other hand, is employed for large-scale data processing tasks that require distributed computing. The post illustrates how to use PySpark to transform data within the S3 environment, taking advantage of its scalability and performance.
dbt (data build tool) is integrated into the workflow for managing data transformations and ensuring data quality. The post explains how dbt can be used to define and execute transformations using SQL, enhancing the maintainability and testability of the data pipeline. This combination of tools allows for a modular and scalable approach to data processing.
The architecture described promotes a decoupled approach, where each component can be independently scaled and optimized. This provides flexibility in choosing the best tools for specific needs and allows for adapting to evolving data requirements. The post concludes by reiterating the benefits of this open, multi-engine approach, emphasizing its cost-effectiveness, flexibility, and avoidance of vendor lock-in. It paints a picture of a modern data architecture empowered by the combination of S3's scalable storage, Python's versatility, and the power of open-source processing engines.
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.