Stories with Tag Data Warehousing

• Preview: Amazon S3 Tables and Lakehouse in DuckDB

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  Posted: 2025-03-18 16:36:20

  Verbose tl;dr

  DuckDB now offers preview support for querying data directly in Amazon S3 via a new extension. This allows users to create and query tables stored as Parquet, CSV, or JSON files on S3 without downloading data, leveraging S3's scalability and DuckDB's analytical capabilities. The extension utilizes the httpfs extension for access and supports various S3-specific features like AWS credentials and different regions. While still experimental, this functionality opens the door to building efficient "lakehouse" architectures directly on S3 using DuckDB.

  This DuckDB blog post announces and details a preview release of a highly anticipated feature: the ability to query data directly in Amazon S3 using DuckDB, effectively turning S3 into a data lakehouse. The post emphasizes the performance and cost benefits of this approach, eliminating the need for complex and expensive data warehousing solutions in many scenarios.

  The core of the new functionality revolves around treating S3 buckets as if they were local file systems. Users can now create DuckDB tables directly on top of Parquet files stored in S3, querying the data without needing to download it first. This direct access is made possible through the integration of the s3fs file system library, enabling seamless interaction with S3 objects. The blog post highlights the simplicity of this integration, demonstrating the creation of a table from S3 data with a single SQL command. This streamlined process eliminates the data movement and transformation steps often required when using traditional data warehouses.

  Performance is a key focus of the announcement. The post explains how DuckDB leverages its internal query engine optimizations to achieve efficient querying of S3-based data. These optimizations include parallel processing, columnar storage, and intelligent filtering, all contributing to fast query execution even on large datasets. The post provides comparative performance benchmarks, showcasing the speed advantages of DuckDB compared to other query engines when accessing data in S3.

  Cost-effectiveness is another significant benefit highlighted in the blog post. By eliminating the need to move and store data in intermediate systems, DuckDB reduces both storage costs associated with data duplication and compute costs related to data transfer and processing. The pay-per-use nature of S3, combined with DuckDB's efficient querying capabilities, results in a more cost-effective solution for many analytical workloads.

  The post also discusses the preview nature of this release. While core functionalities are already implemented and demonstrably performant, ongoing development is focused on expanding format support beyond Parquet, enhancing SQL compliance, and further optimizing performance. The authors actively encourage community feedback to guide the development and ensure a robust and feature-rich final release. They detail how users can try out the preview version, providing instructions for installation and configuration. The post concludes by inviting users to explore the new S3 integration and contribute to its development through feedback and contributions.

  • DuckDB
  • Amazon S3
  • Data Lakes
  • Lakehouse
  • Data Warehousing
  • Cloud Storage
  • Query Engine
  • Analytics
  • Data Engineering
  • SQL
  • Columnar Storage
  • parquet
  • data formats
  • Preview Release
  • big data

  Summary of Comments ( 33 )
  https://news.ycombinator.com/item?id=43401421

  Verbose tl;dr

  Hacker News commenters generally expressed excitement about DuckDB's new S3 integration, praising its speed, simplicity, and potential to disrupt the data lakehouse space. Several users shared their positive experiences using DuckDB, highlighting its performance advantages compared to other query engines like Presto and Athena. Some raised concerns about the potential vendor lock-in with S3, suggesting that supporting alternative storage solutions would be beneficial. Others discussed the limitations of Parquet files for analytical workloads, and how DuckDB might address those issues. A few commenters pointed out the importance of robust schema evolution and data governance features for enterprise adoption. The overall sentiment was very positive, with many seeing this as a significant step forward for data analysis on cloud storage.

  The Hacker News post "Preview: Amazon S3 Tables and Lakehouse in DuckDB" generated a moderate number of comments discussing the announcement of DuckDB's ability to query data directly in Amazon S3, functioning similarly to a lakehouse. Several commenters expressed excitement and approval for this development.

  A recurring theme in the comments is the praise for DuckDB's impressive speed and efficiency. Users shared anecdotal experiences of DuckDB outperforming other database solutions, particularly for analytical queries on parquet files. Some specifically highlighted its superiority over Presto and Athena in certain scenarios, mentioning significantly faster query times. This performance advantage seems to be a key driver of the positive reception towards the S3 integration.

  Another point of discussion revolves around the practical implications of this feature. Commenters discussed the benefits of being able to analyze data directly in S3 without needing to move or transform it. This is seen as a major advantage for data exploration, prototyping, and ad-hoc analysis. The convenience and cost-effectiveness of querying data in-place were emphasized by several users.

  Several comments delve into technical aspects, comparing DuckDB's approach to other lakehouse solutions like Databricks and Apache Iceberg. The discussion touched upon the differences in architecture and the trade-offs between performance and features. Some commenters speculated about the potential use cases for DuckDB's S3 integration, mentioning applications in data science, analytics, and log processing.

  While the overall sentiment is positive, some comments also raised questions and concerns. One commenter inquired about the maturity and stability of the S3 integration, as it is still in preview. Another user pointed out the limitations of DuckDB in handling highly concurrent workloads compared to distributed query engines. Furthermore, discussions emerged around the security implications of accessing S3 data directly and the need for proper authentication and authorization mechanisms.

  Finally, some comments explored the potential impact of this feature on the data warehousing and lakehouse landscape. The ability of DuckDB to query S3 data efficiently could potentially disrupt existing solutions and offer a more streamlined and cost-effective approach to data analytics. Some speculated on the future development of DuckDB and its potential to become a major player in the cloud data ecosystem.

• Smallpond – A lightweight data processing framework built on DuckDB and 3FS

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  Posted: 2025-02-28 01:56:35

  Verbose tl;dr

  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.

  The GitHub repository introduces Smallpond, a novel data processing framework meticulously designed for efficiency and ease of use, especially when dealing with medium-sized datasets (ranging from gigabytes to terabytes). It leverages the strengths of two core technologies: DuckDB, an in-process analytical SQL database, and 3FS, a file system abstraction layer optimized for object storage services like AWS S3.

  Smallpond aims to bridge the gap between simplistic single-machine processing and the complexities of distributed computing frameworks like Spark. It avoids the operational overhead of a distributed system while still providing substantial performance improvements over naive single-machine approaches, particularly when working with cloud-stored data.

  The framework's architecture centers around the concept of "ponds," which represent logical units of data. These ponds are essentially directories residing on a compatible file system (typically 3FS for cloud storage access or the local file system). Within a pond, data is stored as Parquet files, a columnar storage format well-suited for analytical queries.

  Smallpond facilitates data processing by providing a Python API that seamlessly integrates with DuckDB. Users can define data transformations using SQL queries directly within their Python code. Smallpond then orchestrates the execution of these queries against the data stored in the designated pond, leveraging DuckDB's efficient query engine and optimized Parquet handling. This tight integration allows users to leverage the familiarity and expressiveness of SQL while benefiting from the performance advantages of DuckDB and the scalability afforded by cloud storage via 3FS.

  The framework further enhances efficiency by enabling parallel processing of multiple ponds. This allows users to distribute their workload across multiple cores or machines, significantly accelerating processing time for large datasets. This parallelism is managed transparently by Smallpond, simplifying the process for the user.

  Smallpond emphasizes simplicity and ease of use as core design principles. The Python API is designed to be intuitive and easy to learn, even for users without prior experience with distributed computing frameworks. The framework handles the complexities of data partitioning, query execution, and result aggregation, freeing the user to focus on the logic of their data transformations. Furthermore, the reliance on SQL allows users to leverage their existing SQL skills and readily adapt existing SQL-based workflows.

  In summary, Smallpond offers a streamlined and efficient approach to processing medium-sized datasets, combining the power of DuckDB and 3FS to provide a user-friendly and performant alternative to both simplistic single-machine processing and complex distributed systems. Its focus on SQL-based transformations, efficient Parquet handling, and transparent parallelism simplifies the data processing pipeline and allows users to effectively analyze data stored in cloud storage or locally without the overhead of managing a distributed computing cluster.

  • data processing
  • Framework
  • DuckDB
  • 3FS
  • lightweight
  • Data Analysis
  • SQL
  • Python
  • Data Engineering
  • Database
  • file system
  • Open Source
  • Data Science
  • Data Lake
  • parquet
  • CSV
  • Data Warehousing

  Summary of Comments ( 42 )
  https://news.ycombinator.com/item?id=43200793

  Verbose tl;dr

  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.

  The Hacker News post titled "Smallpond – A lightweight data processing framework built on DuckDB and 3FS" has a modest number of comments, generating a brief discussion around the project. Several commenters express initial interest and curiosity about Smallpond, noting the appealing combination of DuckDB and fsspec/3FS.

  One commenter questions the need for another data processing framework given the existing landscape, prompting a response from the project author (seemingly u/tmokmss) clarifying that Smallpond aims to address a specific niche: providing an easy-to-use, Python-native framework tailored for data exploration and analysis on medium-sized datasets that fit comfortably in memory. They emphasize that Smallpond isn't intended to compete with larger-scale distributed processing frameworks like Spark or Dask, but rather offers a streamlined, lightweight alternative for simpler tasks. The author further explains the project's focus on leveraging DuckDB's efficient in-memory processing capabilities, combined with the flexibility of accessing data from various sources via fsspec/3FS.

  Another commenter raises a point about the project's early stage of development and the limited documentation, to which the author acknowledges the current state and expresses their commitment to improving documentation as the project matures. They also invite contributions and feedback from the community.

  The discussion also briefly touches upon alternative approaches, with one commenter suggesting exploring Polars as another potential tool in this space. However, there's no extended debate or comparison between Smallpond and other frameworks. The overall tone of the comments remains generally positive and inquisitive, with users expressing interest in the project's potential while recognizing its early stage of development.

• SQL pipe syntax available in public preview in BigQuery

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  Posted: 2025-02-10 10:38:29

  Verbose tl;dr

  BigQuery now supports SQL pipe syntax in public preview. This feature simplifies complex queries by allowing users to chain multiple SQL statements together, passing the results of one statement as input to the next. This improves readability and maintainability, particularly for transformations involving several steps. The pipe operator, |, connects these statements, offering a more streamlined alternative to subqueries and common table expressions (CTEs). This syntax is compatible with various SQL functions and operators, enabling flexible data manipulation within the pipeline.

  Google BigQuery now offers a public preview of a new SQL syntax feature called "piping," significantly enhancing the readability and maintainability of complex queries. This new syntax allows users to chain multiple SQL SELECT statements together sequentially, passing the output of one statement as the input to the next, much like piping commands in a Unix shell. This streamlined approach simplifies the construction of elaborate data transformations and analyses.

  Traditionally, complex queries in BigQuery often involved nested subqueries or common table expressions (CTEs), which can become difficult to decipher and manage as their complexity grows. The pipe syntax offers a more linear and intuitive alternative. Instead of nesting queries within one another, users can write a series of independent SELECT statements connected by the pipe operator, denoted by |. This operator takes the result set of the preceding SELECT statement and feeds it directly into the subsequent SELECT statement, effectively creating a processing pipeline.

  This feature provides several key advantages. First, it improves readability by breaking down complex transformations into smaller, more manageable steps. Each step in the pipeline performs a specific operation, making it easier to understand the overall logic of the query. Second, it enhances maintainability by promoting modularity. Changes or optimizations can be applied to individual stages of the pipeline without affecting other parts of the query. Third, it can potentially improve performance in certain scenarios by allowing BigQuery to optimize the execution of the pipeline as a whole.

  The pipe syntax supports a variety of SQL operations, including filtering with WHERE clauses, aggregation with GROUP BY clauses, joining with other tables, and ordering with ORDER BY clauses. It also integrates seamlessly with existing BigQuery features like user-defined functions (UDFs) and materialized views. Furthermore, the pipe operator can be combined with WITH clauses to define named subqueries within the pipeline, offering further flexibility and organization.

  While currently in public preview, this pipe syntax represents a significant step forward in making BigQuery more user-friendly and efficient for complex data analysis tasks. It provides a powerful yet intuitive way to construct and manage intricate data pipelines, allowing analysts and developers to focus on the logic of their analysis rather than the intricacies of SQL syntax. This feature aligns with the broader trend of simplifying data processing and making powerful analytical tools accessible to a wider audience. The public preview period allows users to experiment with the new syntax and provide feedback to Google, contributing to its refinement and eventual general availability.

  • SQL
  • BigQuery
  • Google Cloud Platform
  • GCP
  • Data Analysis
  • Data Warehousing
  • Pipe Syntax
  • Public Preview
  • Database
  • Query Language
  • Cloud Computing

  Summary of Comments ( 40 )
  https://news.ycombinator.com/item?id=42998904

  Verbose tl;dr

  Hacker News users generally expressed enthusiasm for BigQuery's new pipe syntax, finding it more readable and maintainable than traditional nested queries. Several commenters compared it favorably to dplyr in R and praised its potential for simplifying complex data transformations. Some highlighted the benefits for data scientists and analysts less familiar with SQL intricacies. A few users raised questions about performance implications and debugging, while others wondered about future compatibility with other SQL dialects and the potential for integration with tools like dbt. Overall, the sentiment was positive, with many viewing the pipe syntax as a significant improvement to the BigQuery SQL experience.

  The Hacker News post discussing BigQuery's SQL pipe syntax has generated several comments, mostly positive and intrigued by the feature.

  Several commenters express excitement about the pipe syntax, viewing it as a significant improvement for SQL readability and workflow. They believe it allows for a more natural, top-down approach to writing queries, making complex transformations easier to follow and debug. This sentiment is echoed by multiple users who find the traditional nested SQL structure cumbersome.

  One commenter points out the similarity and inspiration drawn from dplyr, a popular R package known for its data manipulation capabilities using pipes. They also note how this pipe syntax aligns with other "modern" SQL features found in systems like DuckDB. Another user highlights how the syntax allows for step-by-step data transformations, which they see as beneficial for debugging and understanding query logic.

  A practical use case is mentioned where the commenter envisions using pipes to chain multiple regular expressions for complex data cleaning and validation. The ability to break down these operations into smaller, piped steps is seen as a significant advantage.

  One commenter contrasts BigQuery's approach with something like WITH clauses (Common Table Expressions or CTEs), suggesting that pipes offer better readability, especially when dealing with a large number of transformations. They also touch upon the benefit of improved code organization, which becomes particularly relevant in larger projects.

  A point of discussion arises concerning potential performance implications. One commenter speculates about whether these piped queries might be less efficient than their traditional counterparts. However, another commenter counters this by mentioning that the compiler likely optimizes the execution plan, suggesting that performance shouldn't be significantly affected. This suggests a general curiosity within the community about the behind-the-scenes mechanics and performance characteristics of the new syntax.

  Finally, there's acknowledgment that while pipes enhance readability, they don't fundamentally change SQL's underlying capabilities. The commenter implies that the core functionality remains the same, with pipes primarily serving as a syntactic sugar to improve the user experience.

• Apache Iceberg

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  Posted: 2025-01-23 01:03:02

  Verbose tl;dr

  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.

  The Apache Iceberg website introduces Iceberg as a high-performance format for massive analytic tables. It emphasizes Iceberg's ability to handle data at petabyte scale, making it suitable for large data warehouses and data lakes. The site meticulously outlines several key features that distinguish Iceberg from other table formats.

  First and foremost, Iceberg offers robust schema evolution, allowing users to modify the table schema—adding, deleting, or updating columns—without rewriting the underlying data. This functionality includes support for hidden partitions, which can be utilized for optimizing query performance without exposing users to the underlying partitioning scheme. This dynamic schema evolution ensures data consistency and avoids disruptive downtime associated with schema changes in traditional systems.

  A core strength of Iceberg lies in its ACID properties, ensuring data integrity through atomic operations. This includes serializable isolation, which prevents write conflicts and ensures that all transactions are processed in a consistent and predictable order, akin to a single-threaded execution. This guarantees data accuracy and reliability, even in highly concurrent environments.

  Iceberg's focus on performance is evident in its optimized query planning. Iceberg leverages hidden partitioning and other techniques to prune data files irrelevant to the query, leading to significantly faster query execution. The website explicitly states compatibility with a wide range of data processing engines, including Spark, Trino, Presto, Flink, and Hive, further enhancing its versatility and integration potential.

  The site highlights Iceberg's time travel capabilities. This feature allows users to query the table's state at any specific point in time, effectively providing snapshot isolation and enabling auditing and rollback functionalities. Users can revert to previous table versions with ease, offering a powerful mechanism for data recovery and analysis of historical trends.

  Iceberg is designed for open data access and interoperability. It provides a unified table format that can be accessed by various processing engines without requiring specialized connectors. This open architecture fosters a collaborative ecosystem and simplifies data management across different platforms.

  The website also emphasizes the comprehensive support and resources available for Iceberg. It links to detailed documentation, including a quickstart guide, and provides information on community involvement through mailing lists, Slack channels, and GitHub repositories. This encourages user engagement and facilitates knowledge sharing within the Iceberg community.

  Finally, the site positions Apache Iceberg as a future-proof solution for large-scale analytics, emphasizing its adaptability to evolving data needs and technological advancements. Its commitment to open standards and community-driven development ensures its continued growth and relevance in the rapidly changing landscape of big data processing.

  • Apache Iceberg
  • Data Lakehouse
  • Data Lake
  • Table Format
  • Data Warehousing
  • big data
  • Apache Spark
  • Apache Hadoop
  • Presto
  • Trino
  • Query Engine
  • data management
  • Cloud Storage
  • AWS S3
  • Azure Blob Storage
  • Google Cloud Storage
  • Data Analytics
  • Schema Evolution
  • ACID Properties
  • Data Versioning
  • time travel
  • Hidden Partitioning

  Summary of Comments ( 47 )
  https://news.ycombinator.com/item?id=42799388

  Verbose tl;dr

  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.

  The Hacker News post titled "Apache Iceberg" (https://news.ycombinator.com/item?id=42799388) has a moderate number of comments discussing the merits and drawbacks of the technology. Several commenters express familiarity with Iceberg and share their experiences.

  A compelling line of discussion revolves around Iceberg's performance and scalability compared to other table formats like Hudi and Delta Lake. One commenter mentions that Iceberg's simpler design contributes to better performance, particularly for smaller datasets, while Hudi and Delta Lake might be more suitable for very large datasets due to features like indexing and data skipping. This sparks further discussion about the trade-offs between simplicity and advanced features.

  Another interesting point raised is the ease of adoption and integration of Iceberg with existing data lake infrastructure. Commenters appreciate its compatibility with various query engines and the relatively low overhead in migrating from other table formats. The open nature of the project is also praised, contrasting it with the vendor lock-in concerns associated with some proprietary alternatives.

  Some comments focus on specific features of Iceberg, like schema evolution and time travel. These features are generally seen as positives, with users sharing examples of how they simplify data management and enable efficient data recovery. However, one commenter mentions potential challenges with schema evolution in very complex scenarios.

  There's a brief discussion comparing Iceberg to Databricks' Delta Lake, highlighting the open-source nature of Iceberg as a key differentiator. This aligns with the broader theme of preferring open solutions to avoid vendor dependence.

  A few comments also delve into the technical details of Iceberg's implementation, discussing topics like metadata management and file formats. While not as prevalent as the higher-level discussions, these comments provide valuable insights for those interested in the inner workings of the technology.

  Overall, the comments paint a generally positive picture of Apache Iceberg. The recurring themes are its performance, ease of use, open-source nature, and the advantages it offers over other table formats, especially for organizations looking for a robust yet simpler solution for managing data lakes. While some potential challenges are mentioned, they are often presented in the context of trade-offs and specific use cases, rather than outright criticisms.