Active Data Lakes: Regaining Physical Data Independence Without Losing Interoperability
Pascal Ginter, Viktor Leis
摘要
Data lakes aim to avoid vendor lock-in and enable interoperability between different query engines on a single copy of data. While early data lakes were only collections of files in various formats, they have since evolved to incorporate some features traditionally associated with relational databases. Today, Apache Parquet is the de facto standard file format for relational data in data lakes. This standardization is fundamental to interoperability, but it comes at the cost of physical data independence because query engines integrate tightly with Parquet. As a result, adoption of novel approaches in the areas of file formats, access paths, and storage media has been limited. We propose the Active Data Lake architecture as a way to restore physical data independence and demonstrate its potential experimentally through three example optimizations.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper2
- Interoperable ACID Transactions for Open Table FormatsTobias Götz, Daniel Ritter, Muhammad El-Hindi, Jana GicevaVLDB 2026
- BtrLog: Low-Latency Logging for Cloud Database SystemsMaximilian Kuschewski, Lam-Duy Nguyen, Matthias Jasny, Tobias Ziegler 等VLDB 2026
它引用的顶会 Paper8
- BtrBlocks: Efficient Columnar Compression for Data LakesMaximilian Kuschewski, David Sauerwein, Adnan Alhomssi, Viktor LeisSIGMOD 2023 · 被引用 47 次
- Exploiting Cloud Object Storage for High-Performance AnalyticsDominik Durner, Viktor Leis, Thomas NeumannVLDB 2023 · 被引用 45 次
- The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar CodeAzim Afroozeh, Peter BonczVLDB 2023 · 被引用 44 次
- The Composable Data Management System ManifestoPedro Pedreira, Orri Erling, Konstantinos Karanasos, Scott Schneider 等VLDB 2023 · 被引用 36 次
- Declarative Sub-Operators for Universal Data ProcessingMichael Jungmair, Jana GicevaVLDB 2023 · 被引用 17 次
相关 Paper
- Nested Parquet Is Flat, Why Not Use It? How To Scan Nested Data With On-the-Fly Key Generation and JoinsAlice Rey, Maximilian Rieger, Thomas NeumannSIGMOD 2025 · 被引用 1 次
- A Deep Dive into Common Open Formats for Analytical DBMSsChunwei Liu, Anna Pavlenko, Matteo Interlandi, Brandon HaynesVLDB 2023 · 被引用 23 次
- Rottnest: Indexing Data Lakes for SearchZiheng Wang, Sasha Krassovsky, Conor Kennedy, Alex Aiken 等ICDE 2025 · 被引用 1 次
- An Empirical Evaluation of Columnar Storage FormatsXinyu Zeng, Yulong Hui, Jiahong Shen, Andrew Pavlo 等VLDB 2024 · 被引用 59 次
- TreeCat: Standalone Catalog Engine for Large Data SystemsKeonwoo Oh, Pooja Nilangekar, Amol DeshpandeVLDB 2025
