LakeMLB: Data Lake Machine Learning Benchmark
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914321696555008 |
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| author | Pan, Feiyu Zhang, Tianbin Zhang, Aoqian Sun, Yu Wang, Zheng Chen, Lixing Pan, Li Li, Jianhua |
| author_facet | Pan, Feiyu Zhang, Tianbin Zhang, Aoqian Sun, Yu Wang, Zheng Chen, Lixing Pan, Li Li, Jianhua |
| contents | Modern data lakes have emerged as foundational platforms for large-scale machine learning, enabling flexible storage of heterogeneous data and structured analytics through table-oriented abstractions. Despite their growing importance, standardized benchmarks for evaluating machine learning performance in data lake environments remain scarce. To address this gap, we present LakeMLB (Data Lake Machine Learning Benchmark), designed for the most common multi-source, multi-table scenarios in data lakes. LakeMLB focuses on two representative multi-table scenarios, Union and Join, and provides three real-world datasets for each scenario, covering government open data, finance, Wikipedia, and online marketplaces. The benchmark supports three representative integration strategies: pre-training-based, data augmentation-based, and feature augmentation-based approaches. We conduct extensive experiments with state-of-the-art tabular learning methods, offering insights into their performance under complex data lake scenarios. We release both datasets and code to facilitate rigorous research on machine learning in data lake ecosystems; the benchmark is available at https://github.com/zhengwang100/LakeMLB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10441 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LakeMLB: Data Lake Machine Learning Benchmark Pan, Feiyu Zhang, Tianbin Zhang, Aoqian Sun, Yu Wang, Zheng Chen, Lixing Pan, Li Li, Jianhua Machine Learning Artificial Intelligence H.2.8; I.2.6; I.5.1 Modern data lakes have emerged as foundational platforms for large-scale machine learning, enabling flexible storage of heterogeneous data and structured analytics through table-oriented abstractions. Despite their growing importance, standardized benchmarks for evaluating machine learning performance in data lake environments remain scarce. To address this gap, we present LakeMLB (Data Lake Machine Learning Benchmark), designed for the most common multi-source, multi-table scenarios in data lakes. LakeMLB focuses on two representative multi-table scenarios, Union and Join, and provides three real-world datasets for each scenario, covering government open data, finance, Wikipedia, and online marketplaces. The benchmark supports three representative integration strategies: pre-training-based, data augmentation-based, and feature augmentation-based approaches. We conduct extensive experiments with state-of-the-art tabular learning methods, offering insights into their performance under complex data lake scenarios. We release both datasets and code to facilitate rigorous research on machine learning in data lake ecosystems; the benchmark is available at https://github.com/zhengwang100/LakeMLB. |
| title | LakeMLB: Data Lake Machine Learning Benchmark |
| topic | Machine Learning Artificial Intelligence H.2.8; I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2602.10441 |