  Machine learning (ML) is playing an increasingly important role in data
management tasks, particularly in Data Integration and Preparation (DI&P). The
success of ML-based approaches, however, heavily relies on the availability of
large-scale, high-quality labeled datasets for different tasks. Moreover, the
wide variety of DI&P tasks and pipelines oftentimes requires customizing ML
solutions which can incur a significant cost for model engineering and
experimentation. These factors inevitably hold back the adoption of ML-based
approaches to new domains and tasks.
  In this paper, we propose Sudowoodo, a multi-purpose DI&P framework based on
contrastive representation learning. Sudowoodo features a unified,
matching-based problem definition capturing a wide range of DI&P tasks
including Entity Matching (EM) in data integration, error correction in data
cleaning, semantic type detection in data discovery, and more. Contrastive
learning enables Sudowoodo to learn similarity-aware data representations from
a large corpus of data items (e.g., entity entries, table columns) without
using any labels. The learned representations can later be either directly used
or facilitate fine-tuning with only a few labels to support different DI&P
tasks. Our experiment results show that Sudowoodo achieves multiple
state-of-the-art results on different levels of supervision and outperforms
previous best specialized blocking or matching solutions for EM. Sudowoodo also
achieves promising results in data cleaning and semantic type detection tasks
showing its versatility in DI&P applications.
