Data Deduplication Strategies in an Open Lakehouse Architecture
Blog post from Onehouse
Data duplication poses significant challenges in data engineering pipelines, affecting storage costs, query performance, and data integrity. Duplication can occur at various stages, including ingestion, storage merging, and table management, especially in lakehouse architectures where open table formats like Apache Hudi, Iceberg, and Delta Lake are used. Apache Hudi addresses duplication through built-in deduplication strategies that operate at multiple pipeline stages, allowing users to define custom merge modes and ensuring data consistency. Hudi's approach contrasts with Apache Iceberg and Delta Lake, which rely on explicit MERGE operations and require users to handle deduplication externally. Effective deduplication is crucial for maintaining data quality, optimizing costs, and ensuring accurate analytics, and Apache Hudi's integrated solutions provide a flexible framework for managing duplicates in complex data workflows.
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