Delta Lake vs Apache Hudi
Developers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution meets developers should learn apache hudi when building or managing data lakes that require real-time data ingestion, efficient upserts/deletes, and incremental processing for analytics. Here's our take.
Delta Lake
Developers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution
Delta Lake
Nice PickDevelopers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution
Pros
- +It is particularly valuable for scenarios involving streaming and batch data processing, machine learning workflows, and data lakehouse architectures where combining the scalability of data lakes with the reliability of data warehouses is essential
- +Related to: apache-spark, data-lake
Cons
- -Specific tradeoffs depend on your use case
Apache Hudi
Developers should learn Apache Hudi when building or managing data lakes that require real-time data ingestion, efficient upserts/deletes, and incremental processing for analytics
Pros
- +It is particularly useful in scenarios like streaming ETL pipelines, real-time dashboards, and compliance-driven data management where data freshness and transactional consistency are critical
- +Related to: apache-spark, apache-flink
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Delta Lake if: You want it is particularly valuable for scenarios involving streaming and batch data processing, machine learning workflows, and data lakehouse architectures where combining the scalability of data lakes with the reliability of data warehouses is essential and can live with specific tradeoffs depend on your use case.
Use Apache Hudi if: You prioritize it is particularly useful in scenarios like streaming etl pipelines, real-time dashboards, and compliance-driven data management where data freshness and transactional consistency are critical over what Delta Lake offers.
Developers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution
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