Shuffling

Data shuffling is a process in modern data pipelines where the data is randomly redistributed across different partitions to enable parallel processing and better performance. Shuffling is generally done after some processing has been completed on the data, such as sorting or grouping, and before additional processing is performed. Shuffling can be an expensive operation in terms of time and resources, especially for large datasets.

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References

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  Data Shuffling | Dagster Glossary

  Randomize the order of data records to improve analysis and prevent bias.

  🔗dagster.io
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  Data Privacy through Shuffling and Masking

  Selectively share quality data across your organization for development, analysis and more without exposing Personally Identifiable Information (PII).

  🔗Talend - A Leader in Data Integration & Data Integrity