distinctbatch

Distinctbatch is a term used in data engineering to refer to a processing approach that ensures each batch of data processed is distinct from previous batches. It can be implemented by tagging records with a batch identifier and enforcing deduplication across batches.

Origins and usage: The concept arises in the context of batch processing in ETL pipelines and data warehousing. While not a formal standard, it is described in discussions of idempotent batch processing and data lineage, where reproducibility and traceability of data are important.

Definition and mechanism: A distinct batch approach assigns a unique batch_id for each data load. During processing, the system filters out records that have already appeared in earlier batches, using keys such as primary keys, composite keys, or hash of record content. Deduplication may be performed at ingestion, during transformation, or at load time.

Benefits: Improves data quality by preventing duplication, strengthens reproducibility of nightly builds, and simplifies auditing and lineage tracking. It also helps align batch boundaries with feature store or model training pipelines.

Implementation considerations: Efficient indexing and storage of seen keys is critical. For large-scale pipelines, distributed caches, bloom filters, or external stores may be used. Handling late-arriving data and out-of-order batches requires strategy, such as reprocessing windows or soft deduplication. When multiple sources contribute to the same batch, coordination is needed to prevent race conditions.

Variants: batch-level deduplication, cross-batch deduplication, and per-source distinct batches.

See also: deduplication, idempotent processing, batch processing, data lineage.