ingest-conductor

Parallel data ingestion orchestrates multiple data sources simultaneously by dividing tasks into independent stages. This staged processing allows independent sources to run concurrently, significantly reducing the total time required for batch ingestion.

Staged processing separates the ingestion process into distinct phases, enabling parallel processing of independent sources. This approach optimizes large batch ingestion by managing each stage sequentially while executing source processing concurrently.

Yes, parallel data processing requires the ingest-source Skill for actual data extraction. The orchestrator manages the parallel waves and staged coordination, but relies on ingest-source to execute the underlying data extraction and processing tasks.

Parallel ingestion integrates with the reduce phase by coordinating with the ingest-wrap-reduce Skill. The conductor manages the parallel stages of data extraction, then hands off the processed streams to the reduce phase to merge and finalize the data.

Yes, staged processing is ideal for large batches of documents or data entries. Dividing the ingestion task into parallel stages significantly reduces processing time when handling large volumes of independent data sources.

Orchestrating ingestion waves is limited to independent data sources that can be processed in parallel. It requires specific dependencies, namely ingest-source for extraction and ingest-wrap-reduce for merging, to successfully complete the data processing pipeline.