Integration Data Pipeline
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An Integration Data Pipeline is a automated orchestrated data pipeline that can support data integration tasks between heterogeneous systems through data transformation, data routing, and data synchronization.
- AKA: Data Integration Pipeline, ETL Pipeline, Data Flow Pipeline.
- Context:
- It can typically extract Source Datas from source systems through data extractors supporting batch extraction, incremental extraction, and real-time extraction.
- It can typically transform Raw Datas through transformation engines performing data cleansing, data enrichment, and data normalization.
- It can typically load Processed Datas into target systems through data loaders implementing bulk loading, stream loading, and merge loading.
- It can typically orchestrate Pipeline Stages through workflow engines managing stage dependency, execution order, and parallel processing.
- It can typically handle Data Qualitys through quality frameworks ensuring data validation, data verification, and data reconciliation.
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- It can often provide Error Handlings through exception handlers managing data errors, system failures, and recovery procedures.
- It can often enable Data Lineages through lineage trackers recording data sources, transformation history, and data destinations.
- It can often support Performance Optimizations through optimization techniques including data partitioning, parallel processing, and incremental processing.
- It can often maintain Pipeline Monitorings through monitoring systems tracking pipeline metrics, data volumes, and processing latency.
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- It can range from being a Simple Integration Data Pipeline to being a Complex Integration Data Pipeline, depending on its pipeline complexity.
- It can range from being a Batch Integration Data Pipeline to being a Stream Integration Data Pipeline, depending on its processing model.
- It can range from being a ETL Integration Data Pipeline to being an ELT Integration Data Pipeline, depending on its transformation strategy.
- It can range from being a Single-Source Integration Data Pipeline to being a Multi-Source Integration Data Pipeline, depending on its data source count.
- It can range from being a On-Premise Integration Data Pipeline to being a Cloud Integration Data Pipeline, depending on its deployment environment.
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- It can integrate with Data Integration Platform for pipeline orchestration.
- It can connect to Message Queue for data buffering.
- It can interface with Data Catalog for metadata management.
- It can communicate with Monitoring Platform for pipeline observability.
- It can synchronize with Scheduler Service for pipeline execution.
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- Example(s):
- Enterprise Integration Data Pipelines, such as:
- Cloud Integration Data Pipelines, such as:
- Specialized Integration Data Pipelines, such as:
- ...
- Counter-Example(s):
- Manual Data Processing, which lacks automation and systematic workflow.
- Direct Database Replication, which lacks transformation capability.
- File Transfer Protocol, which lacks data processing and orchestration.
- See: Data Pipeline, ETL Process, Data Integration, Data Transformation System, Workflow Orchestration, Stream Processing, Data Engineering.
- Reference(s):