Data Processing System

A Data Processing System is a computing system that implements data processing algorithms to solve data processing tasks (through automated operations).

• AKA: Data Processor, Processing Engine.
• Context:
  • Data Processing System Task Input: Data Processing System Raw Data, Data Processing System Structured Data, Data Processing System Unstructured Data
    • Data Processing System Optional Input: Data Processing System Processing Parameters, Data Processing System Configuration Settings
  • Data Processing System Task Output: Data Processing System Processed Data, Data Processing System Data Reports
  • Data Processing System Task Performance Measure: Data Processing System Processing Speed, Data Processing System Quality Score, Data Processing System Resource Utilization
  • ...
  • It can provide Data Processing System Core Functions through Data Processing System Processing Engines.
  • It can perform Data Processing System Data Ingestion through Data Processing System Input Handlers.
  • It can execute Data Processing System Data Transformation through Data Processing System Processing Engines.
  • It can manage Data Processing System Data Output through Data Processing System Delivery Mechanisms.
  • It can validate Data Processing System Data Quality through Data Processing System Validation Rules.
  • It can optimize Data Processing System Resource Usage through Data Processing System Resource Schedulers.
  • ...
  • It can range from being a Simple Data Processing System to being a Complex Data Processing System, depending on its data processing complexity.
  • It can range from being a Basic Data Processing System to being an Information Processing System, depending on its processing abstraction level.
  • It can range from being a Data Intensive Processing System to being a Data Unintensive Processing System, depending on its data volume.
  • It can range from being a Standalone Data Processing System to being a Distributed Data Processing System, depending on its processing architecture.
  • It can range from being a Small Data Processing System to being a Big Data Processing System, depending on its processing scale.
  • It can range from being a Batch Data Processing System to being a Stream Data Processing System, depending on its processing mode.
  • It can range from being a Single Purpose Data Processing System to being a Multi Purpose Data Processing System, depending on its application scope.
  • ...
• Examples:
  • Data Processing System Components, such as:
    • Data Processing System Storage Components, such as:
      • Data Processing System Storage Subsystems for data management.
      • Data Processing System Processing Units for computation execution.
    • Data Processing System Control Components, such as:
      • Data Processing System Monitoring Components for system health.
      • Data Processing System Security Modules for data protection.
      • Data Processing System Audit Components for process tracking.
  • Data Processing System Challenge Solutions, such as:
    • Data Processing System Scalability Solutions, such as:
      • Data Processing System Volume Handlers through scalable architecture.
      • Data Processing System Speed Managers through parallel execution.
    • Data Processing System Quality Solutions, such as:
      • Data Processing System Quality Checkers through validation checks.
      • Data Processing System Lineage Trackers through tracking mechanisms.
      • Data Processing System Fault Tolerance Handlers through recovery procedure.
  • Data Processing System Integration Capabilitys, such as:
    • Data Processing System Core Integrations, such as:
      • Data Processing System External Interfaces through system interfaces.
      • Data Processing System Governance Implementations through policy enforcement.
    • Data Processing System Operational Integrations, such as:
      • Data Processing System Monitoring Tools through health checks.
      • Data Processing System Recovery Mechanisms through backup mechanisms.
      • Data Processing System Optimization Tools through tuning parameters.
  • Batch Data Processing Systems, such as:
    • ETL Data Processing Systems, such as:
      • Informatica PowerCenter Processing System (2024) for enterprise data integration.
      • Apache NiFi Processing System (2024) for data flow automation.
      • Talend Data Processing System (2024) for business data processing.
    • Offline Data Processing Systems, such as:
      • Apache Hadoop Processing System (2024) for distributed processing.
      • Apache Spark Processing System (2024) for in-memory processing.
      • Google Cloud Dataproc Processing System (2024) for cloud-based processing.
  • Stream Data Processing Systems, such as:
    • Real Time Data Processing Systems, such as:
      • Apache Kafka Processing System (2024) for event streaming.
      • Apache Flink Processing System (2024) for stateful computation.
      • Amazon Kinesis Processing System (2024) for real-time analytics.
    • Near Real Time Data Processing Systems, such as:
      • Apache Storm Processing System (2024) for distributed computation.
      • Azure Stream Processing System (2024) for cloud streaming.
      • Confluent Platform Processing System (2024) for event processing.
  • Cloud Data Processing Systems, such as:
    • Managed Service Data Processing Systems, such as:
      • AWS Glue Processing System (2024) for serverless ETL.
      • Google Cloud Dataflow Processing System (2024) for stream batch processing.
      • Azure Data Factory Processing System (2024) for data integration.
    • Serverless Data Processing Systems, such as:
      • AWS Lambda Processing System (2024) for function processing.
      • Azure Functions Processing System (2024) for event processing.
      • Google Cloud Functions Processing System (2024) for microservice processing.
  • Specialized Data Processing Systems, such as:
    • Domain Specific Data Processing Systems, such as:
      • Financial Data Processing Systems for transaction processing.
      • Healthcare Data Processing Systems for medical data processing.
      • IoT Data Processing Systems for sensor data processing.
    • Purpose Built Data Processing Systems, such as:
      • Data Quality Processing Systems for validation processing.
      • Data Integration Processing Systems for source combination.
      • Data Transformation Processing Systems for format conversion.
  • ...
• Counter-Examples:
  • Data Storage Systems, which preserve rather than process data.
  • Data Analysis Systems, which analyze rather than transform data.
  • Knowledge Processing Systems, which handle semantic content rather than raw data.
  • Data Collection Systems, which gather rather than process data.
  • Data Visualization Systems, which display rather than process data.
• See: Data Processing System Architecture, Data Processing System Pipeline, Data Processing System Scalability, Data Processing System Framework, Data Processing System Management, Data Processing System Integration, Data Processing System Governance, Data Processing System Optimization, Data Processing System Reliability, Data Processing System Quality Management.

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