Implementations of metadata tend to favor centralized,static metadata.This depiction is at variance with the past decade of focus on big data,cloud native architectures and streaming platforms.Big data velocity can de...Implementations of metadata tend to favor centralized,static metadata.This depiction is at variance with the past decade of focus on big data,cloud native architectures and streaming platforms.Big data velocity can demand a correspondingly dynamic view of metadata.These trends,which include DevOps,CI/CD,DataOps and data fabric,are surveyed.Several specific cloud native tools are reviewed and weaknesses in their current metadata use are identified.Implementations are suggested which better exploit capabilities of streaming platform paradigms,in which metadata is continuously collected in dynamic contexts.Future cloud native software features are identified which could enable streamed metadata to power real time data fusion or fine tune automated reasoning through real time ontology updates.展开更多
文摘Implementations of metadata tend to favor centralized,static metadata.This depiction is at variance with the past decade of focus on big data,cloud native architectures and streaming platforms.Big data velocity can demand a correspondingly dynamic view of metadata.These trends,which include DevOps,CI/CD,DataOps and data fabric,are surveyed.Several specific cloud native tools are reviewed and weaknesses in their current metadata use are identified.Implementations are suggested which better exploit capabilities of streaming platform paradigms,in which metadata is continuously collected in dynamic contexts.Future cloud native software features are identified which could enable streamed metadata to power real time data fusion or fine tune automated reasoning through real time ontology updates.
