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Putting this data in kafka, separate databases, etc breaks not just the logical cohesion of the analysis but also leaves no room for global optimization.
See the premise of Weld : https://www.weld.rs/assets/weld-strata.pdf

• Lastly, there are "too many frameworks"and the programming model is too varied
  • No! Not Another Deep Learning Framework
  • https://www.mosharaf.com/wp-content/uploads/deepstack-hotos17.pdf
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Hypothesis : We need "continuous" analytics in both the time dimension and , in the physical dimension.data dimension and in terms of programming model

• relational algebra
• linear algebra
• differential programming
• probabilistic programming
• computational graphs
• differential dataflow
• etc

Why "deep"

• In terms of algos we have deep learning, ofc.
• But also in terms of "data fabric" we need to handle multi-dimensional, heterogeneous, business rich meaning data and abstractions over data.
  
  • relational data - need #RelationalAlgebra
  • arrays / matrices / tensors - need #LinearAlgebra
  • graph data - need graph representations and algos (https://www.slideshare.net/oracle4engineer/using-graphs-for-data-analysis)

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