---
date: '2022-11-20T11:18:31'
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  created: '2022-11-20T11:18:31.041323+00:00'
  document:
    title:
    - 'Data Engineering in 2022: ELT tools'
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  tags:
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  - data-science
  - ELT
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    - exact: "Working with the raw data has lots of benefits, since at the point of\
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        \ up to fit just a specific use case then you lose the fidelity of the data\
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        '
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        Of course, despite what the'
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    source: https://rmoff.net/2022/11/08/data-engineering-in-2022-elt-tools/
  text: absolutely right - there's also a data provenance angle here - it is useful
    to be able to point to a data point that is 5 or 6 transformations from the raw
    input and be able to say "yes I know exactly where this came from, here are all
    the steps that came before"
  updated: '2022-11-20T11:18:31.041323+00:00'
  uri: https://rmoff.net/2022/11/08/data-engineering-in-2022-elt-tools/
  user: acct:ravenscroftj@hypothes.is
  user_info:
    display_name: James Ravenscroft
in-reply-to: https://rmoff.net/2022/11/08/data-engineering-in-2022-elt-tools/
tags:
- data-engineering
- data-science
- ELT
- hypothesis
type: reply
url: /replies/2022/11/20/1668943111

---



 <blockquote>Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.</blockquote>absolutely right - there's also a data provenance angle here - it is useful to be able to point to a data point that is 5 or 6 transformations from the raw input and be able to say "yes I know exactly where this came from, here are all the steps that came before"