GS/Accessing updates (1.0)

[jorgeorpinel]

https://katacoda.com/dvc/courses/get-started/accessing

Step 1:

• There are a pre-existing file and dir that get used much later which are confusing when you ls. Could they be downloaded automatically when you get to the step that needs them?
• "You don't need to be inside a Git or DVC repo to execute it" - Remove ore reuse the get note from https://katacoda.com/dvc/courses/get-started/versioning
• ls data.xml -> just ls?
• "accessing data file directly" -> "downloading the data file directly"
• "or as a data/model registry" -> (data registry pattern)

Step 2:

• Maybe this whole step should be part of step 1, if we can compress them a little.
• "Remember those .dvc files dvc add generates?" - Link to https://katacoda.com/dvc/courses/get-started/versioning (step 1 if possble)
• "dvc.lock that" -> "dvc.lock, which"
• "in Git, DVC remote storage config saved in Git" -> "in Git, and DVC remote storage config"
• "needed to access and download" -> "needed to access" - but this whole sentence is too long, could be rewritten.
• ls -lh -> just ls?
• "dvc get automated this by reading" - This explanation would make more sense before the wget example
• .dvc/config and get-started/data.xml.dvc links - Should it open the in-system IDE instead?
• "at the dataset-registry you cannot find it" -> "at the dataset-registry, you cannot find the file"
• "stored in a data storage" -> "stored in a DVC remote"

Step 3:

• "if you look at the Get Started repository" -> Should be [Data Registry]
• "dvc get can download them, but how do we first even know what exactly there before downloading (or accessing in other ways we'll cover later)?" -> "We can dvc get them, but how do we even know what data is tracked in a remote DVC repo before accessing it?"
• "we pass Git URL" -> "we pass a Git URL"
• "as with dvc get" -> "as dvc ge"t"
• "Now, you can see the data.xml file. As well" -> "Now we can see data.xml and"

Step 4:

• "Alternatively to the command line dvc get" -> "Besides using dvc commands"
• "with dvc.api" -> "with the Python API" (same link)
• Install dvc first, I think
• cat process.py... - Use IDE instead?
• "Yes, the interface" -> "The interface"
• "works similar" -> "works the same way"
• "It doesn't consume space for a file on the file system - it reads data directly into memory" -> "open() doesn't consume space in the file system - it streams data into memory as needed"
• "Means, you can" -> "This means that you can"
  But this 3rd point is kind of repetitive vs the 2nd one, may want to rephrase a bit
• "interface is the same" -> "the interface is the same"

Step 5:

• I'm not sure we even need the pre-existing example-get-started dir. Why have that an doverwrite data/data.xml? Just to match https://dvc.org/doc/start/data-access#download? The rest of the scenario doesn't match the GS anyway.
• "simplified" -> "simplifies"
• "complexity" -> "the complexity"
• "How about ..." -> "What about datasets or ML models?"
• "DVC repositories and dvc import command" -> "A DVC repository and the dvc import command"
• "The url and rev_lock subfields" - Needs more context (mention dvc.xml.dvc)
• git diff -> could just be cat data/data.xml.dvc. It's not clear why we're comparing something.
• "dvc import, is" -> "dvc import is"
• "repository this" -> "repository, this"

Step 6:
Not sure we need it since we've already mentioned and linked to the Data Registry pattern (use case).