JAjasode·Sep 27, 2017
The frictionlessdata landing page has very generalized verbiage so here's my technical summary of it...
The main idea for "container" or "package" hinges on a file called "datapackage.json"[1].
An analogy would be the "sfv" files like "checksums.sfv" for verifying the integrity of files. Since so many people use "sfv" as a defacto standard, many programs exist to scan it and verify the associated files. Another analogy would be DTD for XML files.
Similarly, if everybody could converge on the file "datapackage.json" as a metadata & schema description standard, a useful ecosystem of utilities and libraries for processing data would take advantage of it.
One example library would be: https://github.com/frictionlessdata/datapackage-py
(In the Python source code for "package.py"[2], Ctrl+F search for "datapackage.json" to see how it looks f
Read the rest
or that particular file.)
With a data wrangling API like that, one could then do joins on csv files directly[3] and write the results to another csv file with the associated "datapackage.json".
Instead of passing "dumb" csv or raw json files around, add a little "intelligence" to the dataset by way of "datapackage.json" so tools can parse the schema and process csv/json at a higher abstraction level. That leads to more "effortless" and "frictionless" data interoperability.
What I can't tell so far is if "datapackage.json" already has momentum of adoption across many communities such as Julia, Tensorflow, Hadoop, etc. and we need to get on the bandwagon -- or -- adoption is still in its infancy and there are other competing data "container/package" specifications to look at.
[1] http://frictionlessdata.io/guides/data-package/
[2] https://github.com/frictionlessdata/datapackage-py/blob/mast...
[3] http://frictionlessdata.io/guides/joining-tabular-data-in-py...