Hi HN. I’m a programmer who has also been very involved in SEO and Internet Marketing, especially earlier on, between 2007-2014.I’ve successfully run many SEO campaigns and in recent years have done so in some e-commerce niches. I continue to implement server side rendering for SEO purposes (and others).I’m about to start work on an app which for the most part is clearly a better fit for an SPA...
https://github.com/frictionlessdata/specs/issues/537
Feel free to add your use cases to that issue.
[1] https://frictionlessdata.io/specs/table-schema/ [2] https://github.com/frictionlessdata/goodtables-py
(I work on the Frictionless Data specifications and tooling at Open Knowledge International.)
Thanks. We are working on the website at present [1], and we are trying to manage a balance of targeting technical and non-technical users, which is hard to get right.
About momentum - I can address that. We have seen significant momentum in the last 2 years, around open data / government transparency / civic tech ( our natural environment - see https://okfn.org for details ), around scientific / academic research via our work enabled by a grant from Sloan [2]( see http://frictionlessdata.io/case-studies/ for a small selection, more reports coming ), and in general around data wrangling and data science efforts (including integration of Table Schema [3] with Pandas [4]).
In terms of big data / machine learning - we have not actively worked in that space to present.
In terms of Julia, and other languages, we have a Julia library in development via our Tool Fund [5], and this will add to implementations [6] in PHP, Java, R, Clojure which are already underway via the Tool Fund, and accompany the Python, Javascript and Ruby implementations that we maintain directly at Open Knowledge International.
[1]: https://github.com/frictionlessdata/frictionlessdata.io/issu... [2]: https://sloan.org [3]: http://specs.frictionlessdata.io/table-schema/ [4]: https://pandas-docs.github.io/pandas-docs-travis/generated/p... [5]: http://toolfund.frictionlessdata.io [6]: https://github.com/frictionlessdata
Point taken about the API. The Data Package [1] and Table Schema [2] libraries are generally designed as low-level libraries for building higher-level applications using the specifications. goodtables-py [3] is an example of a higher-level application built on top. But, point taken, we will look at it, and we'd welcome your feedback on the issue tracker [4].
[1]: https://github.com/frictionlessdata/datapackage-py/issues [2]: https://github.com/frictionlessdata/tableschema-py/issues [3]: https://github.com/frictionlessdata/goodtables-py/issues [4]: https://github.com/frictionlessdata/datapackage-py/issues
(I work on the Frictionless Data specifications and tooling at Open Knowledge International.)
CSV has many, many warts. However, it is the best thing we have right now for serialising data in a way that is easily read by humans (and consumer-grade software) and machines. Libraries like our Tabulator [1] which is used under-the-hood help provide an API to deal with many of the gotcha's when dealing with the format.