FME Challenge: Choosing the Right Transformer (Webinar Tie-In)

I submitted #2 so I thought I add my solution here for reference.

First off I build a web scraper using http caller and html extractor.

I ended up not using the this method and switching to an API to get this data.

I can post more of this solution if anyone wants but I'm still finishing off the the youtube upload due to an issue outside of FME.

Scenario 1: (I know its a bit late)

To read the data, to create point features and choose attributes I would use a Python Creator with following code:

import fmeobjects
import requests
from xml.etree import ElementTree as ET

# Template Class
class FeatureCreator(object):
    def __init__(self):
        pass
    def input(self,feature):
        Path = r"https://tfl.gov.uk/tfl/syndication/feeds/cycle-hire/livecyclehireupdates.xml"
	colList = ["name", "terminalName","lat","long","nbBikes","nbEmptyDocks","nbDocks"]
        response = requests.get(Path)
        root = ET.fromstring(response.content)

        for child in root:
            newFeature = fmeobjects.FMEFeature()
            for grandchild in child:
		if grandchild.tag in colList: 
                	newFeature.setAttribute(grandchild.tag, grandchild.text)
            self.pyoutput(newFeature)
    def close(self):
        pass 

I thought I'd just upload the first bit of XML reading that I created during the webinar. It doesn't address the metadata, but it reads the feed and splits the data into attributes.

I used a Textfile reader with the parameter set to read the entire feed into a single attribute, and then fragmented the data with an XMLFragmenter transformer.

The XMLFragmenter will read directly from the XML feed, but I used a text reader because then I could do a partial run on the XMLFragmenter without having to reread the feed.

Workspace - uses the latest 2018.0 version (but should be OK with any 2018.0)