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Communicating with devices

When connecting devices to Google Cloud Platform, you will need to specify which communication protocol your devices will use. The choices are MQTT, HTTP, or both.

MQTT Protocol

MQTT is an industry-standard IoT protocol (Message Queue Telemetry Transport). It is a publish/subscribe (pub/sub) messaging protocol.

The publish/subscribe model is event-driven. Messages are pushed to clients that are subscribed to the topic. The broker is the hub of communication. Clients publish messages to the broker, and the broker pushes messages out to subscribers.

BigQuery, Google’s managed data warehouse for analytics.

Google Stackdriver, Google’s monitoring, logging, and diagnostics system

Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. In this lab we will explore the Cloud Dataprep UI to build an ecommerce transformation pipeline that will run at a scheduled interval and output results back into BigQuery.

This query will process 5.63 GB when run.

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Base R	Tidyverse	What it does and why tidyverse	Comment
read.csv()	read_csv()	reads in a csv file, but its much faster, shows progress bar for large files, can automatically parse data types	also see read_delim(), read_tsv() and readxl::read_xlsx()
sort(), order()	arrange()	sort column(n) within a data frame	see also order_by()
mtcars$mpg = …	mutate()	modify a column	see also transmute() which drops existing variables
mtcars[,c(“mpg”, “am”)], subset()	select(), rename()	select or rename columns	see also pull()
mtcars[mtcars$am == 1,], subset()	filter()	select rows based on a criterion
aggregate()	summarise(), summarize(), do()	reduce grouped values to a single value	see also varaints like summarize_if()
ifelse()	if_else(), case_when()	standand vectorized if else, but stricter than base version	see also near()
unique()	distinct()	finds unique rows in a data frame, but its much, faster

	atomicwrites==1.2.1
	attrs==18.2.0
	backcall==0.1.0
	beautifulsoup4==4.6.3
	bleach==3.0.2
	bokeh==0.13.0
	certifi==2018.11.29
	chardet==3.0.4
	Click==7.0
	cycler==0.10.0

	packages <- c(
	'dplyr', 'plyr', 'Rcpp', 'chron', 'base64enc', 'data.table', 'reshape2',
	'shiny', 'ggplot2', 'rstan', 'RMySQL', 'RPostgreSQL', 'ggmap', 'mapproj',
	'curl', 'RGtk2', 'rattle', 'httr', 'devtools', 'RODBC', 'ibmdbR', 'rgdal',
	'rmarkdown'
	)

	install.packages(
	packages,
	repos = 'http://cran.r-project.org/',

	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [