$ uname -r
| node: Platform built on V8 to build network applications | |
| git: Distributed revision control system | |
| wget: Internet file retriever | |
| yarn: JavaScript package manager | |
| python3: Interpreted, interactive, object-oriented programming language | |
| coreutils: GNU File, Shell, and Text utilities | |
| pkg-config: Manage compile and link flags for libraries | |
| chromedriver: Tool for automated testing of webapps across many browsers | |
| awscli: Official Amazon AWS command-line interface | |
| automake: Tool for generating GNU Standards-compliant Makefiles |
| //PARTICLE DEVICE | |
| //Define the Particle Photon's Device ID and Access Token. Then use those variables to create the URL to access JSON output from board. | |
| var deviceID = "3e002..................."; //Set to specific device ID. | |
| var accessToken = "6dae...................................."; //Set to specific device's access token. | |
| var particleUrl = 'https://api.particle.io/v1/devices/' + deviceID + '/analogvalue?access_token=' + accessToken; | |
| //WEATHERUNDERGROUND | |
| //Define WeatherUnderground API key, weather station location and request type. Then create URL to access JSON results. |
This is a collection of the most common commands I run while administering Postgres databases. The variables shown between the open and closed tags, "<" and ">", should be replaced with a name you choose. Postgres has multiple shortcut functions, starting with a forward slash, "". Any SQL command that is not a shortcut, must end with a semicolon, ";". You can use the keyboard UP and DOWN keys to scroll the history of previous commands you've run.
http://www.postgresql.org/download/linux/ubuntu/ https://help.ubuntu.com/community/PostgreSQL
A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. It's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.
###Dataflow Engines:
Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf
Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf