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@DGrady
DGrady / oracle-query.org
Last active March 21, 2024 11:57
Example of querying an Oracle database using Python, SQLAlchemy, and Pandas

Query Oracle databases with Python and SQLAlchemy

N.B. SQLAlchemy now incorporates all of this information in its documentation; I’m leaving this post here, but recommend referring to SQLAlchemy instead of these instructions.

Install requirements

  1. We’ll assume you already have SQLAlchemy and Pandas installed; these are included by default in many Python distributions.
  2. Install the cx_Oracle package in your Python environment, using either pip or conda, for example:
@vancluever
vancluever / gnome-tracker-disable.md
Last active May 2, 2024 16:26
GNOME Tracker Disable

Disabling GNOME Tracker and Other Info

GNOME's tracker is a CPU and privacy hog. There's a pretty good case as to why it's neither useful nor necessary here: http://lduros.net/posts/tracker-sucks-thanks-tracker/

After discovering it chowing 2 cores, I decided to go about disabling it.

Directories

@onefoursix
onefoursix / impalaQueries.py
Last active January 16, 2021 23:30
Python CM-API Example to pull Impala Query metrics
#!/usr/bin/python
## *******************************************************************************************
## impalaQueries.py
##
## Getting Info on Impala Queries
##
## Usage: ./impalaQueries.py
##
## *******************************************************************************************
@hadley
hadley / ds-training.md
Created March 13, 2015 18:49
My advise on what you need to do to become a data scientist...

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

  • Statistical knowledge
  • Programming/hacking skills
  • Domain expertise

Statistical knowledge

@sayak-sarkar
sayak-sarkar / sublime-text-3.sh
Last active July 14, 2022 09:18
Gist to install Sublime Text 3 on RHEL 6 +
#!/bin/sh
SHORTCUT="[Desktop Entry]
Name=Sublime Text 3
Comment=Edit text files
Exec=/opt/sublime_text_3/sublime_text
Icon=/opt/sublime_text_3/Icon/128x128/sublime-text.png
Terminal=false
Type=Application
Encoding=UTF-8
Categories=Utility;TextEditor;"
@chenzx
chenzx / 20140724-web-operations-notes.txt
Last active December 22, 2021 10:34
网站运维技术与实践
网站运维技术与实践
跳转至: 导航、 搜索
目录
1 服务器监测
2 产品访问监测
3 数据采集、传输与过滤
4 数据分析与报警
5 测试评估
6 集群架构规划
@v5tech
v5tech / Ganglia监控Hadoop及Hbase集群.md
Last active December 2, 2021 09:04
Ganglia监控Hadoop及Hbase集群性能(安装配置)

Ganglia监控Hadoop及Hbase集群性能(安装配置)

1. 在主节点上安装ganglia-webfrontend和ganglia-monitor

sudo apt-get install ganglia-webfrontend ganglia-monitor

在主节点上安装ganglia-webfrontend和ganglia-monitor。在其他监视节点上,只需要安装ganglia-monitor即可

将ganglia的文件链接到apache的默认目录下

@JosefJezek
JosefJezek / how-to-use-pelican.md
Last active May 12, 2024 11:19
How to use Pelican on GitHub Pages
@dsparks
dsparks / Heatmap.R
Last active August 19, 2022 06:54
ggplot2 heatmap with "spectral" palette
doInstall <- TRUE # Change to FALSE if you don't want packages installed.
toInstall <- c("ggplot2", "reshape2", "RColorBrewer")
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")}
lapply(toInstall, library, character.only = TRUE)
# Generate a random matrix
# This can be any type of numeric matrix,
# though we often see heatmaps of square correlation matrices.
nRow <- 9
nCol <- 16
@hellerbarde
hellerbarde / latency.markdown
Created May 31, 2012 13:16 — forked from jboner/latency.txt
Latency numbers every programmer should know

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs