Leverage Python and Google Cloud to extract meaningful SEO insights from server log data
2Reported today on Search Engine Land
For the full article visit: http://feeds.searchengineland.com/~r/searchengineland/~3/knIuKvByKZ4/leverage-python-and-google-cloud-to-extract-meaningful-seo-insights-from-server-log-data-329199
Leverage Python and Google Cloud to extract meaningful SEO insights from server log data
For my first post on Search Engine Land, I'll start by quoting Ian Lurie:
Log file analysis is a lost art. But it can save your SEO butt!
Wise words.
However, getting the data we need out of server log files is usually laborious:
Gigantic log files require robust data ingestion pipelines, a reliable cloud storage infrastructure, and a solid querying systemMeticulous data modeling is also needed in order to convert cryptic, raw logs data into legible bits, suitable for exploratory data analysis and visualization
In the first post of this two-part series, I will show you how to easily scale your analyses to larger datasets, and extract meaningful SEO insights from your server logs.
All of that with just a pinch of Python and a hint of Google Cloud!
Here's our detailed plan of action:
#1 – I'll start by giving you a bit of context:
What are log files and why they matter for SEOHow to get hold of themWhy Python alone doesn't always cut it when it comes to server log analysis
#2 – We'll then set things up:
Create a Google Cloud Platform accountCreate a Google Cloud Storage bucket to store our log filesUse the Command-Line to convert our files to a compliant format for queryingTransfer our files to Google Cloud Storage, manually and programmatically
#3 – Lastly, we'll get into the nitty-gritty of Pythoning – we will:
Query our log files with Bigquery, inside Colab!Build a data model that makes our raw logs more legible Create categorical columns that will enhance our analyses further down the lineFilter and export our results to .csv
In part two of this series (available later this year), we'll discuss more adva