Google Domains has been a great place to manage all of my registered domains names. While there are several well-established free and paid dynamic dns services — of some which require technical knowledge or complicated setup — Google Domains supports dynamic dns natively and easily (and for free) using either a dedicated API or standards-based integration to open tools like ddclient or in-a-dyn.
In this article, you will learn how to setup dynamic DNS on any device in your home network using ddclient and Google Domains.
We’ll assume that you’ve already registered a domain name that is hosted using Google…
Admittedly, I am way too excited about stumbling upon this tool while researching another project. As I’ve gotten along in may career, I find myself at the CLI less and less. So, when I do have the opportunity to roll up my sleeves and get access to the CLI, I like to have some fun at the same time ;)
Install and configure the GRC (Generic Colouriser) to add a little color to existing CLI commands.
GRC is available via GitHub as well as packaged for many LINUX distributions. …
The ability to connect BigQuery to Google Sheets as a data source may be one of the most powerful, yet underutilized features for quickly and easily integrating small data into your enterprise data warehouse. For example, Google Forms is a quick and dirty way to securely obtain information from your users and supports storage in Google Sheets — which in turn can be exposed directly to BigQuery in realtime and without the overhead of ETL.
I had been successfully querying Drive data in BigQuery both at home and at work for quite some time — until it STOPPED WORKING…..
Install the Google Cloud SDK on a Raspberry Pi to access and interact with your Google Cloud Platform projects via a Service Account. In this example, we’ll create a Service Account with access to load speedtest result data into Google BigQuery.
Google Cloud official documentation for creating service accounts.
Using the Cloud Console, we’ll create a service account with access to load data into BigQuery for our particular project.
First — Navigate to the Cloud Console Service Account menu using the link below or by selecting IAM & Admin →Service Accounts.
I’ve been working on a few home automation projects using historical weather data from the Big Query NOAA Public Weather Dataset to schedule automations, estimate energy usage, or predict when I’ll get my next home heating oil delivery. To take things a step further, I need to know the average mean temperature on a given date.
To do this, we’ll take the last 20 years of NOAA weather data for a given weather station and calculate the mean mean ;) (average temperature by day using the mean temperature values per day). …
Google Sheets, Big Query, and Public Data Sets — Calculating Degree Days and K-Factor.
Have you ever wondered how home heating fuel companies know when to make a delivery? Even if you don’t get propane or home heating oil deliveries, you can still estimate your fuel usage over time by using the same formula used by energy companies by leveraging public weather datasets in BigQuery and your past heating bills.
Much like tracking fuel economy (measured by miles per gallon) in your vehicle, energy companies use a formula that relies on historical weather data (measured in degree days) and a…
Use BigQuery Public Datasets, Geography Functions, Wildcard Tables, and ARRAY_AGG & UNNEST to locate and query local historical weather data near any address.
While working on a smart home data project, I found myself searching for public weather data. It turns out that Google BigQuery has a ton of weather data in the NOAA GSOD (Global Surface Summary of the Day Weather Data) Public Datasets — comprised of daily weather data from approximately 30,000 distinct weather stations worldwide.
With the sudden shift to remote learning in the spring due to COVID-19, Students and Educators were left scrambling to find ways to salvage the remainder of the school year. One of the biggest challenges was connecting students with the software tools installed on school computers — including specialized software that is often too expensive to distribute to students or too resource intensive to run on home computers.
Many schools quickly adopted expensive SaaS (Software as a Service) solutions that loaded some specialized software into cloud computing environments that Students and Teachers could access from home. Others cobbled together wonky…