What if I told you that you can create an out-of-the-box active-active, federated PostgreSQL cluster on Kubernetes?

Since logical decoding was introduced in PostgreSQL 9.4, I have been fascinated by the various applications it has. In fact, I've used this feature to apply the concepts of change data capture

The PostGIS raster extension has a steep learning curve, but it opens up some unique possibilities for data analysis and accessing non-standard data from within PostgreSQL. Here's an example that shows how to access raster data from PostGIS running on Crunchy Bridge

I'm not someone who spends a lot of time or energy on digital images and photography. I'm usually set with my phone's camera app and maybe applying a filter as I upload to Instagram. But when you work at a database company like Crunchy Data, anything in your life can inspire a demo application. How about a simple image upload app built with Django 3.1

In our last blog post on using Postgres for statistics, I covered some of the decisions on how to handle calculated columns in PostgreSQL. I chose to go with adding extra columns to the same table and inserting the calculated values into these new columns. Today’s post is going to cover how to implement this solution using PL/pgSQL

While we talk about "PostGIS" like it's one thing, it's actually the collection of a number of specialized geospatial libraries, along with a bunch of code of its own.

• PostGIS provides core functionality
  • bindings to PostgreSQL, the types and indexes

Greetings readers, today we're going to take a semi-break from my “doing data science in SQL” series to cover a really cool use case I just solved with regular expressions

Crunchy Data's second annual PostGIS Day took place a couple weeks ago on November 19th, and as a first-time attendee I was blown away by the knowledge-sharing and sense of community that I saw, even as I was tuning in remotely from my computer at home. This year's PostGIS Day

In the last two blog posts on data science in Postgres, we got our data ready for regression analysis and had predictive variables that are on wildly different scales. Another example of data on different scales would be annual income versus age. The former is usually at least tens of thousands while age rarely gets to a hundred.

If you do the regression with non-transformed variables, it becomes hard to compare the effect of the different variables. Statisticians account for this by converting raw data values into a Z-score

At most of the places I've worked, the primary language used was not what I gravitated to naturally. If you're going to ask for a language of choice personally, it's python. I appreciate the explicit nature, that it's often pseudocode that can execute and it has a rich ecosystem of libraries (though that’s most languages these days). But as much as anything I latched onto Django

Summarizing data against a fixed grid is a common way of preparing data for analysis. Fixed grids have some advantages over natural and administrative boundaries:

• No appeal to higher authorities
• Equal unit areas
• Equal distances between cells
• Good for passing data from the "spatial" computational realm to a "non-spatial" realm