Latest posts from Joe Conway

Recently I gave a "deep dive" talk on the topic of PostgreSQL security, and I wanted to capture one part of that content into a blog since this format is both better for making that content stand on its own and for expanding on it a bit.

Specifically, in this two-part blog, we will cover a PostgreSQL extension that I wrote called crunchy_check_access

I want to take a few minutes for a deep dive into the effect your data model has on storage density when using PostgreSQL. When this topic came up with a customer, I explained my thoughts on the matter, but I realized at the time that I had never done a reasonably careful apples-to-apples test to see just exactly what the effect is, at least for a model sample size of one. So here it is.

Base Use Case

This is the third and final post of the series intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. The information below provides sample use of R Functions against the NDVI dataset.

As introduced in the previous posts, the combination of PostgreSQL and R provides users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. It is our hope that these posts will cause those building analytic applications to give PostgreSQL a second look.

The first post in this series

This is the second in a series of posts intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language.  This post builds on the example introduced in the initial post by demonstrating the steps associated with preprocessing the Normalized Difference Vegetation Index

This is the first in a series of posts intended to introduce PostgreSQL users to PL/R, a loadable procedural language that enables a user to write user-defined SQL functions in the R programming language. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database. This initial post introduces PL/R and R, provides set up instructions for following the Spatial Analytics example to be used in this series of posts, and provides introductory instruction on Geocoding with PostGIS, R and PL/R.

While PostgreSQL is known for and widely popular as a transactional database due to its SQL compliance, reliability, data integrity and ease of use, it is less commonly associated with analytic applications.

The combination of PostgreSQL and R provide users with the ability to leverage the power and efficiency of PostgreSQL and the rich analytic functionality of R. When further combined with PostGIS, the geospatial extender for PostgreSQL, users can perform powerful spatial analytics within the PostgreSQL database.

This series of blog posts will provide users with information about:

• Introduction to R and PL/R
• Geocoding with PostGIS, R and PL/R
• Example of Spatial Analytics – NDVI Processing
• Example of Spatial Analytics – Sample Analytics Functions

Crunchy Data recently announced the publication of the PostgreSQL Security Technical Implementation Guide (STIG) by the United States Defense Information Systems Agency (DISA), making PostgreSQL the first open source database to provide a published STIG.

While the STIG was authored for the benefit of the U.S. Government, the DISA PostgreSQL STIG offers security-conscious enterprises a comprehensive guide for the configuration and operation of open source PostgreSQL. Enterprises can refer to the STIG as for guidance on PostgreSQL security best practices they consider open source PostgreSQL as an alternative to proprietary, closed source, database software.

Importantly, compliance with the STIG guidance requires only open source software and documentation. The PostgreSQL STIG is based on open source, unmodified PostgreSQL 9.x used in conjunction with certain open source PostgreSQL extensions – most notably, pgaudit