The GDAL community just announced the release of GDAL 2.2… and BigSQL just made it easier to implement!

Install it Now

We support Diegos and Yodas with 2 ways to install:

Option 1: Install with the pgDevOps GUI

For GIS analysts (scientists, statisticians, analysts, etc) who find themselves as the de facto DBA (among other things).

“I don’t have time for this! I’m saving this sea turtle. Just give me a GUI!”

1. Go to and download and run the installer for your operating system.

2. Make sure to check the box for including the pgDevOps component with your install.

3. Open pgDevOps in your web browser:

    <ip of machine>:8051

If installing on local machine:

4. In the pgDevOps dashboard click the Package Manager icon.

5. Click the PostgreSQL installation you want to install on.

6. Click on the PostGIS icon and follow the instructions to install.

That’s it!

Option 2: Install via Command Line and PGC

For DBAs who find themselves supporting these strange spatially enabled databases.

“Real men only use command line! And what the hell is GIS?”

  1. Install pgc via command line to a sandbox.
    *Note: this command will install the BigSQL distribution into the directory you are currently located.

    MAC / Linux:

    python -c "$(curl -fsSL" 


    @powershell -NoProfile -ExecutionPolicy unrestricted -Command "iex ((new-object net.webclient).DownloadString(''))"
  2. Get into the product home directory by navigating via command line to the bigsql directory. Windows users don’t prefix the pgc command with ./ as shown in the below examples:

    cd bigsql
  3. Using the pgc command line tool, run the update command to get the lastest distribution:

    ./pgc update
  4. Then run the install, init, and start commands on pg96:

    ./pgc install pg96 
    ./pgc start pg96
  5. Next, install postgis:

    ./pgc install postgis23-pg96

That’s it!

The GDAL_DATA Directory Path

In order for all your PostGIS extensions to properly work, you will need to set the GDAL_DATA environment variable to the location of the directory gdal.

Lucky for you, with the BigQL distribution this is as easy as running the following commands via a command line tool.


 cd <directory of installation>/pg96
 source pg96.env


 cd <directory of installation>\pg96

Both of these commands set environment variables that will live during your current session. Note that if you close your terminal or command prompt, these variables are removed from memory.

It’s also possible to set GDAL_DATA as a persistant environment variable. But that is the beyond the scope of this tutorial.

To check to see what version of GDAL you have installed:

gdalinfo --version


In 1998, Frank Warmerdam began developing GDAL, which stands for Geospatial Data Abstraction Library. The official definition, found on, is as follows:

GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats. It also comes with a variety of useful command line utilities for data translation and processing.

Clear as mud, right? Let me refer to a better definition written by Planet Labs’ Robert Simmon in the post, A Gentle Introduction to GDAL, Part 1. I higly recommend reading the entire article:

Even the description is complicated. Put another way, GDAL (pronounced ‘gee-dal’ not ‘goodle’ (too close to Google!)) is a collection of software that helps with the translation of data from different file formats, data types, and map projections. It’s used in free software like QGIS and GRASS, and even some commercial applications like ESRI ArcGIS and Avenza Geographic Imager/MAPublisher.


To make things even a bit more confusing, OGR (which I discussed in an earlier post, is part of GDAL:

In theory it is separate from GDAL, but currently they reside in the same source tree and are somewhat entangled.

So what’s the difference between GDAL and OGR?

The GDAL library is historically for raster data extract, transform, load (ETL) processes, while OGR supports vector data. Sometimes people will interchange the names GDAL, OGR, and GDAL/OGR to refer to the same thing. I know… geeks and their esoteric acronyms, right?

Regardless of the complexities, I can’t overemphasize how essential it is to add GDAL/OGR libraries to your spatial data management toolkit!