In more recent years, various Western players touting tech trends like artificial intelligence and predictive analysis have swooped in with promises to solve the region's myriad problems. But Garbal--named after the word for a livestock market in the language of the Fulani, an ethnic group that makes up the majority of the Sahel's herders--aims to do things differently. Building on an approach pioneered by a 37-year-old American data scientist named Alex Orenstein, Garbal is focused on how humbler technologies might effectively support the 80% of Nigeriens who live off livestock and the land. "There's still this idea of'How can we use new tech?' But the tech is already there--we just need to be more intentional in applying it," Orenstein says, arguing that donor enthusiasm for shiny, complex solutions is often misplaced.

It's time to dispel some myths surrounding SQL. That's the message from MemSQL, a scalable real-time Data Warehouse that is designed to ingest and transform millions of events of data per day, while simultaneously analyzing billions of rows of data using standard SQL. As that description makes clear, there's no reason to believe that there's no such thing as scalable SQL, according to Gary Orenstein, Senior VP for Products at MemSQL. One of the oft-cited reasons for moving from SQL to NoSQL is concern that SQL solutions can't scale, Orenstein says. But today, there's a renewed awareness that it is possible to scale SQL, partially thanks to Google's Cloud Spanner globally distributed, relational database service that counts among its features horizontal scaling.

Join Steven Camiña of MemSQL for "Building the Ideal Stack for Machine Learning," where he'll share how to use real-time data for machine learning. Machine learning has been a mainstream commercial field for some time now, but it's going through an important acceleration. In this podcast episode, I talk about that acceleration with two executives from MemSQL, a company that specializes in in-memory databases: Gary Orenstein, MemSQL chief marketing officer, and Drew Paroski, MemSQL vice president of engineering. Orenstein and Paroski identify a few crucial inflections in the machine learning landscape: machine learning models have become easier to write; computing capacity on the cloud has increased dramatically; and new sources of data--everything from drones to smart-home devices and industrial controllers--have added new richness to machine learning models. Computing capacity and software progress have made it possible to train some machine learning models in real time, says Orenstein: "given enough time in computing, you can do just about anything, but only recently have people been able to apply these machine learning models in real time to critical business processes."