Data that can’t be easily queried or read is like a book with the pages glued together: not very useful. For decades, SQL was the established language used to interact with databases. Then data management requirements necessitated by cloud environments and big data use cases led to new systems. These

Introduction It wasn’t that long ago that the idea of a network-connected device that fit in your pocket was novel and futuristic. Things change quickly, however, and today’s IT professional not only has to assume that every user is connected in real-time, but be prepared for every electronic device to

Andrew joined Etsy in 2014, and lives in London, making him their first data scientist who lives outside the USA. Prior to Etsy he spent almost 15 years designing machine learning workflows, and building search and analytics services, in academia, startups and enterprises, and in an ever-growing list of research

During one of our data-munging sessions here at Coralogix, we found ourselves needing to assess the cardinality of large data sets. Getting the accurate result is seemingly trivial: you simply iterate over the data, and count the number of unique elements. In reality, however, the task is more troublesome, mainly

How did you end up working with Cloud technology, and what excites you about the future for it? I started my career in hosting technology back in the mid 90s, right when the first dot-com boom began. As hosting technology matured and naturally evolved into cloud, so did my career.

Big data was once the domain of big iron and big brand frameworks, but in recent years, data processing has relied on x86-based server farms based on CPU-driven architectures. Yet when it comes to real-time performance, these systems just don’t hold up. Von Neumann computing architectures – most commonly seen

‘Streams in the Beginning, Graphs in the End’ is a three-part series by Dataconomy contributor and Senior Director of Product Management at Cray, Inc., Venkat Krishnamurthy – focusing on how big changes are afoot in data management, driven by a very different set of use cases around sensor data processing. In part I, we talked about

Ignacio Elola is a self-proclaimed ‘data nerd, data punk and data scientist’ at import.io , a young startup that’s shaking up the world of data. With their free app, you can transform any website into a table of data or an API in minutes. Recently voted Best Startup by O’Reilly Strata Santa Clara,

‘Streams in the Beginning, Graphs in the End’ is a three-part series by Dataconomy contributor and Senior Director of Product Management at Cray, Inc., Venkat Krishnamurthy – focusing on how big changes are afoot in data management, driven by a very different set of use cases around sensor data processing. In this first part, we’ll talk

Powering real-time applications involves scaling not only existing enterprise applications, but also new applications that have emerged from the web, social media, and mobile devices. Overwhelmed by massive data growth, businesses must carefully select cost-effective technologies that can enable applications to easily manage both the data volumes of today and