A Look at 2017’s Big Data & Machine Learning Database Up & Comers

Apps utilizing faster, more complex analytics are continuously ingesting, processing, analyzing, and either informing or automating actions. Relentless invention is keeping the big data and machine learning markets from coalescing to end-to-end platforms. The door is open for new database players, but only those that successfully and consistently tie technology to use cases will make it through. Traditional databases haven’t or can’t keep up with all three new requirements: scale-out elasticity and pricing that is orders of magnitude lower to accommodate larger data volumes, and increasingly advanced analytics.

A Look at 2017’s Big Data & Machine Learning Database Up & Comers

Apps utilizing faster, more complex analytics are continuously ingesting, processing, analyzing, and either informing or automating actions. Relentless invention is keeping the big data and machine learning markets from coalescing to end-to-end platforms. The door is open for new database players, but only those that successfully and consistently tie technology to use cases will make it through. Traditional databases haven’t or can’t keep up with all three new requirements: scale-out elasticity and pricing that is orders of magnitude lower to accommodate larger data volumes, and increasingly advanced analytics.

The Container Ecosystem Accelerates Up Its Maturity Curve

Premise Containers, generally–and Docker, in particular–are increasingly ready for prime-time enterprise development.  The Docker ecosystem continues to mature as it adds crucial elements to its framework, converges on a common industry reference architecture, and improves the user-, developer-, and administrator-friendliness of core capabilities. However, the application services segment of the commercialized Docker ecosystem is still […]