Kill some time as you escape from the family and find out what you should be on the look out for in 2017.
BashoCEO, Adam Wray
“In 2017, NoSQL’s coming of age will be marked by a shift to workload-focused data strategies, meaning executives will answer questions about their business processes by examining the data workloads, use cases and end results they’re looking for.
“This mindset is in contrast to prior years when many decisions were driven from the bottom up by a technology-first approach, where executives would initiate projects by asking what types of tools best serve their purposes. This shift has been instigated by data technology, such as NoSQL databases, becoming increasingly accessible.
“In 2017, organizations will stop letting data lakes be their proverbial ball and chain. Centralized data stores still have a place in initiatives of the future: How else can you compare current data with historical data to identify trends and patterns?
“Yet, relying solely on a centralized data strategy will ensure data weighs you down. Rather than a data lake-focused approach, organizations will begin to shift the bulk of their investments to implementing solutions that enable data to be utilized where it’s generated and where business process occur – at the edge.
“In years to come, this shift will be understood as especially prescient, now that edge analytics and distributed strategies are becoming increasingly important parts of deriving value from data.”
Vinay Joosery, CEO Severalnines
Database innovation, what will we see in 2017?
“One of the more salient points businesses should consider as we’re moving to an increasingly proliferated cloud deployment market is driven by ‘Data Gravity’. Apps and services do not run very well when data is separated from them, they require proximity to data.
“The large cloud players like AWS, Azure and IBM are wise to this and are expanding their capabilities to accommodate this concerted effort towards cloud with DBaaS offerings and we’ll see a lot more of this movement throughout 2017.
“This does bring in another issue which has had some publicity and that’s the problem of cloud lock-in. Some believe vendor lock-in does not exist anymore thanks to subscription-based business models. Having all your data reside in one cloud, accompanied by the notion of ‘Data Gravity’, means your applications would also tend to gravitate to that cloud.
“Eventually, you become dependent on the tools provided by the given cloud vendor. This may result, as businesses become aware of this, in the diversifying of their infrastructure, so they don’t put all their eggs in one basket.”
Hortonworks Chief Technology Officer, Scott Gnau
Real Time Machine Learning and Analytics at the Edge
“In 2017, ‘centralized-only’ monolithic software and silos of data disappear from the enterprise. Smart devices will collaborate and analyze what one another is saying. Real time machine-learning algorithms within modern distributed data applications will come into play – algorithms that are able to adjudicate ‘peer-to-peer’ decisions in real time.
“Data has gravity; it’s still expensive to move versus store in relative terms. This will spur the notion of processing analytics out at the edge, where the data was born and exists, and in real-time (versus moving everything into the cloud or back to a central location).”