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Amazon Web Services turned a lot of heads recently when it launched a machine learning platform aimed at making predictive analytics applications easy to build and run, joining cloud juggernauts Microsoft and Google with similar ML offerings. It turns out the cloud is very well-suited for this critical type of big data workload. Here are five reasons why.
Google launched a couple of updates to its cloud-based big data products at the Hadoop Summit in Brussels on 16 April. These included the launch of the open beta of Cloud Dataflow, Google’s new service for processing huge amounts of data, as well as an update to BigQuery, which will make the company’s bid data database service available in Google’s European data centers and introduces row-level permissions.
The growth of cloud computing has seen a number of businesses question whether on-premise solutions are still a viable approach to take. According to SoftwareAdvice, 88 per cent of software buyers preferred on-premise solutions in 2008, while in 2014, 87 per cent preferred cloud services.
Today, it seems the cloud is the answer to every question. Where are my photographs? How does this app work? How will our management strategies change with the cloud? It wasn't always thus. So how did the cloud come to develop us all in its fluffy ubiquity? check IBM's inforgraphic.
For software companies, cloud computing offers a modern world of business opportunity, allowing them to provide their application not only as a product but as a service. Thus, they will enjoy, among other advantages, of agility and efficiency.
Migrating software to cloud infrastructure may be sufficient much of the time. But residing in the cloud is not enough to make an application fully scalable, elastic and ‘cloudy’. The software code itself must be modernized if maximum performance and efficiency are required.