The big three vendors of the cloud IaaS market – Amazon, Microsoft and Google – used to spat over price cuts, but now they’ve graduated to trading barbs on their features.
The trend is not new - read more about it here - but the last couple of weeks have been a microcosm of this issue, with vendors attempting to one-up each other’s cloud-based big data platforms.
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Earlier this month Amazon Web Services released a new service named Amazon Machine Learning, which allows users to import data and it will spit out algorithms that can predict future data based on past information.
A week later, Microsoft and Google had rebuttals to that news. Google released the beta of a new service named Cloud Dataflow – allowing customers to create “data processing pipelines” that scale dynamically based on the amount of data that’s loaded into it. Google says this is ideal for large-scale data processing scenarios such as ETL, analytics, real-time computation and process orchestration.
The day after Google’s news, Microsoft released its own big data announcement with the general availability of Azure Stream Analytics. Microsoft pitches Stream Analytics as a tool for processing “Internet of Things” data by “helping uncover real-time insights from devices, sensors and applications.”
Streaming analytics and machine learning tools can mean big money for these vendors, which is one reason they are making a big deal about big data. The more data stored in their clouds, the more money customers pay them.
From a customer perspective, it can make sense to use these platforms though. Instead of investing in the infrastructure needed to store all the data, it can be housed in the cloud. Instead of investing in expensive machine learning and data analytics software that would be installed, it can be spun up in the cloud and paid for by the hour.
Expect these vendors to continue to make the argument that your big data should be in its cloud. It’s the latest battle-ground in the fast-moving IaaS market.