As a whole, big data cloud initiatives seem to be accelerating at major cloud providers like Google, Amazon, IBM, Microsoft, Oracle, Rackspace and Salesforce. The moves potentially make it easier for data scientists and businesses to embrace big data solutions without necessarily needing to buy on-premises hardware or software.
Google's latest moves include continued Cloud Dataflow enhancements -- which allow customers to execute large-scale data processing scenarios such as ETL, analytics, real-time computation, and process orchestration. Moreover, Google BigQuery -- an API-driven service for SQL analytics -- is now available in Europe. And Google Cloud Pub/Sub is designed to provide scalable, reliable and fast event delivery as a fully managed service, the search giant asserts.
Still, Google faces plenty of competition as customers increasingly seek to run big data workloads in public clouds. Earlier this week, Hortonworks acquired SequenceIQ to help customers run Hadoop as a service. And Amazon Machine Learning launched last week -- accelerating Jeff Bezos's effort to make artificial intelligence a low-cost technology for all types of customers.
The cloud efforts have also gone vertical -- as IBM and Apple recently partnered on Watson Health Cloud-- a new platform that multiple health care providers and medical companies plan to plug into.
Smaller, privately held companies also are getting into the act. One prime example is Qubole, which provides Hadoop as a Service and big data tools across most major public clouds. Qubole CEO Ashish Thusoo explained Qubole's strategy in this podcast.
The overall big data landscape seems to be getting crowded -- even in the cloud, where numerous companies now offer Hadoop as a Service. Still, it is very early in the Hadoop market. And only a few big public cloud providers can afford to build out complete suites of big data tools. Google certainly ranks among them.
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