Shared data machine learning PaaS kicks off from Cloudera
|Richard Harris in Cloud Services Tuesday, March 13, 2018|
Machine learning and analytics PaaS has been released by Cloudera to provide users the ability to use shared data.
SDX enables Altus cloud services - including Data Engineering, Analytic Database (beta) and soon Data Science - to securely access data through a reliable shared data experience. There is one trusted source of metadata for all machine learning and analytics services and users. Altus brings the simplicity and scale of the cloud to big data analytics, enabling people to confidently utilize multiple analytics services to unlock the value in their business data. Altus delivers IT control through simplified workload management, governance, and security, while catering to the end user by offering curated self-service access to data and their preferred tools.
Industry analysts have identified big data analytics cloud sprawl as a growing problem in enterprises. The disparate cloud services spun up as “shadow IT” by different teams present IT and organizational challenges because the discrete models and fragmented approaches are usually too narrow and not scalable to manage within the company. It also leads to increased cost, effort, and compliance challenges associated with ungoverned data replication and access.
According to IDC MarketScape, Asia/Pacific Big Data and Analytics Platform 2017 Vendor Analysis, “In 2017 and beyond, IT buyers, which include the various LOBs considering investing in big data and analytics and cognitive computing, would have to consider more than just a single use case within their respective business units. BDA (Big Data Analytics) has been well established on the ROIs and relative ease at which each individual business unit is able to adopt a BDA solution and rapidly apply it within their environment. The common challenge faced is when attempting to scale or replicate success achieved to more LOBs or function groups.”
“Cloudera Altus with SDX enables businesses to build and manage multi-function analytics use cases in the cloud, integrating data engineering, IoT, customer and operations analytics, with machine learning,” said Vikram Makhija, general manager, Cloud Business Unit, at Cloudera. “Cloudera offers a proven solution for businesses to capitalize on the value of their data, avoiding the analytics cloud sprawl problem through the simplicity and scale of Cloudera’s modern cloud platform for machine learning and analytics.”
Clouds are distributed technology platforms that leverage sophisticated technology innovations to provide highly scalable and resilient environments that can be remotely utilized by organizations in a multitude of powerful ways. To successfully build upon, integrate with, or even create a cloud environment requires an understanding of its common inner mechanics, architectural layers, and models, as well as an understanding of the business and economic factors that result from the adoption and real-world use of cloud-based services.
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