Cloudera Unveils CDP, Talks Up ‘Enterprise Data Cloud’
Cloudera today shed more light on its strategy to develop a unified flagship offering called Cloudera Data Platform, which is core to its emerging “enterprise data cloud” strategy. It also announced plans to maintain existing Hortonworks and Cloudera platforms into 2022 and to cross-pollinate existing products in the meantime.
Cloudera‘s new CDP platform will run and be supported on-premises, on private clouds, on the five biggest public clouds run by Amazon, Microsoft, Google, IBM, and Oracle, and any combination thereof, the company said today. The timeline for delivery of CDP was not disclosed.
The company, which completed its merger with Hortonworks last week, revealed that it’s planning two iterations of CDP, which the company had previously referred to as its “Unity” release. The first release of CDP will be composed of a selection of elements from Hortonworks Data Platform (HDP) version 3.x and Cloudera’s Distribution of Hadoop (CDH) 6, and will be focused on running customers’ existing workloads and data, said Cloudera’s Chief Product Officer Arun Murthy, who was a co-founder of Hortonworks.
“Once we’ve delivered that and got past it, we then want to get to a second subsequent version, which you can start to upgrade and migrate to, and that will be the go-forward platform,” he said. “Obviously the key part of CDP is delivering not just the workloads you have today but new and intuitive experiences around key workloads such as data warehousing, data flow, the edge or streaming, AI and machine learning.”
The company also announced that CDH 5.x and 6.x and HDP 3.x will be supported through January 2022, which is in-line with previous guidance the company has given. This company believes that three years is plenty of time for customers to plan their migration paths from older CDH and HDP versions to the unified CDP product. Support for HDP 2.x will end before that time.
The other bit of news coming out today is that Hortonworks Data Flow (HDF) will be integrated with CDH, thereby giving CDH customers the ability to benefit from Hortonworks’ real-time data ingest and streaming technology before the unified CDP product is delivered.
Likewise, there will be integration between HDP and Cloudera Data Science Workbench (CDSW), the company’s flagship machine learning and AI development environment. This will let HDP customers start adopting CDSW in advance of the HDP product. (Interestingly, there was no news about CDH customers utilizing IBM’s Data Science Experience product, which was Hortonworks favored ML and AI development environment, although few really expected Cloudera to support it.)
In addition to providing a place to run SQL analytics and machine learning workloads, the CDP platform will help customers manage all the data in a secure and governed manner, which is no trivial task across so many delivery models.
“A key part of this vision is to help you run all these workloads and manage all this data, on prem, on private clouds, and also on multiple public clouds,” Murthy said. “A big part of this is to have consisted security and governance experiences, so that you don’t have to worry about moving your data or your workload from multiple public clouds, or from on prem to the public cloud
The cloud looms large in Cloudera’s go-forward strategy, and in fact is the number one area of investment and growth, said Cloudera Tom Reilly. But Cloudera’s strategy is not wholly based on the cloud, because that’s not what organizations want.
“Nearly every organization is headed to the public cloud,” Reilly says. “They love the simplicity and elasticity the cloud offers. But they also recognize a strategy based solely on public cloud services can be a very expensive choice, in both near term unexpected operating costs and longer term lock-in.
“It’s clear that customers want both — cloud choice and cloud experience,” Reilly continued. “We agree. At Cloudera, we have always embraced the power of the cloud. We believe that machine learning and analytics, from edge to AI, operating consistently and seamlessly across all clouds, both public and private, is exactly what enterprise want. It’s what we call the enterprise data cloud.”
Cloudera wants to help customers build intelligent applications that span multiple disciplines and delivery models, which is not an easy task when accomplished at scale, said Hilary Mason, the general manager of machine learning at Cloudera.
“At Cloudera we see winning organizations embedding machine learning and AI across their business to improve customer expiree, automate operation, reduce risk and create real value,” she said. “This isn’t about building one applicant or model. Machine learning excellence requires the team, organization, and infrastructure to build and manage hundreds or even thousands of applications and models.”
“This requires the ability to experiment and take action quickly, lowering the cost of prediction across the [organization],” she continued. “At Cloudera, we refer to this trend as the industrialization of AI. And it’s our strategic focus, enabling a reality where the process of identifying the opportunity of applying machine learning, then building, deploying and maintaining those solution is fast and easy, and also well understood and consistent across the business.”