A would-be data management juggernaut got its first public airing as Cloudera -- a combination of formerly separate Hadoop pioneers Cloudera and Hortonworks -- as the newly stand-alone vendor's leaders publicly mapped the road it intends to take forward. "The combination has made sense for many years," said Tom Reilly, CEO of the combined companies, who held a similar role at the former Cloudera. Others agreed these leaders in open-source-oriented big data tooling -- built along lines drawn by big web companies, such as Google and Yahoo -- are better together than apart and can offer users a unified big data platform. Reilly spoke as part of a prerecorded webcast heralding the new company, which came after confirmation that shareholders of Cloudera and Hortonworks had approved a merger of the firms -- a deal first disclosed last October. Cloudera faces distinct challenges, as it moves data applications to the cloud and tries to convey users to the fast-growing new world of machine learning and AI.
Cloudera is betting that it can fuel future growth by becoming critical to deploying, managing and governing machine learning models across enterprises and industries. The company said its Cloudera Machine Learning MLOps suite is now generally available. The effort goes along with its Cloudera Data Platform (CDP) and plays into the company's plan to become more than a Hadoop distribution player. Cloudera merged with Hortonworks last year and set a strategy to manage analytics workloads. The general theory is that Cloudera can be a single pane of glass for multiple data analytics workloads using various Hadoop open source tools.
Cloudera continued its evolution away from Hadoop today by announcing a technical preview for Cloudera Machine Learning, its new data science and data engineering platform that's based on Kubernetes, which enables it to run in the cloud and on premise. Hadoop's influence has waned, in many respects, in direct proportion to the rise of public cloud platforms. Instead of taking the time to build and manage Hadoop clusters to store big data and run analytics on them, companies are turning to cloud providers like Amazon Web Services, who can offer cheap object storage and scalable compute resources. This changing market dynamic has helped to drive 46% year-over-year revenue growth for AWS, and Microsoft Azure and Google Compute Platform are growing even faster. In many ways, the October merger of Cloudera and Hortonworks was a response to this dynamic.
Cloudera has begun rolling out the much-needed revamp of its big-data platform with a new database management and machine learning engine for multiple functions including large-scale, self-service analytics and AI capabilities using telemetry consumed from edge-based endpoints. Under development since completing its merger with onetime rival Hortonworks earlier this year, the company officially launched the Cloudera Data Platform (CDP) during this week's O'Reilly Strata Data Conference in New York. Faced with declining demand for Hadoop, the engine for early cloud-based streaming analytics architectures, the companies came together with CDP as Cloudera's next act. Most notably, Cloudera replaced the core Hadoop Distributed File Store (HDFS) with a cloud object store that can run in Kubernetes clusters. CDP is available in the three major public clouds (AWS, Microsoft Azure and Google Cloud) and on premises, with a bare-metal server version of Cloudera's object store based on Apache Ozone, developed by Hortonworks as a more scalable extension of HDFS.