On Wednesday, at the Strata Data conference in London, Cloudera unveiled a new platform as a service (PaaS) solution called Altus, which could make it easier for businesses to run big data workloads in the cloud.
Altus relies on on-demand infrastructure to help users quickly build scalable, elastic data pipelines that power the kind of applications driven by big data workloads, according to a press release announcing the product. Many of these apps utilize batch-oriented workloads that only run for a given amount of time. Using elastic infrastructure, organizations can scale out to handle the data pipeline workloads, as needed, adding flexibility, the release said.
The promise of the cloud is built on abstraction, and Altus seeks to abstract the management and operation associated with big data infrastructure, as well as the infrastructure itself. According to the release, Altus helps to provide features like metadata, security, management, and common storage "across multiple data engineering applications."
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"Data engineering workloads are foundational for today's data-driven applications," Charles Zedlewski, senior vice president of products at Cloudera, said in the release. "Altus simplifies the process of building and running elastic data pipelines while preserving portability and making it easy to incorporate data engineering elements into more complex BI, data science and real-time applications."
Through Altus, data engineers can provision popular open source tools like Apache Spark, Apache Hive, Hive on Spark, and MapReduce2 in a cloud-native environment, the release said. Altus also provides default cluster settings for faster deployment time, easier management, and simple automation. It's now available on Amazon Web Services (AWS), but will eventually make its way to Microsoft Azure.
Altus is primarily geared toward data pipelines in hopes of helping users worry less about infrastructure, the release said. And like the rest of Cloudera's solutions, data engineers can read and write directly to cloud object storage.
Altus works with multiple versions of Cloudera Distributed Hadoop (CDH), and the service also provides built-in workload management to improve troubleshooting, the release said.
"Data and analytics, particularly in the cloud, continues to be one of the most significant areas of growth and investment for many enterprises," James Curtis, senior analyst for data platforms and analytics at 451 Research, said in the release. "But organizations also faces challenges with cloud-based cluster management, data processing, and migration, which is right where Cloudera is focusing its efforts with Altus."
The 3 big takeaways for TechRepublic readers
- Cloudera just released Altus, a new Platform-as-a-Service that makes it easier to run big data workloads on cloud-native infrastructure.
- Altus relies on on-demand infrastructure and elastic pipelines to help organizations quickly ramp up their infrastructure as needed for those workloads.
- Data engineers can run Apache Spark, Apache Hive, Hive on Spark, and MapReduce2 through Altus, which is now on AWS but eventually will come to Azure.
- Cloudera's new data science tool aims to boost big data and machine learning for businesses (TechRepublic)
- Cloudera introduces Altus, offering Hadoop jobs as a Service (ZDNet)
- How Cloudera defined big data, and was defined by it (TechRepublic)
- Cloudera shares up 20 percent on Wall Street debut (ZDNet)
- Why public cloud R&D is making lock-in worse, not better (TechRepublic)
Conner Forrest has nothing to disclose. He doesn't hold investments in the technology companies he covers.
Conner Forrest is a Senior Editor for TechRepublic. He covers enterprise technology and is interested in the convergence of tech and culture.