Cloudera, a provider of the platform for machine learning and advanced analytics, released Cloudera Altus, a Platform-as-a-Service (PaaS).
The technology company said Cloudera Altus enables businesses to run large-scale data processing applications on public cloud. The Altus service helps data engineers use on-demand infrastructure to speed the creation and operation of elastic data pipelines.
Public cloud deployments are at 12 percent of the worldwide business analytics software market and will grow at a 25 percent CAGR through 2020, according to IDC.
Cloud is one of the fastest growing deployment environments for Cloudera customers. The rollout of Cloudera Altus includes support for Apache Spark, Apache Hive on MapReduce2, and Hive on Spark. It is available in most Amazon Web Services (AWS) regions. Cloudera will expand Altus to support other public clouds such as Microsoft Azure, etc.
“Customers are choosing AWS for their large-scale data processing workloads. The Altus service on AWS will make it easier for Cloudera customers to take advantage of the cloud with on-demand data processing and cost optimization through Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances,” said Ken Chestnut, global segment lead at Amazon Web Services.
Data engineering applications like ETL (Extract, Transform and Load) or batch scoring are often large, batch-oriented workloads that run for a fixed period of time and help companies extract critical insights from raw data.
Organizations can gain significant flexibility and efficiency advantages by running these pipelines on elastic infrastructure. Enterprises want to leverage cloud infrastructure alongside familiar large-scale data processing tools and technologies.
The technology company said Altus reduces the risk associated with cloud migrations. It provides users with familiar tools packaged in an open, unified, enterprise-grade platform service that delivers common storage, metadata, security, and management across multiple data engineering applications.
“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,” said Charles Zedlewski, senior vice president of Products at Cloudera.
“Data and analytics, particularly in the cloud, is one of the most significant areas of growth and investment for many enterprises. 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,” said James Curtis, senior analyst, data platforms and analytics at 451 Research.