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. Organisations 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 Cloudera Altus Data Engineering service simplifies the development and operations of elastic data pipelines; putting data engineering jobs front and centre and abstracting infrastructure management and operations that can be both time consuming and complex. Altus also 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.
“Data engineering workloads are foundational for today’s data-driven applications,” said Charles Zedlewski, senior vice president of Products at Cloudera. “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.”
Cloudera makes it easy, cost-effective, and convenient to deploy these workloads on cloud providers, such as Amazon Web Services (AWS), taking advantage of cloud elasticity, low-cost storage and compute options, and rapid provisioning to deliver a modern data service that can tackle even the most challenging business problems. Cloud object stores such as Amazon Simple Storage Service (Amazon S3) are becoming increasingly popular for their resiliency, scalability, and relatively low cost.
According to IDC, public cloud deployments are now at 12% of the overall worldwide business analytics software market and expected to grow at a 25% CAGR through 2020[1]. Cloud is one of the fastest growing deployment environments for Cloudera customers, and Altus makes it easier than ever to run data engineering workloads in the cloud.
Features and benefits of Altus include:
- Managed service for elastic data pipelines - Cloudera Altus is a PaaS that allows data engineers to easily and quickly provision Apache Spark, Apache Hive, Hive on Spark, and MapReduce2 capacity on cloud-native infrastructure. Altus presents intelligent default cluster settings and environments that dramatically reduce cluster deployment times and operations, automating processes like cluster provisioning, configuring, and termination.
- Workload orientation - Cloudera Altus centres around data pipelines rather than clusters or infrastructures, so users can easily submit, clone, and troubleshoot pipelines with minimal attention paid to the underlying infrastructure.
- No data siloes - The Altus Data Engineering service enables data engineers to run direct reads from and writes to cloud object storage as does the rest of Cloudera’s platform. This data is immediately available for use by other Cloudera workloads without requiring data replication, ETL or changes to file formats. In doing so users can more easily incorporate data engineering into their data science, BI and real time DB applications.
- Backward compatibility and platform portability - Altus supports multiple versions of CDH the most widely used open source platform in the industry. Users can easily move workloads to and from the cloud without needing to modify their applications. Because CDH is backward compatible across minor releases, customers can harness the latest innovation from the Apache big data open source community without fear of breaking their applications from release to release.
- Built-in workload management - Altus automates and simplifies the common operational issues related to elastic data pipelines with workload management. Users can troubleshoot failed jobs with or without the clusters or compute infrastructure being present. In addition Altus’ workload management flags significant performance deviations and proposes a root cause analysis. In doing so customers can run their data pipelines with greater reliability and lower cost.
