Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a more powerful user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows. Leveraging Hue as the SQL development workbench, we continue to see usage of the platform increase and the number of analytic use cases grow –
Azure Data Lake Store (ADLS) is a highly scalable cloud-based data store that is designed for collecting, storing and analyzing large amounts of data, and is ideal for enterprise-grade applications. Data can originate from almost any source, such as Internet applications and mobile devices; it is stored securely and durably, while being highly available in any geographic region. ADLS is performance-tuned for big data analytics and can be easily accessed from many components of the Apache Hadoop ecosystem,
Cloudera Altus (launched in May 2017) is a platform-as-a-service (PaaS) offering that enables users to analyze and process data at scale in public cloud infrastructures. Altus was designed from the outset to support multiple clouds from the perspective of both back-end architecture and front-end workflows. With the announcement of Microsoft Azure support, Altus will be able to support data engineering workloads in Microsoft Azure, with the same Altus interfaces for API and CLI,
Cloudera Director 2.4 improves support for long-running clusters by syncing with upgrades and topology changes via Cloudera Manager, and adds support for Spark 2 and Kudu. Cloudera Director along with CM and CDH5.11 adds support for Microsoft Azure Data Lake Store (ADLS), and pausing of clusters with Amazon EBS volumes.
Cloudera Director helps you deploy, scale, and manage Apache Hadoop clusters in the cloud of your choice.