Cloudera has announced new analytic experiences within the Cloudera Data Platform (CDP), across the CDP Data Engineering, CDP Operational Database, and CDP Data Visualization products.
According to the company, these new services are designed specifically for data scientists and include capabilities like workflow automation, job prioritization, and performance tuning, which are often features that can only be found in add-ons to platforms.
New features in CDP Data Engineering, which is an Apache Spark service that runs on Kubernetes, include visual GUI-based monitoring, troubleshooting, and performance tuning; Native Apache Airflow and robust APIs for automating job scheduling; resource isolation and GUI-based job management; and CDP life cycle integration and SDX security and governance.
CDP Operational Database, which is a high-performance NoSQL database service, added features such as evolutionary schema support, auto-scaling based on workload utilization, multi-modal client access with NoSQL key-value, and CDP life cycle integration.
CDP Data Visualization, which provides visual dashboards, reports, and charts, added the ability to share analysis using drag and drop capabilities and data exploration using AI-powered natural language search and visual recommendations.
“Our customers understand the importance of the data lifecycle to infuse data-driven decision making throughout their business, traditionally integrating data clusters for NiFi, Kafka, Spark, Impala, Hive, HBase and more,” said Arun Murthy, chief product officer at Cloudera. “That still works for data experts – architects and developers – who can use our data lifecycle cluster service – CDP Data Hub. Now with CDP’s analytic experiences, data specialists like engineers, analysts and scientists get exactly what they need to work better, without having to understand or manage clusters, with built-in security and governance across the data lifecycle to make it easy for IT. It’s one enterprise data cloud that works for everyone.”
