Dell Launches Dell In-Memory Appliance with Cloudera Enterprise to Help Customers
FREMONT, CA: Dell has launched the Dell In-Memory Appliance with Cloudera Enterprise to help customers gain valuable business insights with access to interactive analytics. The tool also helps them to capitalize on high-performance data analysis with advanced analytics for interactive, iterative and near real-time analytics with new in-memory capabilities – all within the same software stack.
The Dell In-Memory Appliance with Cloudera Enterprise is designed for customers looking for a processing engine combined with interactive analytics in a flexible, open, preconfigured, and scalable solution. It allows customers to monitor, manage and gain insights in a short window of time, resulting in increased velocity for analytics and thus greater insights and understanding.
Besides this, the Dell In-Memory Appliance with Cloudera Enterprise simplifies the procurement, deployment, tuning and optimization of the leading-edge Apache Spark solution stack, which is integrated with Cloudera Enterprise. It offers: Faster time-to-insights with next generation analytics and streaming workloads, shrinking the advanced analytics cycle from weeks and days to minutes or even seconds; Leading-edge enterprise data hub solution that simplifies the procurement, deployment, management, and optimization of big data projects; Shorter analytics cycles which allow users to be more agile and more inventive; and Flexible and open solution that scales to business needs.
Apache Spark is a fast engine for large-scale data processing that uses in-memory computing for interactive query, iterative processing, graph analysis and streaming data. The unique integration of Apache Spark on the Dell In-Memory Appliance with Cloudera Enterprise will allow customers to use a single tool for fast access to data, resulting in less development time on data modeling and query analysis, and simplified complex pipeline jobs.
The Dell In-Memory Appliance for Cloudera Enterprise is easy to use and is highly compatible with existing solutions, so organizations can deploy a Hadoop cluster in a couple of days for faster time-to-value in an appliance model. The solution is economically scalable from entry level up to 48 nodes without the need to rip and replace.