Looking for faster time to insights or to dramatically increase ROI?
Cancun Systems is re-shaping big data computing through the introduction of a software-defined memory lake (SDML) for big data that enables applications to run up to 10X faster, allowing customers to get richer insights faster and save on infrastructure expenses for significant ROI.
Software-Defined Memory Lake (SDML): A faster and more efficient solution
Cancun Sytems software platform is the industry’s first software-defined memory lake which allows applications to access data from disk at memory speed by making intelligent use of available resources across memory and storage, delivering the speed of memory at the cost efficiency of disk.
Benefits of the MemoryLake PlatformTM are readily available to existing big data frameworks such as Spark, Hadoop MapReduce, Hive, etc. No change is needed to the applications or the infrastructure. With Cancun MemoryLakeTM software platform, line of business owners get insights faster, data scientists can do better queries, and IT departments get a windfall of cycles.
LINE OF BUSINESS OWNER
Faster Time To Insights
Deployment Simplicity & Flexibility
In-Memory Software Platform for Accelerated Insights
Cancun’s MemoryLake™ delivers an SDML that enables applications to run up to 10X faster, accelerating time to insights and tremendous infrastructure efficiencies.
Faster Time to Insights: Applications can now run significantly faster by accelerating and pipelining applications at memory speed, enabling workflows to complete in a fraction of the time.
Infrastructure Efficiency and Savings: Existing build-outs can run more jobs and query more data without additional infrastructure purchases. For cloud deployments, customers can experience both faster insights and immediate savings because they are able to complete jobs and decommission clusters much faster.
Deployment Simplicity and Flexibility: Deploy in private, public, or hybrid cloud environments, and ingest data directly from various sources for richer insights.