Real-time analytics on live data and the ability to analyze data at scale, are key for any digital organization. Acting on insights in the moment helps deliver contextual user experiences, identify new sources of revenue, and prevent costly expenditures. To become a responsive data-driven business, organizations must address current data latency challenges. These challenges are commonly found across three general areas: Three Data Latency Challenges and Solutions Slow data loading: Loading data, moving past batch processing, and receiving analytics responses in real time remains out of reach for too many businesses. A real-time data warehouse reduces extract, transform, and load (ETL), combining transactional and analytical workloads into a single system. Lengthy query execution: Operational insights must be readily available for in-the-moment decisions. A data warehouse that delivers a fast query response can deliver insights whenever the application or users require it, ultimately providing a differentiated service or identifying opportunities. Low concurrency: Digital business assumes a large-scale use of data across an entire business or customer base. As more users engage and interact with data, the response time for those interactions must maintain a consistent experience. A scalable data warehouse will help ensure that data and user growth will not negatively affect the operational system. Real-Time Data Warehouse for the Enterprise #MemSQL on #Cisco #UCS Integrated Infrastructure for #Big Data and Analytics is built to address these challenges. MemSQL provides a scalable, real-time data warehouse platform for high-performance applications that require fast, accurate, secure, and always available data. MemSQL scales linearly to millions of events per second while analyzing petabytes of data for insights. MemSQL also enables fast ingestion and concurrent analytics needed for sensor systems, recommendation systems, and use cases that require instant, actionable insights
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