In midsize and large organizations, critical business processing continues to depend on relational databases including Microsoft® SQL Server. While new tools like Hadoop help businesses analyze oceans of Big Data, conventional relational-database management systems (RDBMS) remain the backbone for online transaction processing (OLTP), online analytic processing (OLAP), and mixed OLTP/OLAP workloads.
Published By: IBM APAC
Published Date: Apr 27, 2018
While relying on x86 servers and Oracle databases to support their stock trading systems, processing rapidly increasing number of transactions fast became a huge challenge for Wanlian Securities. They shifted to IBM FlashSystem that helped them cut average response time for their Oracle Databases from 10 to less than 0.4 milliseconds and improved CPU usage by 15%.
Download this case study now.
Today’s consumers expect instant access to communications services whether they’re in the office, at home, or on the road. With data speeds increasing and international roaming costs decreasing, data usage is rapidly growing. Telecommunications Service Providers (TSPs) are under pressure to deliver more services to more people and approach 100% uptime all while lowering prices to consumers.
Traditional relational databases can’t meet the requirements for massive scalability, availability, and fault tolerance that the rapid growth in data usage and rise of big data demands. Read this solution brief to learn how Riak excels at these. Riak is a distributed NoSQL database optimized for big data. Riak meets many of the challenges you may be facing with your own service operations systems.