Always Parallel for Breakthrough
Performance and Scaling
Big Data Analytics Require a Scalable Analytic Platform
As data volumes and sources continue to increase, your analytic applications are put under enormous strain. That's if you can load the data fast enough to satisfy service-level agreements. Scaling data and analytics to manage terabytes or petabytes of data requires a pervasively-parallel architecture across all functions of the system: querying, processing, loading, export, and even backup and restore.
Massive Parallel Processing (MPP) for
Breakthrough Performance and Scaling
The Aster Data analytic platform, Aster Database, provides massive parallel processing (MPP) on either commodity server hardware from leading vendors, on deployed on a Cloud platform, or as a pre-packaged Aster MapReduce Appliance. Innovations in large-scale distributed systems enable breakthrough performance with linear scale-out to petabytes of user data. The result: 10x-1000x better performance than traditional systems.
- Unlimited Scalability – Aster Database’s Online Precision Scaling™ provides linear scalability across functional tiers (loads, queries, backups) independently or in unison to meet workload requirements. Granular splitting and load balancing of virtual partitions maintains maximum parallelism across CPU cores and servers for massive “no limits” scalability.
Aster Database provides massively parallel processing (MPP) across all tasks
- Pervasive Parallelism – Independent scaling of each function (loads, queries, processes, exports, backups, recoveries, installs/upgrades) guarantees end-to-end parallelism for maximum performance.
- Optimized to Deliver the Highest Performance – Patent-pending algorithms for intelligent data placement, network-optimized shuffling, dual-stage query optimization, and I/O-optimized table compression ensure ultra-fast in-database analytics on terabytes to petabytes of active data.