ZL Technologies Annouces ZL NoSQL DB

Date 2015/7/1 8:18:59 | Topic: Product News

ZL Technologies has announced ZL NoSQL DB™, the company's core processing and storage engine for massive-scale analysis of multiple data types. The NoSQL architecture is a key component of the ZL Enterprise Analytics™ (ZL EA) offering as well as the flagship ZL Unified Archive® platform (ZL UA), and allows for cross-analysis of extremely diverse data types, a task which is potentially impossible to execute in a traditional relational database environment. With ZL NoSQL DB™, businesses can better leverage today's growing volumes of enterprise content via analytics, flexibly adapting to new data types and combining People Data, Business Data, and Machine Data for valuable insights that drive business performance.

The traditional relational database approach excels at many processing tasks, but can be inefficient and difficult to scale when dealing with diverse, evolving data types. ZL NoSQL DB™, engineered directly within ZL Unified Archive®, applies the schema-free benefits of NoSQL processing, allowing new data types to easily be added for analysis without needing to modify the tabular structure associated with relational systems. Horizontal scaling easily adapts to the demands of modern Big Data, allowing growing information to be processed efficiently and cost-effectively. With Query acceleration, an adaptive compression algorithm, an embedded text engine, graph computing engine, and managed partitions and indexes, the ZL NoSQL DB™ provides best-of-breed processing capabilities to optimize the leverage of all enterprise content.

The business benefits of incorporating ZL NoSQL DB™ into the enterprise information governance and analytics environment are immense, allowing for a more flexible, contextual, and consolidated approach to data analysis. Increased performance, scale, and adaptability allow the organization to pool data centrally, cross-analyzing diverse data sources that previously may have been incompatible or structurally isolated via disparate data "silos." With an analytics approach supplemented by NoSQL-based processing, the enterprise can cost-effectively manage and explore new relationships between diverse data sources, with sweeping implications for business productivity and insight.



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