Contactez-nous Suivez-nous sur Twitter En francais English Language

De la Théorie à la pratique

Freely subscribe to our NEWSLETTER

Newsletter FR

Newsletter EN



Attala Systems Introduces the Attala Data Lake Module for Its Composable Storage Infrastructure

August 2018 by Marc Jacob

Attala Systems introduced the Attala Data Lake (ADL) software module for its award-winning NVMe composable storage infrastructure solution, which won “Best In Show” at the 2017 Flash Memory Summit. The multi-tenant nature of ADL provides enterprise customers with a high-perfomance “hot data lake” to applications sharing the same dataset, while decreasing the cost and compexity of the associated hardware. The Attala Systems ADL software module is being introduced at the 2018 Flash Memory Summit. The module is available for evaluation by select customers and partners, and will be generally available (GA) in calendar Q4 of 2018.

Data lakes are one of the latest enterprise storage concepts for analytics, and enable the management of enterprise data as a resource, not just the contents of corporate storage silos. However, demand for common data by multiple real-time applications can impact the availability of this data. The alternative typically utilized is local “hot data caches,” where each application instance has its own hot storage. While this approach increases performance, it can result in data consistency issues if the data sets are common across multiple applications. It also increases storage costs by duplicating hardware, as well as making the management of those datasets more complex. Enterprises need tools to enable the management of data as a shared resource that can be consumed across business units and applications having a need for real-time analytics.

The Attala Systems multi-tenant ADL software module addresses this need by providing a high-performance storage pool that can be shared across applications and organizational boundaries, either as a “hot cache” or as a full-fledged enterprise data lake. ADL also provides for centralized management and provisioning of the hot data lake, reducing operational complexity and expenses. This approach eliminates potential data consistency issues that can occur when multiple local storage caches are utilized for multiple applications with shared data. The result is a low-cost, high-performance solution for real-time analytic applications.

See previous articles


See next articles