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Accelerate business insights by managing hybrid data in memory.

ORGANIZATIONS LOOK TO DATA to provide answers, but most are ingesting it at a volume, speed and variety that creates the even bigger challenge of making sense of it. Due to the tradition of keeping operational and analytical data separate in legacy environments, the increasing need for processing structured, unstructured and semi-structured data, and the convergence of both volatile and non-volatile storage underneath these workloads, data management is threatening to become increasingly burdened by complex architectures and unsolvable pain points rather than a source of insight. And "Big Data" quickly becomes just that--a huge pile of data creating a big headache.


Historically, two types of data processing environments have developed due to different characteristics of analytics (OLAP) and transactional (OLTP) workloads, and the reluctance of performing analytical processing on live transactional data. OLAP often requires ad hoc exploratory capabilities and doesn't have strong SLAs, while OLTP almost always demands strong performance and SLA's for data consistency.

However, increasing demand for real-time analytics, which allows instantaneous business intelligence and decision making, is forcing many enterprises to rethink the fundamental premises behind OLAP and OLTP. Surging innovations in the area of In-Memory Computing provide the technological underpinning for the new software infrastructure for emerging hybrid transactional and analytical processing (HTAP) workloads. With the performance and scalability benefits of In-Memory Computing, Big Data can be effectively stored and processed in DRAM, and both analytical and transactional workloads can be effectively executed without a need for two different systems or ETL data movement processes.

The GridGain In-Memory Data Fabric provides a unique platform for high performance data processing of analytical and transactional workloads without a need for costly ETL processes from silo-ed installations. It combines state-of-the-art transactional processing capabilities with all key analytical processing features in one data layer, sharing the same ultrahigh performance characteristic (high throughput, low latency) of in-memory processing.


Another interesting aspect of hybrid data management is the fact that no longer is there a single data source that serves the application or a set of applications. The typical modern composite application relies on multiple dedicated data sources such as traditional RDBMS for OLTP, NoSQL for OLAP and Hadoop for data warehousing. One of the key challenges of hybrid data management is the ability to effectively query and manage data across a diverse set of data sources, while providing a unified and consistent view on all data to the applications.

The GridGain In-Memory Data Fabric provides a data access and processing layer that takes a holistic view of in-memory processing as a layer on top of any existing data source--instead of requiring a costly replacement of any one of them. GridGain's approach allows to ingest new and traditional data sources without ripping and replacing existing databases, while offering high performance processing of diverse data sets in a hybrid environment.


Just as Flash technology is quickly taking the place of spinning disks as the default storage for many traditional workloads, RAM--especially emerging non-volatile DIMM (NVDIMM) technology--promises long sought-after data persistence for high-performance, hyper-scale applications. NVDIMM makes a normal DDR4 memory persistent and enables dramatic performance optimizations for in-memory-based applications--transactional, analytical and hybrid (HTAP).

Unlike NAND-based storage which is always accessed as a block device, DRAM-based NVDIMM is purely byte addressable memory that's absolutely identical to a normal DRAM. In fact, from the application's standpoint there is no difference between accessing normal DRAM or NVDIMM. Most transactional systems assume a tiered memory hierarchy of volatile memory for processing, and persistent disk storage (HDD, SSD) for durability of the data. With NVDIMM, these systems gain fast and granular access to persistent storage without the performance penalty of involving disk-based storage.

As a leading provider of open source and commercial in-memory technology, GridGain Systems is on the forefront of innovating in the areas of hybrid volatile/ non-volatile memory environments, with the goal to support low-latency write-though operations for real-time applications that cannot afford to lose data.


Modern in-memory technology provides the most logical and comprehensive way to harness the computing power necessary to manage the growing demands of hybrid data management. The GridGain In-Memory Data Fabric--available as an open source project (Apache Ignite incubating) and a hardened enterprise product--offers companies unique capabilities and a competitive advantage in managing diverse data with the speed and scale necessary to address the requirements of modern Cloud, Big Data, social and IoT applications.

It's easy to test our promise. Download a free evaluation copy of the GridGain In-Memory Data Fabric at

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Publication:Database Trends & Applications
Geographic Code:1USA
Date:Feb 1, 2015
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