The GridGain In-Memory Data Fabric delivers unprecedented speed and unlimited scale to accelerate your business and time to insights by enabling high-performance transactions, real-time streaming and fast analytics in a single, comprehensive data access and processing layer that spans all key applications (Java, .NET, C++) and data stores (SQL, NoSQL, Hadoop). It is a comprehensive in-memory solution that includes a database-agnostic data grid, a clustering and compute grid, a real-time streaming engine as well as plug-and-play Hadoop acceleration.
The data grid feature in the GridGain In-Memory Data Fabric has been built from the ground up with a notion of horizontal scale and ability to add nodes on demand in real-time; it has been designed to linearly scale to hundreds of nodes with strong semantics for data locality and affinity data routing to reduce redundant data noise.
The data grid feature in the GridGain In-Memory Data Fabric supports local, replicated, and partitioned data sets and allows to freely cross query between these data sets using standard SQL syntax. It supports standard SQL for querying in-memory data including support for distributed SQL joins. The GridGain In-Memory Data Fabric offers an extremely rich set of data grid capabilities, including off-heap memory support, load-balancing, fault tolerance, remote connectivity, support for full ACID transactions and advanced security.
In-Memory clustering and compute grids are characterized by using high-performance, integrated, distributed memory systems to compute and transact on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. A comprehensive set of API’s provided by the GridGain In-Memory Data Fabric allows users to distribute computations and data processing across multiple computers in a cluster in order to gain high performance and low latency.
Many streaming technologies evolve around a single use case such as event workflow management or streaming data querying. However, customers usually require both, rich event workflow combined with complex event processing (CEP) data querying, and as a result are left with the difficult task of integrating different streaming technologies together. The real-time streaming feature included in the GridGain In-Memory Data Fabric fully integrates event workflow and CEP capabilities.
The real-time streaming feature of the GridGain In-Memory Data Fabric uses programmatic coding with rich data indexing support to provide CEP querying capabilities over streaming data.
The GridGain In-Memory Data Fabric also provides comprehensive support for customizable event workflow. When events come into the system through different execution chains, GridGain in-memory streaming can support multiple execution paths for the same events executing in parallel on one or more nodes, supporting loops and recursive branching.
As streaming data is fairly constant, it is important to define the scope of streaming data operations by limiting the size of data being queried. The GridGain In-Memory Data Fabric offers rich sliding windowing functionality which includes sliding windows that can be limited by size or time, windows that slide with either every individual event or in batches, windows that are unique or allow duplicates, and windows that can be sorted or snapshotted.
The GridGain in-memory file system (GGFS) included in the GridGain In-Memory Data Fabric has been designed to work in dual mode as either a standalone primary file system in the Hadoop cluster, or in tandem with HDFS, serving as an intelligent caching layer with HDFS configured as the primary file system.
As a caching layer it provides highly tunable read-through and write-through logic and users can freely select which files or directories to be cached and how. In either case GGFS can be used as a drop-in alternative for, or an extension of, standard HDFS providing instant performance increase.
In additon to the Hadoop Acceleration feature of the GridGain In-Memory Data Fabric, GridGain also offers the Hadoop Accelerator as a standalone, plug-and-play solution.