BIG DATA APPLIANCES: HARDWARE AND S

BIG DATA APPLIANCES: HARDWARE AND SOFTWARE TUNED FOR ANALYTICS
Because big data applications and analytics demand a high level of system performance that exceeds the capabilities of typical systems, there is a general need for using scalable multiprocessor configurations tuned to meet mixed-used demand for reporting, ad hoc analysis, and more complex analytical models. And as can be seen in relation to the example use cases in Table 6.1, there are going to be a plethora of performance drivers for computational scalability, with respect to data volumes and the number of simultaneous users. Naturally, the technical leaders must assess the end-users’ scalability requirements to help in selecting a specific architectural approach.
There are essentially two approaches to configuring a highperformance architecture platform. One (the hardware appliance approach) employs specialty-hardware configurations, while the other (the software appliance approach) uses software to manage a collection of commodity hardware components.
Hardware appliances are often configured as multiprocessor systems, although the architectures may vary in relation to the ways that different memory components are configured. There are different facets of the system that contribute to maximizing system performance, including CPU/core configurations, cache memory, core memory, flash memory, temporary disk storage areas, and persistent disk storage. Hardware architects consider the varying configurations of these levels of the memory hierarchy to find the right combination of memory devices with varying sizes, costs, and speed to achieve the right level of performance and scalability and provide optimal results by satisfying the ability to respond to increasingly complex queries, while enabling simultaneous analyses.
Different architectural configurations address different scalability and performance issues in different ways, so when it comes to deciding which type of architecture is best for your analytics needs, consider different alternatives including symmetric multiprocessor (SMP) systems, massively parallel processing (MPP), as well as software appliances that adapt to parallel hardware system models.
Hardware appliances are designed for big data applications. They often will incorporate multiple (multicore) processing nodes and multiple storage nodes linked via a high-speed interconnect. Support tools are usually included as well to manage high-speed integration connectivity and enable mixed configurations of computing and storage nodes.
A software appliance for big data is essentially a suite of highperformance software components that can be layered on commodity hardware. Software appliances can incorporate database management software coupled with a high-performance execution engine and query optimization to support and take advantage of parallelization and data distribution. Vendors may round out the offering by providing application development tools, analytics capabilities, as well as enable direct user tuning with alternate data layouts for improved performance.