Big data has captured public attention in unexpected way, and now businesses, both online and offline, cannot comprehend data analysis without utilizing big data technologies.
One of the toughest challenges facing the big data analytics community today is not the extra-large size of data sets, but the critical requirement of placing large data sets into fast access memory for processing. The need to derive meaningful information from big data data sets in real time, specifically, requires a solution that can provide very large (1 TB or larger) storage.
Many of the available capabilities of big data analytics will become useless unless faster processing can be provided through speedy memory caches. Availability of large memory for storing large data sets can prove to be a significant technology enhancement in overcoming hurdles associated with big data analytics. Fast access to memory can also mean deriving more value from data!
As this technology whitepaper from ScaleMP,
Meeting the Changing Demands of Big Data Analytics suggests,
Deriving insight from Big Data often requires putting large data sets into fast access memory.
Challenge of big data analytics
Analytics-related applications can only reach their potential if high volume data can be loaded into memory because:
- Predictive simulations often require more memory to load sophisticated models used in processing;
- Complex database queries, which often produce results that are used to determine the next step of processing, require large memory availability; and
- Real-time analytics such as customized online experiences are conducted on very large data sets, which have to be loaded to memory.
But a suitable solution for large memory requirement is hitherto unknown to many vendors.
Some common methods to combat the memory limitation problem are:
- Distributed memory model. In this scenario, performance can be a serious issue as application processing is widely broken down and distributed over a set of server clusters. There may be data traffic overloads, movement bottlenecks, processing distractions etc. As a result, a distributed processing strategy does not provide the desired efficiency and agility of results.
- SMP systems. Usually very expensive to acquire and even more expensive to maintain via service contracts.
ScaleMP : The leader in provisioning fast access memory
ScaleMP has come out with an innovative solution to this vast big data processing issue by providing technology that adds memory from distinct components like the I/O or the CPU, residing on discrete cluster servers. This available memory is then virtualized and made available to applications requiring memory storage of at least 1 TB of data. This particular technology provides a virtual memory bank from inexpensive servers. Sample usage scenarios for this type of virtual memory are provided in the whitepaper.
This solution works well for memory-intensive big data applicationswhile maintaining the efficiency and agility in using various amount of resources for varying processing needs.
Many organizations today keenly rely on big data analytics for speedy research work, more efficient operations, enhanced customer service, and increased profits. Much of the success of big data analytics depend on its ability to search and retrieve patterns, correlate patterns with events, then derive intelligence from large datasets to make smarter business decisions.
In order to conduct such high-end analytics, the system will require very large volumes of data (1 TB or more) to be loaded into memory for super-fast processing and reporting. In these typical applications, only large memory banks can facilitate high-end processing speed and power of server clusters plus Hadoop.
Unfortunately, the common solutions are either limited or very expensive. Therefore , ScaleMPs solution comes as a welcome change to the memory-strapped world of big data analytics. ScaleMPs technology white paper, Meeting the Changing Demands of Big Data Analytics, discusses at length, why large data sets need to be loaded on memory for analytics processing. The paper first highlights the common solutions in vogue today.
The paper proceeds to introduce ScaleMPs own solution of virtualizing memory in server clusters to tackle this problem. The data scientists who may be facing a limited memory issue in their day-to-day big data analytics environment will find this whitepaper very useful.