Just like Big Data is ubiquitous, Hadoop occupies a wide landscape in the global market. Since its inception by Doug Cutting, Hadoop is the most credible and sound processing, and delivery platform to handle massive amounts of data across all departments. However, with the latest developments and some traditionally established systems in technology to confront this data eruption, enterprises, together with Hadoop, are adopting Apache Spark and an intensive open source computing platform, High-Performance Computing Cluster (HPCC).
A project developed by LexiNexis Risk Solutions, HPCC now takes on Hadoop’s big data dominance. Although a distributed database processing system having the capabilities of HDFs and MapReduce is irreplaceable, HPCC is making its way in the big data entourage. Revered as Data Analytics Supercomputer (DAS), this computing system has been powering up since the early 2000s. In an effort to manage, sort, link and analyze billions of records within seconds, LexiNexis built a data intensive supercomputer to process massive volumes of data 24*7.
In the midst of using Hadoop and Spark, many of you may still be foreign to the unique and state-of-the-art benefits of implementing HPCC in your organization. Read through the best-in-class analytics features to gain better understanding of the supercomputer:
- The platform includes two integrated clusters to filter and provide large quantities of structured and unstructured data.
- HPCC is a more mature and cultivated enterprise-ready project using an easy-to-learn, consistent programming language ECL (Enterprise control Language) grounded on C++.
- This language reliance lets developers and programmers to execute rapid operations since C++ runs on top of the operating system and does not require a Virtual Machine like Java.
- The data recovery mechanism in HPCC is excellently reliable and mission-critical with a layered architecture comprising security, recovery, audit and compliance layers. The data, if lost, can be reclaimed like a traditional database management system.
Now that you have known the key characteristics of High-performance computing cluster, data-intensive computing is an important segment to learn. There are several significant characteristics that separate data-intensive computing systems from other big data handlers. It works on the principle of ‘Move the code to the data,’ which signifies that instead of moving data from one location to another, the program/algorithm is transferred to the nodes with data that needs to be processed. Program movement has been effective since the size of a code is relatively smaller than the large data sets, reducing system overhead and improving performance.
Another notable attribute of data-intensive computing is its machine-independent approach, where data applications are expressed using high-level data operations, such that the runtime system takes care of the scheduling, execution and movement of programs and algorithms across the computing cluster. On the contrary, machine-dependent programming software has a complex parallel processing task and reduces the productivity of the programmer and the system on the whole. They are also more prone to system failure, which is avoided in HPCC supercomputer.
Data-intensive systems in HPCC are designed to be fault-resilient, and the computations on extensively large amount of data continue to execute with reduced number of nodes, delivering highly scalable results.