What Is Big Data Storage? A Clear and Concise Guide

Big data storage is a technology that applies to storage technologies that specifically address, in some fashion or another, the three Vs: volume, velocity, or variety. Yet big data doesn’t address the classification of relational database systems. In simpler terms, it’s a compute-and-storage architecture designed to collect and manage large data sets, enabling real-time data analytics. This does not imply that relational database systems don’t address these hurdles, but alternative storage technologies such as columnar stores and innovative combinations of different storage systems are frequently more efficient and less costly.

Big data analytics is utilized by organizations to obtain more comprehensive intelligence from metadata. Generally speaking, big data storage utilizes cost-efficient hard disk drives, although the decline in prices for flash seems to allow for mass adoption of flash in servers and storage systems as the framework of big data storage. Such systems can be hybrids mixing disk and flash storage or all-flash arrays (AFA).

As of now, there are no specifications as to what defines big data, such as volume size or capacity. However, most would agree that big data storage volumes expand exponentially to the terabyte or petabyte scale range. Furthermore, big data is comprised of unstructured data—primarily file-based and object-based storage.

The Components That Make Up a Big Data Storage Infrastructure

Big data systems groups a copious amount of commodity servers (also referred to as commodity computers or commodity hardware) that are connected to high-capacity disks in order to help analytics software written to crunch immense volumes of data. These systems rely on massively parallel processing databases to analyze data ingested from a variety of sources. This hodgepodge of data usually lacks any kind of structure being that it is derived from multiple sources, which makes it a sparse fit for processing using a digital database based on the relational model of data.

For now, the Apache Hadoop Distributed File System (HDFS) is the most prevailing analytics engine for big data and is typically merged with some characteristics of a NoSQL (non-SQL or nonrelational) database. HDFS divides the data analytics over hundreds or thousands of server nodes without suffering performance-wise.

Additionally, Hadoop is capable of distributing processes through a component called MapReduce, safeguarding the hardware from a catastrophic failure. At the network’s edge, various nodes work as a platform Thus, when any queries arrive, MapReduce performs processing directly on the storage node the data occupies. After the analysis is concluded, MapReduce infers the aggregated results from each server and “reduces” them to present a singular cohesive answer.

What Specifications One Should Search for in Big Data Storage

According to an article published by FedTech, global IP traffic is suspected to double or even triple in only a few years—growing steadily to 25 gigabytes per capita by 2020 (an increase from 10 gigabytes per capita since 2015).

This data growth presents an extensive opportunity to gain new capabilities, identify previously undiscovered models and increase levels of service to consumers. However, big data analytics can’t subsist in a vacuum. Since there are tremendous quantities of data associated with these solutions, they must use a strong infrastructure for networking, processing, and storage, as well as for analytics software. In order to achieve that, the following specifications should be met in every big data storage solution.

Supports Tiered Storage

Big data storage architectures should be capable of prioritizing data while retaining some data for analytics and some data for archiving. A majority of big data storage solutions have a storage hierarchy for prioritizing flash memory, disk, tape storage, etcetera.

Supports Scalability

Predicting big data storage requirements is futile. You would have to determine the amount of data required to manage applications and predictive models for every big data category, including expected requirements.

Nonetheless, there is likely one principal application that is responsible for most of the organization’s income and is the center of a big data initiative—this application should be used to measure initial storage necessities. However, whatever strategy is chosen, one should be positive that storage scalability doesn’t impact data throughput nor administration overhead.

Secure and Dependable Networking

The extensive volumes of data that must be alternated back and forth in big data initiatives demand robust networking hardware. A lot of organizations already operate with networking hardware that facilitates 10-gigabit connections with only minor modifications needed—the installation of new ports, for example—to support a big data initiative. Having secure network transports is an indispensable step to any upgrade, particularly for traffic that crisscrosses network boundaries.

Predictive Analytics Software

Organizations need to select big data analytics products based on what functions the software can complete, this is true. Nonetheless, factors such as ease of use and data security are also important. A common role of big data analytics software is predictive analytics—which is the interpretation of current data to obtain foresight about future trends.

Predictive analytics has already been applied across numerous disciplines, including marketing, financial services, and actuarial science. Governmental utilization includes things like child protection, capacity planning, fraud detection, among others. Additionally, many governmental agencies are using big data analytics technology to flag high-risk offenders in criminal cases.

Enough Processing Power

Servers designed for big data analytics should have plenty of processing capability to maintain a big data infrastructure. Many analytics vendors offer a plethora of cloud processing options that can be particularly attractive to a lot of organizations that encounter seasonal peaks.

For example, if enterprises have quarterly filing deadlines they might securely fire up on-demand processing power in the cloud to process the influx of data that comes in around those time frames while depending on processing devices on-premise to manage the more constant, day-to-day needs.

Flash Storage Means Performance Advantages

A majority of organizations already maintain adequate storage in-house to establish a big data initiative. Nevertheless, organizations might select to invest in storage solutions optimized for big data. While not essential for every big data deployment, flash storage is exceptionally attractive due to its performance benefits and high availability.

Of course, this article only covers the tip of the iceberg. There is a lot more to learn when it comes to understanding big data storage. The best way to do this is by contacting one of our experts today to learn more about how RAID Inc. can assist you in launching a successful big data initiative.