Large amounts of data or big data results when the process of data capacity goes above than of conventional system databases. For a business of any size, there can be a point when the amount of data coming in isn’t able to fit in the database structure. This data can go to waste if the business has no alternative way available to process it.
McKinsey and Company conducted a research and found out that companies who utilize big data more efficiently were able to experience more growth compared to companies who weren’t smart with big data processing.
In recent times, big data processing was only acceptable by big companies like Oracle, Infor and Google, but due to the development of open source servers, cloud architectures and commodity hardware, smaller companies are able to take advantage of big data processing as well.
Here are some options for companies who’re looking to handle and process large amounts of data:
1. Servers with sophisticated architectures
Sophisticated servers allow for double sided storage for a computer database along with on-board server management and resources for storing the data in an efficient and scalable manner.
The servers often come with built-in cards, SSD storage and technology that can process data and store it effectively 20,000 times faster than conventional storage solutions.
2. Virtualization technology
While sophisticated and cloud servers continue to offer flexibility and reduce the processing and operating costs for firms, it may not be appropriate in every case to opt for sophisticated server solutions.
For example, companies that are analyzing real-time data of low-latency will need virtualization technology because cloud architecture doesn’t offer the resources to process real-time data without offering the latency that can make the results distorted by milliseconds.
DMP stands for data management platform. Sometimes, the environment for data processing becomes so complex that using storage format and serialization alone becomes no longer viable. Issues arise across management and data processing pipelines, and this is where data management platforms come in for the rescue.
DMP provides rely on in-house schedulers to manage data flows in the environment and most of the processing is carried out in a decentralized fashion. The complexity of data is transmitted into a platform where engineers are able to focus on the processing logic.
4. Open source platforms
Open source platforms like Hadoop provide flexible options to handle data coming in from multiple sources. The platforms manage this by reading from databases and running processer machine learning tools and carrying out large-scale processing by aggregating the data.
There are several other applications provided by open source platforms, and they’re also able to detect the changes in data trends due to fluctuations in social media, web traffic and weather sensors.
However, just coming up with a processing and storage solution doesn’t complete the picture. Handling large amounts of data also requires an input from the company’s data analyst. They should be able to read, combine and make the most out of the stored information.
A company may have 450 million registered users in their database, but if they can’t disparate those users and make use of their credentials effectively, they would only be able to go so far in terms of revenue and profitability.
Big data may be have been underutilized by several companies, but new solutions offer a way of making the most out of it.