In recent times, firms, agencies, governments and consumers have largely embraced the need for collecting details of their transactions and storing them as data. As more transactions ensue, so does the size of the stored data. This data most times accumulate into sizes beyond the normal storage capacity of the firms, making handling and use difficult, however, this challenge is handled in big data optimization. See how.
Although there is no direct definition for the term ‘big data’ since the word “big” is relative, big data can refer to any data collection that out-matches the storage and processing capacity of consumer end and small-scale servers. For small enterprises, a small number of terabytes could be called “big data” while the definition of big data for larger enterprises may exceed One petabyte, and a petabyte is 1,024 terabytes of data.
Big data can also be considered on the basis of five criteria which are:
speed; in this criterion, data is categorized by the speed with which it is collected. Technological improvement in network and hardware over the years has ensured an increase in the rate at which enterprises collect data at the same time.
worth; Worth refers to the value in the data collected. An enterprise may store a lot of information that may have any potential in aiding decisions. Though it is safer to collect all related information, reviews should be carried out to decide what data to collect, and if the collected data would be helpful in decision making after analysis.
variety; Variety refers to varying forms of data collected. Big data under variety can either be structured or unstructured. Structured data include information such as phone numbers, email addresses of customers, etc. while unstructured data may be in the form of an article reviewing a product.
trustworthiness; This refers to the quality of truthfulness/trust in the data, it is a futile effort gathering big data which after analysis cannot be relied upon.
size; Size deals with the volume of information gathered. The size of big data varies due to the nature of the data collected. For instance, big data collected from a movie hosting web server will most likely be larger than that collected from a small business enterprise.
What are the best tools for big data analysis?
Analysis of big data can be done efficiently and fast with the aid of certain tools manufactured for such purpose. These tools make use of efficient storage systems and specific algorithms to analyze large amounts of data within short durations. Some of the best tools for analyzing big data are:
In relation to the type and volume of data concerned, popular relational database tools like PostgreSQL and MySQL can be used for analyzing big data.
Actually, tools used for analyzing big data are expected to be shared on multiple servers. They utilize resources present in multiple servers to process a large amount of data in no time. Hadoop, for instance, is designed to utilize tens or a few hundreds of singular servers linked in clusters.
Users however are not coerced to use multiple dedicated servers. In analyzing big data for smaller enterprises, one reliable and strong dedicated server should be adequate.
On high-spec dedicated servers, it is possible to engage clusters of virtual machines to replace tools like Hadoop nodes. Many firms link clusters of individual dedicated servers to generate their private cloud bringing all resources into one point. This helps them to efficiently organize and allocate resources in engaging several big data analysis on the private cloud.
Between cluster and single servers, the best for your enterprise’s big data structure is dependent on the volume of data concerned, if its size is adjustable if it has redundant components and also the software to be used.
When selecting and optimizing a dedicated server for the purpose of analyzing big data, there are certain factors to consider which are; the prospects of transferring a large volume of data to be processed into the server, the backplane which is the link between servers must be durable in holding a large amount of data if a cluster is to be used, tools used normally optimized for direct execution employing many threads on each server and sharing work among many servers, some big data tools are optimized to process data ‘in-memory’ which happens to be faster than disk-based data processing.
Though there exists no single sufficient solution for dedicated server hosting in dealing with big data. However, the following guidelines will help you in planning your big data management systems.
Your server will receive a great deal of data most times from a data center or third party. There may be cases of erratic network if the server doesn’t have adequate capacity to hold the data. At the minimum level, 1 Gbps is recommended if a large volume of data is to be sent to the server frequently.
To reduce high expenditure, patronize a dedicated server host provider that can give you bandwidth packages that can carry the data load you will be transferring. GTHost actually has your need to settle with our varying dedicated server capacities for your use.
Large RAM capacity is always beneficial. Tools like Couchbase will take on processing in memory and this will be fast if they won’t be reading and writing to storage due to RAM insufficiency. Applications that analyze big data will always make use of as much RAM and available. A dedicated server with 64 gigabytes or more of RAM capacity is of preference when dealing with production tasks, though this is not a static rule. Our GTHost experts will help you in choosing what package suits you best.
It is best if your server has enough space for analyzing your data. It is ideal for the space to be enough so as to take up additional data created during the analysis process. It is preferable to have fast storage but it is not always necessary to stock your dedicated server with terabytes using SSD storage.
It is also advisable to use spinning hard drives, though slow and doesn’t cost much they could still fit into your storage needs.
Tools such as Spark spreads processing tasks across many threads. These tasks are performed across the machine’s cores in parallel. Spark will make do of servers that have a minimum of 8 to 16 cores but this may increase based on the size of load it is processing. Using many cores will enhance performance better than using a small number of more powerful cores.
GTHost has all your optimizing needs taken care of with our different server packages to suit your budget and big data management needs. Contact us and you will be glad you did.