SMEs: How to Back Up Big Data?
Most SMEs are still new to Big Data. To experiment safely with all possible ideas and options, they must first and foremost be able to protect all their data.
Big Data promises new opportunities for small and medium-sized businesses to help them stay one step ahead of their competitors. The only idea that they can reap benefits is now enough to convince them to embark on the adventure.
They can quickly benefit from effective practices and structured and recognized approaches in the sector. It is important to note, however, that if errors or failures can corrupt the data, companies should be able to revert to a later version to continue the analysis. This action must also be independent of the underlying technical infrastructure, and the risks remain similar, that a large data analytics company relies solely on the services of the largest cloud providers it associates its own hardware and software with cloud services or manipulates the most important elements on its own infrastructure.
The importance of data backup
The greatest danger does not come from platforms, services or infrastructures, as application developers and cloud service providers have implemented many preventive maintenance methods over the years, which allow them to guarantee the best availability of their Big Data modules. Here, it is the human factor that has the greatest impact.
Indeed, in a recent study, Kroll Ontrack, a data recovery company, points to human errors as the most important cause of data loss (84%): a careless mouse click or misconfiguration of the system and the essential data of the company fly away.
Data analysts want to be able to work with new algorithms, to have a fresh look at information and, ideally, to acquire new knowledge. With the multitude of steps required for analysis, all kinds of errors can occur at any time and thus corrupt the database, or simply produce unnecessary results.
With a lack of backups, the consequences can be significant. For example, a business in the distribution sector had to re-perform a full inventory of the situation in all its subsidiaries, as individual entries were corrupted in the Big Data environment. No one could say with certainty what values were accurate and all the data had to be reviewed again. As a result, the company quickly decided to back up its Big Data.
The other risks are probably already well known for other uses. Essential parts of the infrastructure, such as the database, may fail or be hacked. Application developers are launching new versions and features on the market. During updates, problems such as errors can occur, and make the Big Data module inoperative.
In any case, it is wise to quickly return to a previous version to resume analysis. Finally, a data analyst may want to save and archive the particular state of an important analysis in order to review it later.
Welcome to the Big Data universe
Most SMEs are investing in an application module of leading cloud service providers to gain their first Big Data experience without having to commit a lot of resources. Whether it’s Amazon, IBM, Google or Microsoft, each of the service providers allows companies to start with controlled investments and choose a purely cloud-based model.
Suppliers themselves operate under a shared responsibility model, in which the company has a responsibility for data and compliance. In case of corruption or loss of data, the responsibility for the reconstruction lies with the company itself.
The Truth in Cloud study clearly demonstrated that. While vendors often provide organizations with built-in protection methods for application modules as a feature, each cloud provider uses a different approach with its own policies and consoles that are incompatible with other environments.
For example, if a company uses cloud-based Big Data analytics from different vendors, cloud teams will have to deal with different technologies and understand what will actually happen when they restore that data.
Save, yes, but how?
Whether purely cloud-based or shared-use, or located in the data center, each of these Big Data approaches are highly dynamic, mostly distributed, large, virtualized, and rapidly growing systems. traditional backups to their limits.
If the backup provider dominates all modern and legacy platforms, workloads, and various large data architectures, the enterprise can centrally cover and control all backup requirements with a single piece of software. will reflect massively in the operating costs of the backup.
All in all, these features are essential to cover today’s large data environments and at the same time to guarantee the company that the concept of backup is future proof.
