October 6, 2019
Big Data, no longer just a buzzword or a promise of exciting things to come, is something that many businesses leverage daily in this modern age.
With the advent of the digital age, high volumes of data are generated daily from the internet, social networks, healthcare applications, sensors, and various other sources.
Big Data refers to datasets that are varied and complex to such a high degree that they cannot be manipulated with traditional data processing solutions. This explosion of diverse data is what businesses routinely use to create, fine-tune, and implement various plans and strategies.
While things are still relatively at an early stage with Big Data, some challenges and issues need solutions going forward. A lot of these challenges have to deal with issues of privacy and security. Coping with these safety challenges can have a significant impact on the future use and potential of Big Data.
At the outset, it is essential to understand some crucial characteristics of Big Data projects that can explain the safety challenges that can come with the territory. Here are some important points.
It is essential to recognize the safety challenges that can arise out of these factors and acknowledge that with the exponential increase in the generation of data, more and more of it would need to be protected.
Since the advent of Big Data, the scale of it has faced constant change. As the scale of data generation keeps increasing exponentially, an event like a leak can bring catastrophic consequences.
The challenges of storing and managing Big Data come mainly from the appropriateness of the storage solutions. In many cases, this data is stored using solutions that employ a horizontally scalable, distributed storage platform.
Mostly in cloud storage applications, solutions like Tachyon, QFS, HDFS, Ceph, GlusterFS can provide the storage volumes and scalability needed. However, this does not necessarily mean that they satisfy the security and concurrency requirements that are ideal.
Furthermore, storing data in the cloud can itself present safety challenges. In many cases, data needs to be moved across cloud and local storage for processing, and this opens up opportunities for unauthorized access.
The owner of any data is liable to lose control over the information when a cloud service is used for storage. The right storage solutions need to implement reliable means for the data owner to test the data's safety and integrity, using solutions like checksums, digital signatures, trapdoor hash functions, message authentication codes, or Reed-Solomon codes.
While large enterprises control a lot of Big Data, data is also frequently shared across businesses regularly. However, there is very little in terms of supervision or specifications regarding the sharing and use of that data. The self-discipline of enterprises often becomes the only determining factor behind safe practices.
The sheer scale of Big Data can further compound safety challenges with long transmission times across possible vulnerable pipelines. Many enterprises process Big Data in place and transmit only the analytical results or classify the data into smaller parts and transmit only the data relevant for analysis downstream.
For competitive advantage, companies can share business and individual data. This can increase the risk of disclosure of personal or proprietary information. The need of the hour can be reliable technology and a safe, secure environment.
There is a lot that organizations can do to identify key problem areas in Big Data safety and implement the appropriate solutions. The implementation of encryption can be a simple move that has far-reaching consequences with solving significant problems. Securing endpoints, networks, applications, and physical sites can bring the table an all-around approach to overcoming these safety challenges.
A lot of data security technologies have also evolved with time, becoming more scalable and flexible in dealing with the requirements of Big Data operations. Here are some techniques worth taking a look at.
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