The world is becoming sophisticated with the use of big data which is helping in innovation of artificial intelligence (AI), and the internet of things. Greater data generation is helping us to better understand the world and make precise predictions. For instance, big data can easily be used to make predictions about an organizations’ most favorable future investment.
Looking from the point of view of an organization, the extent to which we rely on our devices has increased drastically. The increase in the exchange of information not only puts pressure to make newer systems which handle greater bandwidth, but are at the same time capable of covering areas which were previously unreachable. Communication is a key element for enterprises; an always on-premise paradigm has been developed and being used in the world now. There are huge potential benefits for businesses for using this greater data for communication, but still there are great challenges related to big data which need to be looked into.
Big Data Challenges
Businesses are trying to capitalize on big data for greater growth and success. However, organizations are facing many challenges. There are two basic categories in which the problems can be divided which are further explained in each category.
Having the great amount of information is just not enough, it is necessary to provide meaning to the information. Just having the data is not enough, it needs to be determined what data (both structured and unstructured, and internal and external) to use for different decisions. Various data scientists need to be hired who are good at managing the structured and unstructured data, while creating insights about the data. Even the extremely experienced data scientists cannot work alone; cloud storage solutions provide the level of accuracy needed to give meaning to the data. Total backup solutions are needed by big storage companies, an example is the StoneFly DR365 appliance; it provides a total backup solution for all the physical and virtual servers and workstations in a single box.
Big data is not limited in amounts, it is huge and hence needs to be handled in that way. Cloud backup services provide the best solutions to deal with the large volumes of data, velocity and variety of big data. Technological challenges are linked with the presentation and usage of big data. For instance, even after investing in big data and making out the needs of the organization, the analysis of big data needs to be put in a presentable form for making decisions. Organizations need to make visual models and take the help of visualization to bring meaning to big data. Organizations can however look for analytics enabled clouds like Amazon AWS which automatically provide analytics of the data stored in the AWS cloud.
Cloud Solutions for Big Data
With the turn of the century, big data has gained paramount importance and businesses have prioritized big data backup and disaster recovery in the cloud. Various partnerships have been made by cloud storage companies such as Amazon AWS and StoneFly to provide the customers with secure off-site backup and replication.
- Optimization: Big data needs optimization and speed to be helpful and meaningful in the most efficient and effective way. Cloud backup services allow centralized management with the use of data in the hands of authorized personnel. So, there is no challenge faced by the organization when dealing with large amounts of big data.
- Increased Storage Capacity: Cloud based solutions for data storage provide storage which can scale-out according to the needs of the user. Greater big data can easily be stored with cloud storage which can scale-out. Users do not have to worry about over-provisioning or under-provisioning storage; they can be relieved of such problems and increase storage as they go.
- Multipart Uploads: The large objects of big data can easily be handled with multipart uploads of data for cloud backup. All users are provided with the most intact cloud storage services. Multipart uploads basically allow for the division of data into chunks which are uploaded in parallel threads which resolve the problem of slow internet addressing the problem of disconnection.