Insights about Updates

Updates November 21, 2020
While Big Data is responsible for data storage and processing, the cloud provides a reliable, accessible, and scalable environment for Big Data systems to function. Big Data is defined as the quantity of digital data produced from different sources of technology, for example, sensors, digitizers, scanners, numerical modelling, mobile phones, the Internet,  videos, social networks. Cloud Computing and Big Data are complementary to each other. Rapid growth in Big Data is regarded as a problem. Clouds are evolving and providing solutions for the appropriate environment of Big Data while traditional storage cannot meet the requirements for dealing with Big Data, in addition to the need for data exchange between various distributed storage locations. Cloud Computing provides solutions and addresses problems with Big Data.  The Cloud Computing environment is expanding to be able to absorb big amounts of data as it follows the policy of data splitting, that is, to store data in more than one location or availability area. Cloud Computing environments are built for general-purpose workloads and resource pooling is used to provide flexibility on demand. Therefore, the Cloud Computing environment seems to be well suited for Big Data.  Big Data processing and storage require expansion as the cloud provides expansion through virtual machines and helps Big Data evolve. This is a consistent relationship between them. For the cloud environment to fit with Big Data, the Cloud Computing environment must be modified to suit data and cloud work together. Many changes are required to be made on the cloud: CPUs to handle Big Data and others.  Big Data and Cloud Computing are among the most important stages of IT development. Information privacy and security are some of the most important issues for the cloud because of its open environment with very limited user control. Security and privacy influence Big Data storage and processing because there is a vast use of third party services and the infrastructure used to host important data or to perform operations as growing data and application growth bring challenges.  There are multiple benefits of Big Data analytics in Cloud: Improved analysis- With the advancement of Cloud technology, Big Data analysis has been upgraded causing better results. Hence, companies prefer to perform Big Data analysis in the Cloud. Moreover, Cloud helps to integrate data from numerous sources. Simplified Infrastructure- Big Data analysis is a tremendous strenuous job on infrastructure as the data comes in large volumes with varying speeds, and types that traditional infrastructures usually cannot keep up with. As Cloud Computing provides flexible infrastructure, which we can scale according to the needs at the time, it is easy to manage workloads. Lowering the cost- Both Big Data and Cloud technology delivers value to organizations by reducing ownership. Cloud allows customers for Big Data processing without large-scale Big Data resources. Hence, both Big Data and Cloud technology are driving the price down for enterprise purposes and bringing value to the enterprise. Security and Privacy- Data security and privacy are two major concerns when dealing with enterprise data. Moreover, when your application is hosted on a Cloud platform due to its open environment and limited user control security becomes a primary concern. Hence, nowadays the system integrators bring in a Private Cloud Solution that is Elastic and Scalable. Furthermore, it also leverages Scalable Distributed Processing. Big Data and Cloud Computing constitute an integrated model in the world of distributed network technology. The development of  Big Data and its requirements is a factor that motivates service providers in the cloud for continuous development because the relationship between them is based on the product, storage and processing as a common factor. Big Data represents the product and the cloud represents the container. Cloud computing represents an environment of flexible distributed resources that use high techniques in the processing and management of data and yet reduce the cost. All these characteristics show that cloud computing has an integrated relationship with big data.  Both are moving towards rapid progress to keep pace with progress in technology requirements and users.   Big Data And Cloud Computing: A Perfect Combination While Big Data is responsible for data storage and processing, the cloud provides a reliable, accessible, and scalable environment for Big Data systems to function. Big Data is defined as the quantity of digital data produced from different sources of technology, for example, sensors, digitizers, scanners, numerical modelling, mobile phones, the Internet,  videos, social networks. Cloud Computing and Big Data are complementary to each other. Rapid growth in Big Data is regarded as a problem. Clouds are evolving and providing solutions for the appropriate environment of Big Data while traditional storage cannot meet the requirements for dealing with Big Data, in addition to the need for data exchange between various distributed storage locations. Cloud Computing provides solutions and addresses problems with Big Data.  The Cloud Computing environment is expanding to be able to absorb big amounts of data as it follows the policy of data splitting, that is, to store data in more than one location or availability area. Cloud Computing environments are built for general-purpose workloads and resource pooling is used to provide flexibility on demand. Therefore, the Cloud Computing environment seems to be well suited for Big Data.  Big Data processing and storage require expansion as the cloud provides expansion through virtual machines and helps Big Data evolve. This is a consistent relationship between them. For the cloud environment to fit with Big Data, the Cloud Computing environment must be modified to suit data and cloud work together. Many changes are required to be made on the cloud: CPUs to handle Big Data and others.  Big Data and Cloud Computing are among the most important stages of IT development. Information privacy and security are some of the most important issues for the cloud because of its open environment with very limited user control. Security and privacy influence Big Data storage and processing because there is a vast use of third party services and the infrastructure used to host important data or to perform operations as growing data and application growth bring challenges.  There are multiple benefits of Big Data analytics in Cloud: Improved analysis- With the advancement of Cloud technology, Big Data analysis has been upgraded causing better results. Hence, companies prefer to perform Big Data analysis in the Cloud. Moreover, Cloud helps to integrate data from numerous sources. Simplified Infrastructure- Big Data analysis is a tremendous strenuous job on infrastructure as the data comes in large volumes with varying speeds, and types that traditional infrastructures usually cannot keep up with. As Cloud Computing provides flexible infrastructure, which we can scale according to the needs at the time, it is easy to manage workloads. Lowering the cost- Both Big Data and Cloud technology delivers value to organizations by reducing ownership. Cloud allows customers for Big Data processing without large-scale Big Data resources. Hence, both Big Data and Cloud technology are driving the price down for enterprise purposes and bringing value to the enterprise. Security and Privacy- Data security and privacy are two major concerns when dealing with enterprise data. Moreover, when your application is hosted on a Cloud platform due to its open environment and limited user control security becomes a primary concern. Hence, nowadays the system integrators bring in a Private Cloud Solution that is Elastic and Scalable. Furthermore, it also leverages Scalable Distributed Processing. Big Data and Cloud Computing constitute an integrated model in the world of distributed network technology. The development of  Big Data and its requirements is a factor that motivates service providers in the cloud for continuous development because the relationship between them is based on the product, storage and processing as a common factor. Big Data represents the product and the cloud represents the container. Cloud computing represents an environment of flexible distributed resources that use high techniques in the processing and management of data and yet reduce the cost. All these characteristics show that cloud computing has an integrated relationship with big data.  Both are moving towards rapid progress to keep pace with progress in technology requirements and users.  
Updates November 20, 2020
Artificial intelligence is the hypothesis and practice with regards to building machines equipped for performing tasks that seem to require intelligence. At present, cutting edge technologies endeavoring to influence this reality to incorporate machine learning, artificial neural networks and deep learning In the meantime, blockchain is another documenting framework for computerized data, which stores information in an encrypted, distributed ledger format. Since information is encrypted and distributed across many different computers, it empowers the making of carefully designed, exceptionally robust databases which can be read and updated only by those with permission.  Every innovation has its level of complexity, however, the combination of the two might be advantageous to both. Although much has been composed from an academic point of view on the capability of combining these ground-breaking technologies, real-world applications are scanty right now. Nonetheless, I expect this situation to change shortly.    AI, BLOCKCHAIN AND DATA It’s fair to say we find ourselves in an AI revolution of sorts and this is mostly because of the advancements being made in the field of Big Data. The handling of data is an undeniably hot topic and businesses dealing with it – for commercial reasons or something else – have a legal and moral responsibility to safeguard it.  The emergence and abundance of data have catalyzed blockchain as a viable data storage solution. Not at all like cloud-based solutions, the data on a blockchain is broken up into small sections and distributed across the entire computer network. Each computer or node holds a complete copy of the ledger and no central authority or control point is meaning that if one or two nodes are undermined, data will not be lost. All that takes place on the blockchain is encrypted and the data cannot be altered. Essentially, this means blockchains are the ideal storeroom for sensitive or personal data which, if processed with care with the use of AI, can help unlock profitable bespoke experiences for consumers.   TRACKING POWER Decisions taken by AI systems can be troublesome for people to comprehend, however, blockchain can shed new light on this by helping us track the thinking process, and understand decisions. Being able to record AI’s decision-making process on a blockchain could be a noteworthy step towards increased transparency. In this instance, blockchain would fill a similar need as the board, with the exception that the data composed on the latter can be modified or erased whereas on the blockchain it would be immutable and permanent.   COMPUTING Finally, even though they are incredibly useful in our daily lives, computers are unable to carry out a task without getting explicit instructions. If you were to operate a blockchain, with all its encrypted data, on a computer you’d need a lot of processing power. The hashing algorithms used to mine Bitcoin blocks, for instance, take a “brute force” approach – which consists in systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement before confirming a transaction. AI allows us to move far from this and tackle tasks more progressively and efficiently. Imagine a machine learning-based algorithm, which could practically sharpen its skills in ‘real-time’ if it were fed the appropriate training data. Even though blockchain and AI have extraordinary potential in their own right, one can't resist the urge to ponder what they may accomplish if their consolidated power were put to great use. The two technologies are mutually inclusive, and could potentially pave the way for a much more transparent, and efficient world.     

Pin It on Pinterest

Contact Us

Pin It on Pinterest