Volume
As the volume of data is increasing, we need to plan for the storage and processing of terabytes or petabytes of data in real time. This is a characteristic of Big Data. However, in modern times cheap storage and processing is possible, and there are multiple vendors
and solutions available in the market with the capability of catering to scalability, distributed storage, and high processing querying requirements. Organizations will turn to cloud computing. Organizations will turn to cloud computing to process and cloud storage to store data for real-time reporting. Data scientists can dig deep and analyze data to find patterns and create predictive models to see future directions and prescriptive models. The amount of data that needs to be stored for prescriptive analytics is big. With the advent of cloud solutions, this volume of data can be handled with modern solutions.
Data Value
Data value is the ability to transform data into information and then into insights, and to create value from data when and wherever and in whichever format required. This means, that safe and secure support for business intelligence reports, automation and artificial intelligence (AI) capabilities is essential in modern times.
At a minimal level, data value is the ability to create reports and dashboards. At the second upper level, it is an ability to automate manual activities. At the third level, it is the ability to integrate with code and create predictive models, use statistical methods, and enable machines to improve with experience and learn to make integrated decisions. Having the ability to offer multi-language support and create queries—e.g., SQL, Python, R, etc.—is a value addition for business.
Fault Tolerance
Fault tolerance is the ability of a system to continue operating properly in the event of the failure of one or more of its components. A fault-tolerant design enables a system to continue its intended operation when some part of the system fails. Fault tolerance is an important consideration for any system that must be highly available. By incorporating fault tolerance into the design of a system, it is possible to reduce the risk of system downtime and ensure that the system can continue to operate even in the event of failures.
Managing, processing, and storing large-scale data requires high availability, reliability, reduced costs, and improved security in data warehouses. One of the performance points is handling the faults that occur during computation; e.g., handling disk failures. If a unit of work fails in hardware, then the system must automatically restart the same task on an alternate node and not restart the entire query from the beginning. This is a critical value addition and differentiating factor in systems handling real-time databases; e.g., payments, orders booking, etc.
