Building scalable enterprise digital commerce for a global pharma giant
Digital transformation around the e-commerce services across multiple geographies and business divisions for a globally leading pharma company.
As more and more businesses these days consider opportunities to leverage data, it impacts resources and results in poor loading and response times. Capacity planning helps in determining the optimum utilization of resources. It also plays an important role in decision-making. It can be classified as short-term, long-term and medium term based on the duration taken into consideration.
The ultimate goal of capacity planning is to maintain the current and future level of requirements at minimal wastage. It is dependent on a variety of factors like human resources, job design, production technology, scheduling, quality assurance etc.
Big data is turning out to be an important part in the industry for information management and analytics. It offers a bunch of options for IT simplification and cost reduction in addition to new business related capabilities. Therefore, it is important to carry out big data capacity planning as the first step, whenever big data projects are taken into consideration.
The big data cluster capacity planning methodology addresses forecasting and workload characterization. Large quantities of varied data structure and types require more number of systems to process them.
For high and multiplied efficiency, high throughput hard drives with an underlying file system that supports the modern read and write pattern are required. It also needs support from a fast network layer in order to cope with immediate data transfer.
The big data helps plan capacity planning by ensuring:
