The COVID-19 outbreak has altered the way organizations do business, indeed so fast that organizations are still finding it stressful to cope with the sudden changes. Now businesses are struggling to adjust to a new normal, which often changes quickly to meet peaks or drops in consumer demand. Although moving rapidly is of utmost importance, there is also the possibility of unnecessary cost or loss of revenue due to efficiency discrepancies, inadequate process management, or subpar client experiences.
The ongoing economic downturn has sparked an outcry for cost-cutting throughout companies. Data lakes ranging from on-premise environments to a public cloud platform have begun to flourish, always trying to keep infrastructure and operating costs minimal while offering versatility for the organization. Traditional data lakes were developed on-premises at several large companies, with dynamic workflows in place representing numerous business units. In all of these conditions, the on-site infrastructure is also overloaded, leading to a rise in aggregate maintenance costs. The data-driven organizations are increasingly embarking on new and unanticipated workloads.
If organizations solely depend on the on-premise infrastructure, there are chances that it may fail to keep up, as it is time-consuming to provide new infrastructure and involves huge operational cost for each hardware and software. While the guarantee of a public cloud provider with a regulated elastic infrastructure sounds brilliant, but when we scale up in this scenario the costs quickly start to add up.
Every organization aims at having its cost under control even when the data keeps on increasing. Hybrid cloud can be managed efficiently using the following recommendations:
Migrate to public cloud infrastructure
A complete elevation of the on-site environment and migration to a public cloud might sound appalling. But often, costly storage, network, and operating costs overweigh the benefits of elastic computing infrastructure. Maintain a foot in both environments by migrating some workloads to leverage cloud computing from a busy data lake on-premises.
Data locality
A core concept for the initial architecture of data analytics was that you get value from the data locality. There is no locality in the situation where computing workloads are transferred to a cloud and isolated from the data. In the sense of a public cloud, performance gains are converted into cost savings achieved by elastically scaling down the compute, if not required. Highly distributed caching capacity organizes hot data automatically to be similar to performance computing while keeping cold data in affordable storage.
Formulate security solutions.
Vendor-lock can be avoided with the help of open-source software as well as abstraction. But, it is equally important to have adequate security features in the infrastructure to safeguard the organization’s data. Security planning and security access methods are both mandatory to secure the data.
Pick the best tools for the workloads, and reconsider the current cloud strategy if necessary, as a step to stimulate the growth of your business.
