When talking about websites or online shopping platforms, one of the main factors that can affect a customer’s decision is site speed. Immediate response is the norm and a latency of even a few milliseconds can cause revenue loss or even loss of customers. This is why in-memory caching has become a familiar method for both developers and business owners alike.
The biggest challenge with business growth is that the amount of data and application usage grow along with it, bringing new challenges in enterprise data management. As a website becomes bigger and acquires more users, the risk of performance degradation increases.
Businesses must find a solution that will deliver optimal latency levels and help manage unplanned peaks in usage. Data engineers can use caching techniques to enhance the performance BI tools and applications.
The global pandemic has affected businesses the world over, with some being forced to phase out some services or even shutter its stores. It’s predicted that, for the coming years, technology trends will experience a boom, including personalization, contactless business, and artificial intelligence (AI).
With most companies now doing business online, it’s predicted that, by 2024, enterprises will shift to performing analytics online through tools that use AI and machine learning.
The eCommerce industry is set to be one of the largest beneficiaries of this shift, as online shopping platforms continue to be on demand. Convenience and accessibility are the main drivers of online shopping, with 43% of online shoppers doing their shopping while in bed, 23% while in the office, and 20% while in the car or bathroom.
Age also plays a major role according to recent statistics, with 67% of millennials expressing a preference for online shopping over shopping in physical stores.
The Benefits of In-memory Caching
The influx of online shoppers have led to a surge in data gathered, and companies need to find solutions that will help manage increasing website traffic and mobile app usage.
Caching will help address this need while also allowing for faster processing and efficient analytics of data. Aside from eliminating bottlenecks in the data analytics process, caching also provides the following benefits:
1. Enhanced performance without requiring a “rip-and replace”
Aggressive caching allows enterprises to maximize existing hardware without disruption in performance.
Even if you use the server where the content originated, you can leverage servers along the delivery path to bear most of the load to ensure quick response times.
2. Faster response times
Caching eliminates the need to go back and forth around the network so that data can be retrieved faster.
If caches are maintained close to the user, retrieval becomes near-instant. Using a browser cache is a perfect example of this feature.
3. Availability during network interruptions
Caching allows setting up policies so that data can still be served to end users even if it becomes unavailable from the origin servers.
4. Decreased network costs
Because data is cached at various points in the network path between the data consumer and its origin, there’s less network activity beyond the cache. This becomes more prominent when the data is cached closer to the consumer.
The Future of Caching in eCommerce
By allowing for the management of mixed workloads within the same architecture, in-memory caching eliminates the usual bottlenecks experienced in the data analytics process.
This also eliminates the need to separate transactional databases from analytics databases so that data movement within the network is minimized and low latency is ensured.
Today’s businesses can use caching to gain better insights on customer demographics and evolving purchasing behavior. This is especially important in the current business landscape, as more people turn toward online shopping and become more dependent on their mobile devices.
Businesses should leverage the power of current technologies to determine personalized offers to customers and modify purchase considerations in real time.
Caching, however, can only be effective with proper management. Dynamic access patterns and limited resources can pose challenges, and this can be managed through the eviction of unused data.
Speed is the name of the game in eCommerce, and the online shoppers of today expect nothing less than instant or real-time responses. To achieve this, eCommerce systems must be able to handle millions of user requests and still provide reasonably immediate, if not real-time, responses.
Access to live data is required, and this is where conventional data warehouses and database architectures fall short. In-memory caching can save the day by helping improve scalability, availability, and performance for both applications and BI tools.
In light of the shift to online transactions, the data-driven approach is a common method employed by businesses who want to win big in the data game. eCommerce, in particular, shows great potential to be one of the most lucrative channels at present and for years to come.
As competition becomes more intense, online retailers must find ways to remain profitable while providing the best service at a price the customers want. In-memory caching helps empower businesses to harness data and data-processing technologies to promote growth and thrive in a virtual world.
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Author Bio: Edward Huskin
