3 Major Challenges Of Analyzing Big Data On The Cloud

Modern businesses have realized the potential of utilizing Big Data for improved sales and long-term sustainability. In today’s digital age, we’re generating more data than ever and when analyzed this data can uncover hidden insights and discover new possibilities for your business. Companies dealing with huge datasets have been traditionally using on-premise platforms for storing their data. However, with the promise of efficient storage and processing of Big Data on the cloud, most enterprises have or are in the process of adopting cloud for analyzing big data on the cloud.

The worldwide public cloud services market is forecast to grow 17% in 2020 to total $266.4 billion, up from $227.8 billion in 2019, according to Gartner, Inc. Enterprises are readily making this shift as the cloud offers the benefits of lowered costs, ease of accessibility, data scalability, etc. More so, using the cloud for big data analytics strategically enables businesses to save on infrastructure costs and gain deeper insights that can be applied to improve products and customer experiences.

But is shifting to the cloud enough for transforming big data analytics? Certainly not! While many companies have moved to the cloud, they often seem challenged when trying to access all their data. BI tools cannot scale to analyze billions and trillions of rows neither deliver desired query performance. 

Challenges Of Analyzing Big Data On The Cloud

. Slow Business Insights-

One of the most common misconceptions that businesses carry while shifting and analyzing their data on the cloud is they will get instant insights once they make this move. However, that’s not true. While leaving on-prem to adopt the cloud is a great decision, a lot of work needs to be done for achieving the desired output of near real-time insights. When connecting big data on the cloud to traditional BI tools for instant analytics, the outcome for queries often leads to longer waiting times as BI tools are not built to handle such scale. Moreover, if queries are complex in nature, they might take even longer to return responses.

Krynica-Zdroj, Poland – July 11, 2017: Businessman using Google Analytics in the office on the touch screen of his tablet. Google Analytics is the most famous application for advanced web traffic analysis in the world

. Performance Issues-

While cloud companies advertise a lot on the fact that businesses are able to deliver high BI performance simply by moving their data on the cloud, this is not entirely true. You can definitely increase the performance of your data analytics even when analyzing it on the cloud but the problem starts when the size of your data starts increasing. Live connection to BI tool doesn’t work and computation takes a long time. 

. Vendor Lock-in-

An important aspect to think about before moving your data to the cloud is vendor lock-in. It refers to a situation where a customer is stuck with a specific vendor and have only option to use their services or partner services. When enterprises plan to move big data on the cloud, they are often concerned. They need a solution that works with all cloud vendors seamlessly. 

How OLAP Can Resolve These Challenges

As enterprises grow, their data volumes will increase, and this data is only useful it can be accessed and analyzed instantly. In order to interactively analyze massive volumes and complex data models, enterprises a solution that not only accelerates BI but also makes it available on BI tool of choice of their business users.  

Enter OLAP (Online Analytical Processing). Many enterprises and leading businesses are reaping the benefits of OLAP technology to resolve their big data analytics challenges on the cloud. OLAP technology lets you build massively scalable and fully pre-aggregated cubes. As all combinations of data are already aggregated, when a business user fires a query, there is no processing at run time and business answers get fetched in sub-seconds using any BI tool.

Business people are considering business results and investment plans.
Business Performance Concepts

While OLAP is the solution for interactive analysis, but traditional OLAP based tools also result in poor performance when exposed to massive datasets. Therefore, businesses need to choose an OLAP product that can handle the growing scale of their data while delivering the desired BI performance. Storing and analyzing big data on the cloud through a smart OLAP-based BI acceleration layer enables you to achieve instant insights on big data without any compromise.  

Conclusion

Big data analysis, business intelligence, technology solutions concept on virtual screen

Cloud and Big data is undoubtedly a great combination given that businesses are capable of harnessing the power and benefit of both for data analysis. Leveraging big data on the cloud requires enterprises to not just focus on shifting their data and BI on the cloud but also to opt for a modern BI architecture that is capable of handling the scale of your data while delivering business insights in sub-seconds. 

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