Cloud Analytics: Challenges and New-age Solutions

The data landscape has continually evolved with changes in the ways that businesses operate. What used to be a manual recording of data in registers has now transformed into a stream of data flowing in from a huge variety of sources. Management of massive volumes of data has always been a priority issue for data enthusiasts. And this has led to the evolution of data storage and analytics technologies.

Cloud Analytics is one such concept brewing out of the endless need to manage endless data. In this blog, we shall talk about Cloud Analytics, its benefits over the traditional On-Premise data lakes, the BI related challenges on the cloud and the Magic Wand that can help to overcome them!

What is Cloud Analytics?

Simply put, Cloud Analytics is the process of data analysis on the cloud in real time. A cloud is a remote computing resource. It essentially constitutes data centers comprising of servers which are accessible over the internet, publicly or privately for storage and computing needs. Business Intelligence tools are then used to analyze this data on demand, from the cloud. 

Over the years, there has been an increase in the volume of consumer purchases over the online space and the integration of offline and online customer journeys. This has led to an increasing need of maintaining large database of customer credentials for the purpose of marketing, supply chain management and lead generation. Moving big data infrastructure on the cloud for their data storage and analysis needs has thus become more of a necessity than a choice, for today’s organizations. 

Why Cloud Analytics?

Following are the major benefits of analyzing your big data on the cloud:

• Cost-effectiveness: Cloud analytics platforms usually charge a subscription based on  storage or compute consumption. This reduces infrastructural costs to a great extent by charging you only for resources that you use. 
• Easy scalability: Unlimited storage combined with elasticity makes cloud more scalable than On-Premise data lakes.
• Remote access: Cloud analytics lets your team access data even remotely. This reduces the need for relocating data to another analytical environment.
• Reduces infrastructure management tasks: The cloud service provider takes care of all the back-end administrative tasks such as back-ups, disaster management, infrastructure health and maintenance, etc. reducing the data infrastructure management load.
• Easy integration: With increasing volumes of data, cloud analytics lets you easily integrate data sources and respond to changes faster. This makes your data platform more agile.

The scalability, elasticity, agility, comprehensiveness and easy accessibility of the cloud makes it a more viable option than data lakes. And with a shift to cloud, organizations find Cloud BI to be critical to their current and future endeavors. But Cloud BI does not come without its own challenges.

The Ifs and Buts of BI on Cloud

Cloud can help you accommodate big data and scale up as much as you want but digging out insights from this data is a tricky task. Conventional BI tools can help you gather, organize, analyze and present insights from big data on the Cloud and use it in decision making. However, as more and more data lands into the cloud, BI tools start facing critical performance issues.

As volume and concurrency of data on the cloud increases, the time required by existing BI tools to process a query also increases making them slow. All the heavy lifting on the data is done in run time because of which queries take too long to return. This results in low performance and limited capacity of BI tools in processing the growing data on the cloud. And analysts find it difficult to gain access into critical insights necessary for timely decision making.

Enhancing the Power of Cloud Analytics 

To overcome BI challenges on the cloud, organizations need to rely on a reliable solution like the next-generation Smart OLAP technology for Cloud. Smart OLAP is a powerful concept that helps you accelerate BI performance  on the cloud by building multidimensional data cubes. These cubes contain pre-aggregations of data across all the possible combinations and cardinalities. As the heavy lifting on data is done in advance, the query processing time gets reduced, generating responses is less than a second. Also, the Smart OLAP layer can be built directly on the cloud which means that the data cubes can be built and stored in the cloud itself, eliminating memory limitations. 

And that’s how Smart OLAP empowers your existing BI tools to help you leverage the power of cloud analytics.