Cloud migration is now the most preferred option being used by enterprises worldwide.

With the advent of digital transformation and increasing customers for online services, migration from on-premise processing to the cloud has gained momentum. All know that cloud infrastructure is advanced, sophisticated, and available on demand, making the technology viable and affordable. Hence moving big data analytics to the cloud is only an expected outcome of total digital transformation.

Hesitation in Shifting from On-Premise to Cloud

Though migration to the cloud and adapting to the concept of ‘as a service’ is being adopted by enterprises for the deployment of an application, there has been a certain hesitation inmoving data for analytics to the cloud. ‘Why fix when nothing is broken’ is the basis of the initial reluctance, and having data on-premise is still considered more secure. Of course, it is generally accepted that the cloud offers scalability and performance advantages for big data analytics, but privacy and cyber security are nagging afterthoughts for any migration.

The First Step Is the Toughest

Cloud vendors have realized that moving big data to the cloud should be a step-by-step or baby-steps approach. Initially, micro-projects involving small chunks of data should be migrated to the cloud, so the customer gets an actual feel of cloud-based data analytics. The experience of having a successful migration and seeing the actual advantages results in changing the perception in favor of big data in the cloud. The first step is the toughest, but soon enterprises start expanding their data analytics operations to the cloud and away from legacy on-premise systems. The benefits of speed, reliability, and scalability are appreciated though concerns of privacy, availability and data security need to be addressed in a systematic and performance-based manner.

The Customer Begins to See the Advantages

After the trial run of using the cloud for big data analytics, most customers begin to fathom the distinct advantages of using the cloud. Even though the comfort zone still exists for on-premise infrastructure, the possibilities that open up via cloud migration are an attractive proposition.

Scalability is one of the dominant factors driving customers to the cloud for data analytics. Scalability is crucial for big data projects, but the clincher is that scalability requirements can be requested and met on demand. So the pressure of projecting accurate scalability is tremendously reduced as infrastructure expansion and upgrades are all available at a request away.

Customers also love the idea that downgrades of infrastructure and services are also available depending on project or company demands. Scale up or scale down – all easy operations for the customer. This kind of flexible scalability is not feasible with on-premise operations.

Everything is ‘as a service’ in the cloud. This flexible and on-demand availability of IT infrastructure and services is a highly viable and favorable option for customers. Varying business demands, budgets, financial restraints, and other corporate shackles that could impede data analytics projects are now reduced. The ‘order-and-use service model’ for big data analytics projects becomes a very attractive option over on-premise considerations.

Besides, sophisticated features, software, and infrastructure offered by cloud vendors are numerous, and having access to such an array of technology would not be possible with on-premise operations. This vast array of infrastructure, platforms, software, tools, and services available on demand with the scalability as required is a huge plus point over on-premise operations. It would not be affordable nor practical for a customer to acquire on-premise all that the cloud has to offer for a data analytics project literally on ‘click and request.’

The IT teams of customers would naturally choose the cloud option over the on-premise option since everything is already set up and deployed for large data analytics projects. The team can hit the ground running with this model for analytics projects. The convenient part is that, when not required, all IT infrastructure acquired could be switched off and given back at no additional and recurring costs.

Migration and Direction of Data Analytics

The highest use of data analytics is for predictive analytics, business intelligence, and reporting capabilities across numerous sectors such as healthcare and the public sector. There has been an exponential increase in data mining to be done due to the huge increase of users and available services. Keeping up with this explosion requires all analytics projects to ramp up with networking technologies, tools, processing power, and storage.

This kind of upgrade is now most feasible with the cloud as on-premise would be expensive, slow to turn around, and inefficient. The introduction of artificial intelligence and machine learning into data analytics architecture has further complicated matters in terms of demand for processing power, technology tools, and other IT infrastructure. The customers are convinced that these demands for advanced data analytics can only be met via migration to the cloud.

Big data analytics for social media analysis is on the increase as well. Social media data is huge and constantly on the increase. Mining this data, along with all the analysis required and mixed with artificial intelligence and machine learning, makes the cloud the clear platform winner for the way forward for data analytics.

Bottom Line – A Mix of Many Technical Worlds and Expertise Is Needed

The way forward for any data analytics project is a mix of many worlds, all converging on data. Kyyba excels at Data transformation, End to End Data testing covering a traditional database or data warehouse or data lakes, orchestrating data pipelines to move large volumes of data from different sources to a target destination, Setting up & running training & deployment environments for AI/ML Models and cloud migration are now linked to an intricate web of sophisticated technologies.

All these capabilities take advantage of the cloud to process large volumes of data, which is reliable, trustable, and durable to offer that report or dashboard to your customers which shows accurate results that power their business. Get in touch to learn more.

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