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Recently, there had been many queries from old and new customers regarding upgrading their databases. What, we at CoreIT understand from such queries is that people are often clue less or have an unrealistic assumption about the migration of database to Cloud.
Today we give a short brief about the different aspects that enterprises must be aware while opting for Cloud-based data analytics.
Cloud-based data analytics have the ability to process in real time and can operate with technology such as machine learning and predictive algorithms. But cloud-based data analytics is a lengthier and tougher path than it may seem to be. Here’s why.
Tons of Data
The first and foremost reason is that the transfer of data from enterprises to the public cloud is a huge task than it is often anticipated. It involves huge amounts of human labor and constant monitoring; even with tools like AWS snowball or other offerings from Google or Microsoft the processes still need to move through tons of data, which is just a start and a scratch to the surface of cloud-based data analytics.
Data Integration
Data integration is a big issue in the cloud as moving the data doesn’t automatically resolve integration challenges. The task of on-premise systems to be synced with the data stored in the cloud in a timely manner is humungous and needs to be done systematically for anticipated results. This often involves tailoring old and new data-integration technologies to set up processes that include data movement and structure transformation.
Intricacy due to security
Lastly, cloud-based analytics databases are complex and difficult to configure due to the security sub systems in the database. Security needs to be systemic with the entire data analytics systems, both in the cloud and on premises, which itself increase the complexities in real time.
CoreIT believes cloud analytics challenges can all be overcome but IT has to understand the level of effort needs to be thorough for getting results
