The key task was to update a database up to Microsoft SQL Server 2017. As the client works in an industry with strict regulations, we had to study the niche and detect all possible risks related to cybersecurity and sensitive information protection.
The main system based on SaaS had a function of automated data processing that was not presented in another system. The data architectures were completely different and we had to find an answer to the question of how to unite databases with different data formats and resources. We initiated the process of data transformation.
In order to unite all the data of the organization, our team started to unify data. This process presupposed the structuring of unprocessed data from multiple flows, including incomplete and isolated data sets.
As a result, we built a new data architecture that corresponds to the needs of the company. Then, we proceeded to migration of a database from SQL Server 2014 to SQL Server 2017.
We defined the following stages:
Migration to a new MS SQL Server let the system faster process bigger volumes of data. Optimized SQL requests allowed us to reduce the time required for execution which ensures immediate software reaction to user’s requests.
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