2018 was a year full of valuable lessons. Businesses learned even more about cybersecurity, privacy, and innovation. In 2019, companies should use all the information they have discovered and take action towards improvements, starting with their databases.
Databases are the most crucial element in overcoming similar issues in the coming year. Many things could happen, and database managers and developers should take these database management musts into account to be ready.
NoSQL and Microservices
Living on flexibility and characterized by being highly available, microservices is the new obligatory software architecture. The mindset of a microservices design goes hand in hand with the dynamic thinking of the nonrelational data method.
As unstructured data continues growing, and more applications are launched from cloud platforms to support the optimal use of resources to the application layer, the increasing attraction of cutting-edge developers to nonrelational thinking will continue.
Distributed Cloud Databases
A distributed cloud database is a database in which operational data is spread across different physical locations.
Cloud-native companies are leading the way. Traditional businesses have been forced to become technology companies and operate at cloud-scale to accommodate millions of customers. Now, all companies need to operate at cloud-scale to compete and succeed, and they’re leveraging cloud-scale technology innovations.
To accomplish parity and achieve true competitive advantage, scaling relevant solutions for their customer base will be necessary. Companies that do not move at cloud-scale are doomed to fail.
Across the globe, companies have felt the pressure to ensure data is compliant and secure. Compliance standards such as the General Data Protection Regulation (GDPR) have made it crucial for these companies to protect their data and monitor by whom and where it is being used.
Therefore, databases should keep accurate logs that track who is accessing data, where they’re accessing it from, how they’re accessing it and why. Failure to follow the correct policies and processes when it comes to database compliance can lead to lawsuits and fines.
No one can argue that AI is now the hottest buzzword in IT. Almost every IT product is now suddenly AI-enabled. SQL Server is no exception; it has become the first RDBMS [relational database management system] with built-in AI.
What does built-in AI for an RDBMS entail? It refers to the AI functionality provided by the Machine Learning Services component of SQL Server 2017, which allows users to incorporate machine learning and AI libraries written in R or Python into routines that can run on SQL Server systems. This enables analytics applications to be executed where the data is hosted rather than needing to first surface it to another application layer. SQL Server DBAs and developers need to understand how these AI design patterns can work with the database platform.