Kevin Kline, Database Expert at SolarWinds, explores how automation of modern databases is increasing productivity and improving lives.
Automation and human development are inseparable. From simple watermills that grind corn to make flour, to robot production lines where pre-programmed machines carry out complex tasks – automation is a tangible sign of human progress.
Throughout history, the development of new labour-saving technologies has helped make us more productive and freed us from routine and repetitive tasks. All too often, the history of automation has been synonymous with the mechanisation of physical tasks. From factories and assembly lines to the invention of the printing press, each development has heralded a new era in human history.
But automation doesn’t just begin and end in the physical world. The advent of the digital world has set automation on a new path of exploration. You only have to look at artificial intelligence for IT operations (AIOps) – the collection of technologies, tools, and processes that are employed to manage and automate IT operations at the enterprise scale – to see this in action.
Just like automation in the physical world, these digital tools help make our teams more effective and free us from routine tasks to pursue more valuable, creative, and fulfilling work.
And one area where that’s certainly true is in the use of automation and AI in databases.
Databases and the lessons of automation
Databases are essential to any successful IT environment. With an estimated 328.77 million terabytes of data collected every day, not only is it critical to find ways to store this information, it’s imperative that we find ways to make sense of it all as well.
Which explains why automation – including routine tasks such as backups – has played such a key role in most enterprise IT organisations. In the early days, managing databases was labour-intensive with users often having to navigate the entire database manually to find the data they needed.
However, the advent of early database management systems – such as IBM’s Information Management System (IMS) – allowed for some basic administration that would lay the groundwork for more robust database automation.
The development of the Structured Query Language (SQL) represented the next major step towards automation by standardising the way organisations interacted and administered their database.
Together, these have led to the establishment and growth of relational database management systems such as Microsoft SQL Server, Oracle, MySQL and PostgreSQL, which in turn automated tasks such as backup, recovery, and high availability.
Database automation has helped manage ever-expanding levels of data
These are all important developments because databases have grown ever more complicated and difficult to manage as the amount of data collected and stored soars.
To address this, the development of AIOps has helped take automation to a whole new level. Thanks to the work done in recent years, AIOps now helps to detect performance anomalies, monitor for security threats, optimise performance, and improve decision-making based on data analysis.
As a result, automation has helped IT teams tackle the ever-evolving difficulties of database administration.
But innovation never stands still. One area of development gaining real traction is the use of AI in database observability, which can generate greater insights and provide a view into the ‘black box’ of databases.
This additional visibility isn’t just a ‘nice feature’ – it’s critically important. Why? According to SolarWinds’ data, one third of tech pros are managing more than 300 databases in their organisation’s environment.
For these overworked teams, AI can detect and remediate issues before they even arise, saving both time and money.
AI heralds a new phase in database automation
Despite the overwhelming benefits of automation, there are still large database estates that are managed manually. For those responsible for such systems, there is an ever-present concern that an entire business could grind to a halt under the weight of a rapidly growing database.
The implementation of database automation would go a long way to preventing such catastrophic issues from happening. And should they arise, the insights AI tools provide also means issues could be addressed more quickly.
Not only does automation save time, it also saves the organisation money. Plus, it makes IT teams more productive by freeing them up to work on more rewarding and complex tasks such as innovating and designing new products.
Just like the watermill, printing press, or the factory, database automation is changing the way teams work.
As we think about where the future of automation is heading – especially amid questions about the use of AI – we must not forget about the impact such innovation has had on human history. Automation in all its forms has helped us to become more productive and lead more fulfilling lives. The same is true for automation in IT.
