How Machine Learning Increases the Strategic Value of Database Administration

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The DBAs are now increasingly seeking Artificial Intelligence and Machine Learning capabilities into database management for database optimization. These solutions from both the database vendors and third-party service providers allow the database admins to spend less time searching for bottlenecks and more time to focus on their core creative and productive work and meet the strategic business goals.

The Role of Machine Learning and AI in DBA

As we mentioned above, machine learning and AI had entered into the mainstream of database administration over the last couple of years. Here, we will cover up some brief descriptions to understand this landscape before exploring how this technology benefits database administrators.

  • Machine learning as a subset of artificial intelligence uses some statistical techniques to allow computers to predict the outcomes using data models and datasets. Examples of machine learning in database administration include fraud detection, email filtering, a ranking system for driving online marketing, and so on.
  • In AI, a machine imitates a certain level of human cognitive functions like problem-solving and learning. Some examples of significant machine learning capabilities include automated trading systems, autonomous driverless cars, intelligent routing, and delivery systems, etc.
  • On the other hand, deep learning is a subset of machine learning, which uses an artificial neural network approach to ML and task-based algorithms. The classic use cases include Natural Language Processing, computer vision, and speech recognition, etc.

 

How Research Contributed to the Autonomous DBMS Systems?

Let us further explore a real-time example. As you tend to use database systems and anticipate some operational issues, you need to take some actions to prevent these by effectively distributing some extra resources, dropping or adding indexes, automatically adjusting the execution plan for queries, etc. It is the actual concept behind machine learning and autonomous databases, which will predict when such a problem will occur and whether the DBAs need to act automatically. Over the last decade, academic researchers have also explored ML to fine-tune the DBMS systems automatically. You may take advice from expert providers like RemoteDBA.com to decide your appropriate DBMS to use. Some classic examples of this utility research are as below.

OtterTUNE from Carnegie Mellon Database Research Group leveraged the data from their previous database workloads to fine-tune the new DBs using machine learning strategies. This uses data for building new ML models which capture how database systems respond to various configurations. Finally, OtterTUNE used specific models to experiment for their recommended settings and new applications, which improved the target objective like improving throughput and cutting short latency.

DBSeer, yet another open-source framework built by MIT, now uses machine learning and regression techniques to identify any bottleneck resources. With this, it can effectively predict the performance of a given set of utilities. As a result, many professionals use and take advantage of the facilities available for their requirements.

 

AI Capabilities Integrated into the Database Products

Database management systems handling a huge volume of data for operating complex workloads are challenging to manage as they have thousands of configuration settings needing skilled database professionals to administer them. As a result, the companies like Oracle and Microsoft have started to use artificial intelligence and machine learning for their DBs to enable autopilot monitoring. It helped the DBAs proactively address the problems caused by many conventional databases. Some of the examples are as below:

  • Microsoft Azure SQL DB Advisor offers recommendations for customized performance tuning for the DBAs and effectively gives inputs for performance improvement to be applied automatically.
  • MS SQL Server Query Store can monitor the query usage to provide a workable plan to identify all the queries and execution plans.
  • Oracle 18C Autonomous Database can automatically upgrade patches and tune effectively by understanding what looks normal and help eliminate any complexity in manual management and human errors.

 

Machine Learning Features are Used to Benefit Businesses

As we can see, R and Python have become very popular in developing ML applications. The database vendors now largely integrate these to offer statistics from our data analysis for graphic representation of data. As a result of DBMS, technology has become a catalyst to implement machine learning solutions to help businesses make better decisions. Some examples areas:

  • MS SQL Machine Learning Server is an example of a standalone service, which can run independently off SQL server DB Engine instances. This can also offer a developmental environment with parallel and distributed Python and R workloads over small-size and tolerable datasets by using proprietary packages and engines for calculation within the server.
  • MS SQL Server Machine Learning is also a classic example of database installation for ML. It can easily operate inside the SQL Server DB Engine instance and offer external support for R and Python scripts to handle resident data on various SQL server instances. These will also keep the analytics close to the data and help eliminate any security risks related to data movements.
  • Oracle R Enterprise is also an effective component of Oracle DB Enterprise Edition, making R language and developer environment ready for data analytics. It can also let the users run the commands and machine learning scripts, and graphical analysis on data stored in Oracle DB.

As all these integrations offer various benefits, certain essential DBA functions like performance fine-tuning require human supervision to ensure that these are properly secured. It is also essential to make sure that no machine learning strips compromise the performance of the given DB. Finally, database admins should also take a critical approach in coordinating with the changing needs of data scientists, data architects, enterprise application developers, and data engineers through the entire data lifecycle.

The explosion of the Internet of Things also causes massive streaming of unstructured data in real-time, which will challenge future database administrators to manage big data processing. ML can also come into the picture to protect data privacy and sovereignty alongside extracting actionable insights and values from the data. DBAs also should feel highly empowered by emerging AI and ML applications. In this changing scenario, no other individual understands gathering and managing data better than a skilled database administrator.

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