Most Artificial Intelligence (AI) applications are about having the right data at the right time, but also about being able to process them in intelligent ways. The global leaders on AI are already collecting, aggregating, processing and managing very large volume of data. In the future, a proliferating number of enterprises will have to manage very large datasets, as a means of empowering their AI-based processes. This has already a significant impact on the databases of these organizations, which must be more scalable and more intelligent than ever before. However, the relationship between AI systems and modern databases is a two-way one. On the one hand, the quality of the data management infrastructure of an enterprise is a decisive factor for its ability to adopt and fully leverage AI. On the other, AI technologies will be increasingly used to enhance the intelligence, the reliability and the quality of service offered by modern databases.
Database Modernization: The Starting Point for Running AI
Once upon a time, legacy data management systems like Relational Databases and Data Warehouses were the main pillars of the enterprise data management infrastructures. In the era of Big Data and AI, enterprises deploy and use a great variety of databases and datastores, which solve different problems and support a wide range of application requirements such as real-time performance, handling of data with high ingestion and cost-effective scalability for very large datasets. Nowadays, corporate data management infrastructures comprise relational databases and data warehouses, along with Big Data datastores like noSQL databases and data lakes. The latter compromise the traditional ACID (Atomicity Consistency Integrity Durability) properties of relational database systems, in order to support the handling of arbitrarily large datasets. This modernization of the corporate data management infrastructure is driving the development and deployment of AI applications like machine learning, bots, text analytics and natural language processing.
Machine Learning for Data-Driven Scalability and Automation
Databases are not only about storing data in a consistent and reliable way. Rather, their value steps from their data management functionalities. Machine learning and other AI techniques provide the means for enhancing these functionalities towards increased scalability and intelligence in managing very large datasets. As a prominent example, machine learning algorithms are currently used for identifying and applying optimal backup and recovery policies. Likewise, AI techniques are used for identifying the optimal sequence of data operations in queries, in order to automate workflows accordingly. In this direction, several vendors of data management and data storage products have partnered with BigData and data mining product vendors.
Machine learning provides also the means for implementing added-value functionalities over data management products. For instance, several data management products come with build-in pattern identification and knowledge extraction capabilities. Some database and data management vendors leverage machine learning in offering data discovery and data quality auditing tools. Overall, for many companies the application of machine learning at their data management layer is the first step to applying it at the application layer of enterprise projects.
Artificial Intelligence Databases
The undeniable impact of machine learning and artificial intelligence on data management systems has given rise to a new type of database, namely AI databases. AI databases integrate AI technologies to provide value-added features that differentiate them from traditional databases. As a characteristic example, AI databases provide full-text search and text-analytics capabilities, which differentiate them from traditional relational databases that are typically queried based on keywords, phrases and their combinations using Boolean operators. As another example, AI databases provide the means for accelerating the usually expensive training of machine learning models based on proper engineering of their data management capabilities. This is very important for machine learning applications, given that it might take time and many training cycles prior to finding a proper machine learning model.
There are also BigData databases that facilitate the integration of the different datastores of the enterprise data management infrastructure, as a means of boosting the development of AI algorithms over large datasets. In some cases, AI databases can be run on specialized hardware chips that accelerate the processing of heavy loads at the data layer of AI applications. Specialized hardware helps database vendors deal with the data processing and data governance challenges of the AI era.
Some AI databases are also providing NLP (Natural Language Processing) based query interfaces. NLP querying functionalities enable their users to interact with them in more natural, effective and friendly ways. Hence, such AI databases offer much more expressive and intelligent interfaces when compared to traditional SQL-based interfaces. Moreover, some other AI databases offer machine learning based cybersecurity features such as automated, data-driven detection of security intrusions and assessment of risks. Likewise, they come with multimedia processing algorithms, such as algorithms for analyzing images. They also offer automation features such as elimination of repetitive tasks and automation of development (e.g., querying) and administration (e.g., backup, recovery) tasks.
The Transition to DataOps
AI databases are supporting organizations in realizing a transition to the DataOps methodology. DataOps includes a set of practices for automating the development and testing of data analytics pipelines, while at the same time facilitating communication across data science teams. DataOps is aiming at increasing the agility and efficiency of data science teams. Furthermore, DataOps provides a framework for effective interactions between data scientists, developers, architecture and databases administrators. This framework can be blended with DevOps in order to improve the quality and reduce the cycle time of data analytics operations, which are very common in modern IT projects.
Overall, AI is having a disruptive impact on the database market. It drives the implementation of novel, value-added features over conventional databases such as the use of machine learning for optimizing storage and archiving processes. Furthermore, it drives organizations’ investments in the modernization of their data management infrastructures, including investments on data warehouses, noSQL databases, and data lakes. However, the evolution of databases has a significant impact on AI systems as well. It feeds such systems with large amounts of data and accelerates the training of machine learning models. Enterprises with access to the most scalable and intelligent data infrastructures will have a competitive advantage over their competitors in the AI market. In this context, companies had better monitor the evolution of the AI and of the data management markets in order to keep up with the latest trends and to make educated decisions.
