How to use ar­ti­fi­cial in­tel­li­gence for databases

AI databases are database-supported systems that integrate ar­ti­fi­cial in­tel­li­gence in order to process data more ef­fi­ciently. Databases designed for machine learning are not only powerful tools for data-driven decisions in companies, but also offer a wide range of ap­plic­a­tions in the field of data analysis.

What are AI databases?

AI databases are advanced database systems that integrate ar­ti­fi­cial in­tel­li­gence tech­no­lo­gies to ef­fi­ciently handle complex data pro­cessing and analysis tasks. While con­ven­tion­al databases are mainly used for data storage and simple queries, AI databases are designed to recognise patterns in large amounts of data. This enables them to make concrete pre­dic­tions and support data-based decisions. AI-powered databases are often optimised for ap­plic­a­tions that use machine learning, which allows seamless in­teg­ra­tion of the al­gorithms re­spons­ible for pattern re­cog­ni­tion, data clas­si­fic­a­tion and pre­dic­tion.

A char­ac­ter­ist­ic feature of ar­ti­fi­cial in­tel­li­gence databases is that they learn from the data and analyses they collect. This is par­tic­u­larly useful for ap­plic­a­tions where re­quire­ments are con­stantly changing, such as per­son­al­ised customer targeting and dynamic market analysis. Spe­cial­ised AI models such as neural networks are often used to increase the precision of data pro­cessing.

What dis­tin­guishes an AI database from a con­ven­tion­al database?

AI databases offer functions that are specially tailored to data-intensive and AI-driven ap­plic­a­tions. Con­ven­tion­al database systems are usually used for less complex tasks. Therefore, ar­ti­fi­cial in­tel­li­gence databases differ sig­ni­fic­antly from tra­di­tion­al databases in some respects. The following overview sum­mar­ises the most important dif­fer­ences:

• Designed for a variety of data types: Con­ven­tion­al database systems primarily process struc­tured data such as tables with clearly defined fields. AI databases, on the other hand, are optimised for a wide variety of data types. Cor­res­pond­ing systems can also ef­fi­ciently manage un­struc­tured data such as images, text and audio files. This enables them to meet the dynamic re­quire­ments that are common in the field of AI ap­plic­a­tions.
• Real-time data pro­cessing: To train models and make pre­dic­tions, AI ap­plic­a­tions often require fast, real-time data pro­cessing. Dis­trib­uted ar­chi­tec­tures, modern indexing mech­an­isms and in-memory pro­cessing (which enables entire databases to be stored in RAM) are often used to query and calculate data.
• Scalab­il­ity: AI-supported databases can scale ho­ri­zont­ally or add ad­di­tion­al nodes to handle large amounts of data and com­pu­ta­tion­ally intensive tasks. Ho­ri­zont­al scaling makes it possible to flexibly adapt both the storage capacity and the computing power.
• In­teg­ra­tion of ML frame­works possible: In many cases, ar­ti­fi­cial in­tel­li­gence databases provide direct in­ter­faces for machine learning frame­works. This seamless in­teg­ra­tion sim­pli­fies the machine learning pro­cessing pipeline as no ad­di­tion­al data migration to a separate analytics en­vir­on­ment is required.

What are the best AI databases?

Which database proves to be optimal depends heavily on the in­di­vidu­al re­quire­ments and the specific area of ap­plic­a­tion. The various ar­ti­fi­cial in­tel­li­gence databases sometimes differ con­sid­er­ably in terms of their focus. The best systems are char­ac­ter­ised by high flex­ib­il­ity, scalab­il­ity and per­form­ance. Here are some of the leading AI databases:

• MySQL: The database MySQL, developed and operated by Oracle since 1995, offers first-class scalab­il­ity for large data volumes and comes with other practical features, such as a flexible community license.
• H2O.ai: The open source platform H2O.ai offers spe­cial­ised tools for databases with machine learning and AI. It supports dis­trib­uted ar­chi­tec­tures that enable fast data pro­cessing and extensive models. Thanks to its in­teg­ra­tion with frame­works such as Tensor­Flow and Spark, H2O.ai is par­tic­u­larly suitable for companies that want to build complex pre­dict­ive models and data analyses.
• Redis: The in-memory database Redis supports numerous data struc­tures and can also be used as a message broker or cache.
• Tensor­Flow Extended (TFX): Tensor­Flow Extended (TFX) is an end-to-end platform for the creation and operation of machine learning pipelines. It enables training data man­age­ment, model de­ploy­ment and con­tinu­ous eval­u­ation. The software is an excellent choice for de­vel­op­ing and scaling AI-powered ap­plic­a­tions that require data-intensive analysis.
• Post­gr­eSQL: The database system Post­gr­eSQL extends the SQL language to process and scale complex data workloads.
• MongoDB: When it comes to document data and NoSQL databases, MongoDB is clearly one of the leading and most popular systems.
• MariaDB: The database man­age­ment system MariaDB was created as a spin-off from MySQL. Discover how it compares directly to MySQL in this direct com­par­is­on: MariaDB vs MySQL.
• Weaviate: Weaviate is a modern graph database with AI functions that was specially developed for semantic searches and knowledge man­age­ment. It combines classic database functions with AI models to ef­fi­ciently process un­struc­tured data such as text, images and vectors.

How to find the right AI database?

In order to find the optimal ar­ti­fi­cial in­tel­li­gence database, it’s necessary to take several variables into account. In addition to the specific re­quire­ments, these include above all the fore­cas­ted data growth and the type of analyses to be carried out. The following tips will support you in your decision-making process:

• Record AI workload: Analyse how high the expected data volume will be and at what rate data is likely to be received. It’s also important whether you are only carrying out simple queries or creating extensive machine learning models.
• Important search features: Per­form­ance, speed and scalab­il­ity are of crucial im­port­ance. When working with AI ap­plic­a­tions, it’s often also ad­vant­age­ous if the database supports different data types and can process several op­er­a­tions sim­ul­tan­eously (par­al­lel­ism).
• Evaluate database types: NoSQL databases are popular because they’re suitable for managing large amounts of un­struc­tured data. NewSQL databases combine the re­li­ab­il­ity of ordinary SQL databases with the scalab­il­ity of NoSQL databases. A graph database is ideal for complex data re­la­tion­ships. To analyse data over time, a time series database may be required. GPU-ac­cel­er­ated databases with high-speed pro­cessing functions are a good choice for deep learning.
• Costs and internal company con­sid­er­a­tions: Above all, look at the total cost of ownership. In addition to the ac­quis­i­tion costs, these also include expenses incurred in the long term, such as for scaling, support and main­ten­ance measures. If you’re working with sensitive data, it’s also necessary for the AI database to comply with current data pro­tec­tion and security reg­u­la­tions.
• Carry out a feas­ib­il­ity check: Before selecting a database system, it’s wise to run a proof of concept to verify its suit­ab­il­ity for your specific use case. Ad­di­tion­ally, consider the com­plex­ity of im­ple­ment­a­tion, as it often demands sig­ni­fic­ant expertise and effort.