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 recognize patterns in large amounts of data. This enables them to make concrete predictions and support data-based decisions. AI-powered databases are often optimized 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 personalized customer targeting and dynamic market analysis. Specialized 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 summarizes 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 optimized 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 characterized 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 specialized 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: Analyze 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 analyze 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.