Choosing the Right Database in the Age of AI
Explore PostgreSQL’s role in AI, compare relational and NoSQL databases, learn how pgvector helps enhance AI workloads, and more
Learn the importance of choosing the right database for AI data management
AI is changing how the world captures, processes, stores, analyzes, and uses data. In the future, AI will be incorporated into the design, management, and operation of most organizations’ data workloads, creating a more efficient and intelligent business landscape.
Since AI depends on data, how well this data is organized and managed directly affects the quality of AI output. Businesses must harness database management systems that accelerate and enable accurate data retrieval for optimal AI-driven operations.
With its scalability and robust extensibility, PostgreSQL is the perfect foundation for AI data management.
Know the key traits of relational databases, NoSQL databases, and specialized vector databases
Before starting an AI project, it’s vital to know the differences among database models to determine which one best suits your AI workload needs.
Relational databases, non-relational — or NoSQL — databases, and specialized vector databases can all handle the high volumes of data used for AI, but each has its distinct strengths.
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Relational databases
PostgreSQL has advanced querying capabilities that allow for intricate analyses of complex relationships among data entities. It is also highly flexible, boasting a vast array of extensions — like pgvector, for example, which adds support for the high-dimensional vector data that’s essential for many modern AI applications.
Relational databases such as PostgreSQL are all about data integrity and reliability. Entering and retrieving information calls for a specific format, or schema, that requires a clear definition of data types and relationships before data entry. This makes them ACID-compliant, offering atomicity, consistency, isolation, and durability to guarantee data validity. - NoSQL databases
In contrast, NoSQL databases allow you to input data based on your chosen formats — documents, key-value pairs, and graphs, for instance — instead of following a strict schema. This can work well for AI applications that need to leverage unstructured and semi-structured data. NoSQL databases are also designed for horizontal scalability, where data is spread across multiple servers, which can help make the high volumes of data required for AI more feasible. - Specialized vector databases
Specialized vector databases such as Pinecone, Milvus, and Weaviate manage and manipulate high-dimensional vector data. They serve as efficient ways to handle vast amounts of complex, unstructured data required by increasingly sophisticated AI models — but they cause issues with vendor lock-in and require higher technical expertise for management and maintenance.
When choosing a database model, consider both your needs and the issues you want to avoid. Relational databases are better suited for AI applications needing complex queries and structured data relationships but have scalability limitations. NoSQL databases offer flexibility and scalability but can have issues with data consistency. Specialized vector databases excel in specific, sophisticated processing needs but can complicate data management and incur higher costs.
Despite the typical tradeoffs with relational databases for AI, PostgreSQL’s extensibility — which essentially turns it into a multi-model database — positions it well as a best-of-all worlds option.
Learn how pgvector allows PostgreSQL to handle vector-based data efficiently
pgvector enables efficient storage and similarity searches of high-dimensional vector data, which are commonly used for machine learning models, recommendation systems, and natural language processing applications.
Vectors represent entities like text or images as points in high-dimensional space. This allows for the calculation of distances between points and the determination of similarity in unrelated data. pgvector allows PostgreSQL to perform efficient nearest-neighbor search operations to identify similar vectors.
Organizations can leverage pgvector for all kinds of AI applications, using the extension to build intelligent search, ranking, and recommendations — all within the PostgreSQL environment. Examples of specific use cases are:
- Anomaly detection and fraud prevention in the financial services industry
- Recommendation engines for e-commerce platforms
- Natural language customer service chatbots and AI copilots
Discover PostgreSQL’s many advantages for robust AI data management
In addition to the vector capabilities of the pgvector extension, there are many other open source extensions to support the broad variety of data that come together in AI applications — like the TimescaleDB extension, for example, that makes SQL scalable for time-series data.
PostgreSQL also has features like Common Table Expressions (CTEs) and window functions that let analysts and developers craft complex queries to efficiently process large volumes of data, deriving actionable insights in real time. Tools like EvaDB can connect to your database and perform SQL queries on pre-trained models from Hugging Face, OpenAI, YOLO, and PyTorch.
Many AI data projects harness PostgreSQL’s versatility. Its PostgresML extension is used in natural language processing applications, supporting text classification, translation, and the development of chatbots and other conversational agents. It can manage complex image data such as biometrics for financial applications and medical imaging for healthcare programs. With the TensorFlow, pgvector, and Next.js extensions, it can facilitate personalized content suggestions for recommendation systems by calculating embeddings for user preferences. Projects like Ottertune and DBTune also harness PostgreSQL to optimize database performance through AI-driven tuning engines.
Leverage AI with a robust and versatile relational database
All in all, the success of an AI application relies on the database that powers it. Which database you choose directly impacts how the application functions, from data retrieval speeds to analytical capabilities.
Relational database PostgreSQL offers critical advantages. Its open source, extensible nature – with a wealth of tools like pgvector – opens it up to the many rapid developments in AI, and it can quickly and reliably process large volumes of data to offer accurate, detailed insights for a broad range of AI applications.
With PostgreSQL, you can harness AI to improve operations, grow your company, and lead the charge in creating a smarter and more dynamic business environment.
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Find out how PostgreSQL’s design tenets from 30 years ago make this database ideal for the future of AI
PostgreSQL is highly extensible to meet the complex demands of AI workloads. It allows for new and custom data types with various AI-related extensions like pgvector, TimescaleDB, and PostgresML, and is compatible with machine learning frameworks like TensorFlow and PyTorch. It also has robust security features such as Fine-Grained Access Control, Transparent Data Encryption, Role-Based Access Control, and audit logging.
- Because relational databases enforce ACID properties (Atomicity, Consistency, Isolation, Durability), data remains accurate and consistent — a key requirement for AI model training.
- PostgreSQL can handle advanced querying capabilities like complex SQL queries, joins, and window functions, allowing for sophisticated AI data analysis.
- It’s easier for AI to derive insights from complex datasets when these are precisely structured — something PostgreSQL excels in, as opposed toNoSQL.
Specialized vector databases lack scalability and performance for large-scale use and come with steep learning curves. They can create data silos, vendor lock-in, and security concerns.
Postgres offers robust security features like data encryption, access control, and auditing capabilities. Its built-in tools help organizations protect sensitive data efficiently while ensuring compliance with data protection regulations.
pgvector is an extension for Postgres that enables efficient storage and similarity search of high-dimensional vector data, commonly used for machine learning models, recommendation systems, and natural language processing applications.
pgvector extends Postgres to support vector data types. Vectors represent entities like text or images as points in high-dimensional space. This allows for the calculation of distances between points and the determination of similarity in seemingly unrelated data.
pgvector on EDB Postgres AI unifies AI, transactional, and analytical workloads in a single, secure platform, delivering 4.22X faster queries, 5X smaller disk footprint, and 18X storage cost efficiency vs. basic Postgres while leveraging enterprise-grade features for seamless integration, rapid development, and future-proof scalability.
The single-database approach simplifies development, accelerates time-to-market, and reduces infrastructure costs. Customers benefit from a mature, enterprise-grade platform that evolves with their AI needs, avoiding the complexities and limitations of multi-database solutions.
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