Databases have evolved far beyond simple data storage systems. Modern databases are expected to support AI workloads, analytics, application development, and secure data management—all within a single platform.
Oracle Database 26ai reflects this evolution. It introduces several features designed to make databases more intelligent, scalable, and developer-friendly.
In this post, I’ll highlight five interesting capabilities of Oracle 26ai that caught my attention while exploring recent demos and documentation.
1️⃣ Hybrid Vector Index – Enabling AI-Driven Search
One of the most exciting additions in modern databases is vector search.
Vector search allows databases to store and query embeddings generated by machine learning models. Instead of matching exact words, the system can find results based on semantic similarity.
This capability is essential for building:
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AI assistants
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semantic search systems
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recommendation engines
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Retrieval Augmented Generation (RAG) applications
Oracle 26ai introduces Hybrid Vector Index, which combines vector similarity search with traditional SQL filtering.
Example
Suppose we store product descriptions and their embeddings.
CREATE TABLE products ( id NUMBER, description VARCHAR2(500), embedding VECTOR(768) );
To search for products similar to a query embedding:
SELECT id, description FROM products ORDER BY VECTOR_DISTANCE(embedding, :query_vector) FETCH FIRST 5 ROWS ONLY;
The hybrid vector index improves performance by combining vector search with structured filtering.
Example:
SELECT id, description FROM products WHERE category = 'electronics' ORDER BY VECTOR_DISTANCE(embedding, :query_vector) FETCH FIRST 5 ROWS ONLY;
This allows AI search and structured queries to work together efficiently.
2️⃣ JSON Relational Duality – Bridging Documents and Tables
Applications often use JSON-based APIs, while databases traditionally store data in relational tables.
Oracle introduced JSON Relational Duality, allowing data to be viewed both as:
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relational tables
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JSON documents
without duplicating the data.
This makes it easier to build modern applications while keeping relational integrity.
Example
Create a table storing customer data.
CREATE TABLE customers ( id NUMBER PRIMARY KEY, name VARCHAR2(100), city VARCHAR2(100) );
The same data can be exposed as a JSON document.
SELECT JSON_OBJECT( 'id' VALUE id, 'name' VALUE name, 'city' VALUE city ) FROM customers;
This flexibility allows developers to interact with the database in the format most suitable for their application.
3️⃣ Secure Data Redaction – Protecting Sensitive Information
Security and privacy are critical for modern data systems, especially when dealing with personal or financial information.
Oracle provides data redaction capabilities to prevent sensitive data from being exposed to unauthorized users.
Instead of returning real values, the database can automatically mask or redact sensitive fields.
Example
Suppose we want to hide credit card numbers from certain users.
BEGIN DBMS_REDACT.ADD_POLICY( object_schema => 'HR', object_name => 'PAYMENTS', column_name => 'CARD_NUMBER', policy_name => 'REDACT_CARD', function_type => DBMS_REDACT.FULL ); END;
When queried, the column might appear as:
XXXX-XXXX-XXXX-1234
This feature helps enforce data protection policies directly inside the database.
4️⃣ Domain Types – Improving Data Consistency
Another interesting feature is Domain Types, which allow developers to define reusable data definitions with constraints.
This helps maintain data consistency across multiple tables.
Example
Define a domain for email addresses.
CREATE DOMAIN email_domain AS VARCHAR2(255) CHECK (REGEXP_LIKE(VALUE, '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$'));
Now the domain can be reused across tables.
CREATE TABLE users ( id NUMBER, email email_domain );
This ensures every table using this domain follows the same validation rules.
5️⃣ Built-in GraphQL Support
Modern applications often use GraphQL APIs instead of traditional REST APIs.
Oracle Database now supports GraphQL queries directly within the database layer, allowing developers to expose data through GraphQL without building separate middleware.
This reduces complexity in application architectures.
Example GraphQL query:
{ customers { id name city } }
The database can resolve this query and return structured data directly.
This capability helps simplify data access for modern web and mobile applications.
Why These Features Matter
Looking at these features together reveals an interesting trend.
Modern databases are evolving to support multiple workloads:
| Capability | Purpose |
|---|---|
| Vector Search | AI and semantic search |
| JSON Duality | Modern API-friendly data access |
| Data Redaction | Security and compliance |
| Domain Types | Data quality and consistency |
| GraphQL | Simplified application integration |
Instead of relying on multiple separate systems, many of these capabilities are now built directly into the database engine.
Final Thoughts
Oracle Database 26ai reflects a broader shift in data platforms.
Databases are no longer just repositories for structured tables—they are becoming intelligent data platforms capable of supporting AI, analytics, and modern application architectures.
Features like vector search, JSON duality, and built-in security controls show how database systems are adapting to the needs of modern applications.
For developers and data professionals, understanding these capabilities can open up new possibilities for building AI-powered and data-driven systems.
Many of these capabilities were introduced in Oracle 23ai and continue to evolve in Oracle 26ai as part of Oracle’s AI-focused database platform.
