F5-TTS Model Installation on Windows - Easy Step by Step Tutorial

Tired of duplicating massive datasets just to add vector search capabilities? With Oracle AI Database 26ai, you can now run high-performance similarity search directly on your existing Apache Iceberg tables stored in object storage — no data copying, no extra ETL pipelines, and no governance headaches.

This feature is a game-changer for data lakes built on Iceberg, Parquet, and cloud storage (OCI Object Storage, S3, etc.).

Why This Matters

• Avoid massive data duplication and sync issues
• Keep data in its original governed location
• Query Iceberg + Oracle tables together in the same SQL
• Create fast vector indexes without moving the source data
• Works great for RAG, semantic search, and recommendation systems

Step-by-Step: Query Iceberg Vectors in Minutes

1. Create External Table over Iceberg

CREATE TABLE ext_iceberg_vectors (
    id           VARCHAR2(100),
    content      CLOB,
    embedding    VECTOR(1024, FLOAT32)   -- match your embedding dimension
)
ORGANIZATION EXTERNAL
(
    TYPE ORACLE_BIGDATA
    DEFAULT DIRECTORY DATA_PUMP_DIR
    ACCESS PARAMETERS
    (
        com.oracle.bigdata.credential.name = 'OCI_CRED',
        com.oracle.bigdata.fileformat = 'parquet',
        com.oracle.bigdata.access_protocol = 'iceberg'
    )
    LOCATION ('iceberg:https://objectstorage.<region>.oraclecloud.com/.../metadata/v1.metadata.json')
)
REJECT LIMIT UNLIMITED;

2. Run Similarity Search (with on-the-fly embedding)

SELECT id,
       content,
       VECTOR_DISTANCE(embedding, 
                       VECTOR_EMBEDDING(embedding_model USING :search_query AS data)) AS score
FROM   ext_iceberg_vectors
ORDER  BY score
FETCH FIRST 10 ROWS ONLY;

3. Speed It Up with Vector Index

CREATE VECTOR INDEX iceberg_vec_idx 
ON ext_iceberg_vectors(embedding)
ORGANIZATION NEIGHBOR PARTITIONS
WITH TARGET ACCURACY 95;

Best Practices for Production

• Use credential objects for secure access to object storage
• Match vector dimension and type exactly with your embedding model
• Create IVF or HNSW indexes for large Iceberg tables
• Combine with Oracle tables in the same query for hybrid search
• Great for air-gapped environments (embeddings run in-database via ONNX)

Real-World Use Cases

• Semantic search over data lake documents
• RAG applications using Iceberg as the knowledge base
• Real-time recommendations without data movement
• Unified analytics across structured + unstructured data

Conclusion

Oracle AI Database 26ai + Apache Iceberg gives you the best of both worlds: the governance and scale of a modern data lake with the powerful, familiar vector search capabilities of Oracle.

No more unnecessary data copies. Just point, index, and query — delivering fast semantic search on your existing Iceberg tables today.