Name | NoSQL - Column Store | RDBMS - Columnar |
Description | Wide-column store based on Apache Hadoop and on concepts of BigTable | Columnar RDBMS optimized for Big Data analytics |
Database model | Wide column store | Relational DBMS |
Data scheme | schema less | yes |
Typing | no | yes |
Secondary indexes | no | yes |
SQL | no | yes |
Server-side scripts | yes | yes |
Triggers | yes | yes |
Partitioning methods | Sharding | shared disk or shared nothing architectures with SAP IQ Multiplexer |
MapReduce | yes | no |
Consistency concepts | Immediate Consistency | Immediate Consistency |
Foreign keys | no | yes |
Transaction concepts | no | ACID |
Concurrency | yes | yes |
Durability | yes | yes |
User concepts | Access Control Lists (ACL) | fine grained access rights according to SQL-standard |
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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