Lessons from storage engineering for Knowledge bases and RAGs.

Data at rest and in transit are chunks of binaries that make sense only when there are additional layers built to process them, store them, ETL them or provide them as results to queries and storage engineering has a rich tradition in building datastores, as databases and data warehouses and even making them virtual and hosted in the cloud. Vector databases, albeit the authority in embeddings and semantic similarity, do not operate independently but must be part of a system that serves as a data platform and often spans multiple and hybrid data sources for best results. The old and the new worlds can enter a virtuous feedback loop that can improve the use of new datastores.

Take Facebook Presto, for example, as a success story in bridging structured and unstructured social engineering data. Developed as an open-source distributed SQL query engine, it revolutionized data analytics by enabling seamless querying across structured and unstructured data sources. Presto's ability to perform federated queries allowed users to join and analyze data from diverse sources, such as Hadoop Distributed File System (HDFS), Apache Cassandra, and relational databases, in real-time. This unified approach eliminated the need for multiple specialized tools, bridging the gap between structured and unstructured data. Presto's architecture, optimized for low query latency, employed in-memory processing and pipelined execution, significantly reducing end-to-end latency compared to traditional systems like Hive3. Its scalability and flexibility made it a valuable tool for handling petabyte-scale datasets.

Drawing parallels to technologies that work with structured and vector data, vector databases emerge as a compelling counterpart. These databases are designed to store and retrieve high-dimensional vectors, which are mathematical representations of objects. By mapping structured data into vector space, vector databases facilitate similarity searches and enable AI algorithms to retrieve relevant information efficiently. For example, Milvus, a popular vector database, supports vectorizing structured data and querying it for advanced analytics. This process involves converting structured data into numerical vectors using machine learning models, allowing for nuanced analysis and pattern detection.

Both Presto and vector databases share a common goal: unifying disparate data types for seamless analysis. Some examples of vector databases include:

 

• Milvus: Milvus is an open-source vector database designed for managing large-scale vector data. It supports hybrid searches, combining structured metadata with vector similarity queries, making it ideal for applications like recommendation systems and AI-driven analytics.

• Weaviate: Weaviate is another open-source vector database that integrates structured data with vector embeddings. It offers semantic search capabilities and allows users to query data using natural language prompts.

• Redis (Redis-Search and Redis-VSS): Redis has extensions for vector search that enable hybrid queries, combining structured data with vector-based similarity searches. It's optimized for high-speed lookups and real-time applications.

• Qdrant: Qdrant is a vector database that supports hybrid queries, allowing structured filters alongside vector searches. It is designed for scalable and efficient AI applications.

Azure Cosmos DB stands out as a versatile database service that integrates vector search capabilities alongside its traditional NoSQL and relational database functionalities. When compared with the above list, here’s how it stands out:

• Hybrid Data Support: Like Milvus, Weaviate, Redis, and Qdrant, Azure Cosmos DB supports hybrid queries, combining structured data with vector embeddings. This makes it suitable for applications requiring both traditional database operations and vector-based similarity searches.

• Integrated Vector Store: Azure Cosmos DB allows vectors to be stored directly within documents alongside schema-free data. This colocation simplifies data management and enhances the efficiency of vector-based operations, a feature that aligns with the capabilities of vector databases.

• Scalability and Performance: Azure Cosmos DB offers automatic scalability and single-digit millisecond response times, ensuring high performance at any scale. This is comparable to the optimized performance of vector databases like Redis and Milvus.

• Vector Indexing: Azure Cosmos DB supports advanced vector indexing methods, such as DiskANN-based quantization, enabling efficient and accurate vector searches. This is like the indexing techniques used in specialized vector databases.

• AI Integration: Azure Cosmos DB is designed to support AI-driven applications, including natural language processing, recommendation systems, and multi-modal searches. This aligns with the use cases of vector databases like Weaviate and Qdrant.

While Azure Cosmos DB provides robust vector search capabilities, it also offers the flexibility of a general-purpose database, making it a compelling choice for organizations looking to unify structured, unstructured, and vector data within a single platform.

#codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNYlIwEAAAABD8nSsN--hM7kfA-W_mzuWw?e=BQczmz