Vector databases are drastically changing how you interact with data, enabling powerful applications in search, recommendation, and image or video analysis. The demand for these capabilities is constantly increasing. While third-party vector database providers offer compelling solutions, AWS also gives tough competition.
AWS provides a robust cloud infrastructure and a growing suite of AI services to address your ever-evolving requirements. This article will discuss the need for vector databases and explore various services you can use on AWS to build and deploy vector database solutions.
What Is AWS?
Amazon Web Services (AWS) is a cloud computing platform that offers over 200 services to help you scale your organization securely. These services include computing, storage, databases, analytics, networking, mobile, deployment, management, IoT, security, and enterprise applications.
With a massive global network of data centers, AWS operates from 105 availability zones within 33 geographical locations, ensuring low latency and high availability. It provides flexibility, scalability, and a cost-effective pay-as-you-go pricing model, empowering you to innovate faster, reduce IT costs, and improve agility.
Why Is a Vector Database Needed?
Conventional databases effortlessly manage structured data but struggle with unstructured data such as images, text, audio, and videos. This limitation makes it difficult for you to run applications requiring similarity search, recommendation systems, and advanced analytics.
Vector databases address this challenge by storing and indexing data as numerical representations (vectors). This approach allows you to find items similar to a given query based on their underlying characteristics.
Unlike conventional databases that rely on exact matches, vector databases enable approximate nearest neighbor (ANN) searches, allowing for more flexible and relevant data retrieval. This capability is essential for modern applications that require real-time, scalable, and efficient management of complex data types.
Vector databases are crucial for applications in various domains, including:
- E-commerce: You can use vector databases to provide product recommendations, perform image searches, and enable personalized shopping experiences.
- Bioinformatics: Vector databases can help you facilitate biotech applications such as drug discovery, protein structure analysis, and genomic data analysis.
- Finance: You can leverage computer vision and natural language processing (NLP) for face and biometric identification, allowing authorized access to prevent fraud.
- Semantic Search: With vector representations, you can seamlessly implement image or video search and run question-answering systems.
Which Service Can Be Used on AWS for Vector Databases?
AWS offers multiple services to build and deploy vector database solutions for different use cases and performance requirements. Here are some key options:
Amazon OpenSearch Service
Amazon OpenSearch Service is a fully managed Elasticsearch service that supports vector search through plugins and integrations. It allows high-performing similarity searches on large datasets using the k-Nearest Neighbor (k-NN) search. OpenSearch Service enables you to store, update, and query billions of vector embeddings, making it ideal for AI-powered applications that require fast and accurate results.
Amazon MemoryDB for Redis
You can utilize Amazon MemoryDB for Redis for applications demanding low latency and high throughput, such as real-time chatbots and fraud detection. It supports millions of vectors with single-digit millisecond response times and has in-memory data storage for rapid query performance. MemoryDB offers high availability and durability through a multi-AZ architecture, ensuring data integrity and resilience. It eliminates the need for database administration and allows you to focus on application development.
Amazon Aurora PostgreSQL
Amazon Aurora PostgreSQL is a relational database service compatible with PostgreSQL and supports vector search through the pgvector extension. It allows you to store and index vector embeddings within a relational database and leverages PostgreSQL's mature ecosystem for workloads with complex query patterns and data relationships. Amazon Aurora PostgreSQL is highly flexible, customizable, and ideal for hybrid search capabilities.
Amazon DocumentDB
This vector db AWS solution combines flexible document-based storage with powerful vector search capabilities. It efficiently stores and indexes vectors alongside JSON documents, enabling applications to handle structured and unstructured data. Amazon DocumentDB offers horizontal scaling, high availability, and robust performance while accommodating diverse vector data types.
Choosing the right service depends on several factors, such as data volume, query patterns, performance requirements, and integration needs. By considering these factors, you can select the most suitable AWS service for your organization’s vector database needs.
Build Data Pipelines for Vector Database Solutions with Airbyte
Data integration is a vital step in building vector databases. It ensures that your data is in a suitable format and readily accessible for vectorization and advanced analysis. Airbyte, an AI-powered data integration tool, can help you with this.
Airbyte streamlines extracting, transforming, and loading structured and unstructured data directly into your vector database by facilitating a user-friendly interface and 350+ pre-built connectors. It also allows you to build custom data pipelines within minutes by providing a low-code Connector Development Kit (CDK).
Airbyte supports automated chunking and indexing features, enabling seamless transformation and storage of raw data in eight different vector database destinations. You can also generate embeddings using a pre-built set of LLM providers, such as OpenAI, Cohere, and Anthropic.
With Airbyte’s Python library, PyAirbyte, you can create custom data pipelines within your Python workflows and perform complex transformations. It helps you clean, normalize, and enrich your data before loading it into the vector database without compromising privacy and control. This ensures the data is in the desired format and quality for optimal vectorization.
There are abundant resources available to familiarize yourself with the tool and a vibrant community of over 10K data professionals to help you with troubleshooting. You can learn more about Airbyte’s features by referring to the official documentation.
Key Takeaways
AWS offers a powerful ecosystem for AWS vector db solutions. While there isn't a dedicated AWS vector database service, you can effectively utilize services like Amazon OpenSearch, Amazon MemoryDB, and Amazon Aurora PostgreSQL.
Each service caters to different performance, scalability, and data complexity requirements. By understanding your specific use case and workload, you can select and implement the optimal AWS service to support your vector-based applications.
FAQs
Is AWS Kendra a vector database?
No, AWS Kendra is not a vector database. It is an intelligent enterprise search service that uses natural language processing and vector embeddings for improved search relevance.
Which are the four types of database platforms in AWS?
AWS offers four primary database types: relational, graph, in-memory, and key-value. Each type caters to specific data models and workload requirements.
What database solutions can we use with AWS Elastic Beanstalk?
AWS Elastic Beanstalk supports a variety of database solutions, including Amazon RDS, Amazon Aurora, Amazon DynamoDB, Microsoft SQL Server, Oracle, and other relational databases running on Amazon EC2.