By now, most are aware of technologies like generative AI, large language models (LLMs), ChatGPT, and the company OpenAI. Similarly, most of these users are aware that these technologies and AI models require sufficient data to function and generate results. With this in mind, where and how is that data stored? Vector databases – the same data fueling these AI applications.
Working with generative AI and LLMs is becoming increasingly relevant to business operations. As such, this post will explore the ways in which vector databases can be developed or integrated into Microsoft Fabric.
Types of Vector Databases Available Within Fabric
A vector database is a specific kind of database that saves unstructured data (text, images, audio, video, etc.) in the form of multi-dimensional vectors representing certain characteristics or qualities. The number of dimensions in each vector can vary widely, from just a few to several thousand, based on the data’s intricacy, relationship, and detail. It is not a NoSQL database in which data is stored in JSON documents.
The primary benefit of a vector database is its ability to locate and retrieve data according to their vector proximity or resemblance, by using “Approximate Nearest Neighbor” (ANN), hashing, and graph-based search swiftly and precisely.
This allows for searches rooted in semantic or contextual relevance rather than relying solely on exact matches or set criteria as with conventional databases. For instance, with a vector database, users can:
- Search for songs that resonate with a particular tune based on melody and rhythm.
- Discover articles that align with another specific article in theme and perspective.
- Identify gadgets that mirror the characteristics and reviews of a certain device.
Within the Fabric ecosystem, there are two types of vector databases available: OneLake and Synapse.
OneLake supports vector databases by enabling you to store and query vector embeddings in the lakehouse, which is a combination of data lake and data warehouse. OneLake leverages Azure Data Lake Storage Gen2. OneLake also integrates with Azure Cognitive Search, which is a cloud search service that uses AI to enrich your data and provide natural language and image processing capabilities.
Synapse supports vector databases by using the Synapse SQL engine. Synapse also integrates with Azure Machine Learning, which is a cloud service that helps you build, train, and deploy machine learning models.
Vector Databases Within Fabric Compared to Other Vendors
Having vector databases within Fabric provides several benefits compared to other vendors, such as:
Integration
Fabric integrates data lake, data engineering, and data integration from Power BI, Azure Data Factory, and Azure Synapse into a single SaaS experience. It also unifies the OneLake and lakehouse architecture across the enterprises. This means a highly integrated, end-to-end, and easy-to-use product that is designed to simplify your analytics needs. No need to piece together different services from multiple vendors or deal with the complexity and overhead of managing the underlying infrastructure.
User-Tailored Capabilities
Fabric offers a comprehensive set of analytics experiences designed to work together that can be tailored to a specific persona and a specific task. It is possible to perform a wide range of analytics tasks using vector databases, such as data transformation, data enrichment, data exploration, data visualization, data modeling, data prediction, data streaming, and data reporting.
Centralized Governance
Fabric provides centralized administration and governance across all experiences. IT teams can centrally configure core enterprise capabilities and permissions are automatically applied across all the underlying services. With Fabric and Purview, you can ensure data quality, compliance, and security for your vector databases.
Performance
Fabric leverages the power and scalability of the Azure cloud platform to deliver high-performance analytics for any database, including vector databases. Fabric supports both serverless and dedicated modes for your analytics workloads, allowing you to optimize your costs and resources. It is possible to ingest both batch and streaming modes.
Final Thoughts
In conclusion, vector databases are a powerful way to store and query data based on their semantic or contextual meaning. Fabric is a SaaS platform that offers a comprehensive suite of services for data engineering, data science, data warehousing, real-time analytics, and business intelligence. It integrates data lake, data engineering, and data integration from Power BI, Azure Data Factory, and Azure Synapse into a single SaaS experience.
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