Developing Intelligent Data Apps with Microsoft and Snowflake
Artificial Intelligence is getting mainstream now led on the OpenAI as adoption continues to increase after all the hype. Organizations now are looking at Data Apps instead of just Traditional Analytic Solutions like Dashboards or Reports. Snowflake is being the center of it all with its Data Cloud as “Organized Data” is essential and the basis for the Data Apps and AI. Microsoft, being a leader in AI, continuously evolve and develop innovative solutions that make AI securely accessible for Enterprises and organizations to consume and innovate on. The combination of both is perfectly suited stack for developing intelligent Data Apps.
The previous blog Integrating Azure OpenAI with Snowflake: Architecture and Implementation Patterns talks about the various integration patterns with Azure OpenAI and Snowflake and Implementing Generative AI in Snowflake with Azure OpenAI using Streamlit talks about using Streamlit with Azure OpenAI and Snowflake. In this we will be discussing about using those patterns to expand on Developing Intelligent Data Apps.
Tiered Architecture
In the traditional Tired Application Architecture, there is a separation of UI (Front End), App Logic (Backend) and Data (Database). The data is moved into the backend logic where the actual compute happens. In the advancement of cloud and AI era, the Data Apps are now meeting Data where it resides, inside the Cloud Data Warehouse. The modern Architecture for Data Apps with reference to AI and LLMs looks like this.
The key differentiation is the separation of the Prompt Engineering layer from both the Application and the Data. This following the traditional pattern of tiered architecture but is more suitable for the modern age infusion of LLMs and AI. The other trend to note here is that a lot of Data Apps do not require heavy framework like a full-fledged Software Application does but is more suitable for Low-Code / No-Code solutions or simple frameworks like Streamlit. So how does Organization deploy a Solution with this that is secure and provides Enterprise capabilities? Enter Microsoft and Snowflake.
Modern Data Application Stack
Microsoft, being a leader and forerunner in building AI for Enterprises focused on security and trust has a complete Data and AI stack that organization can leverage to deploy and run AI workloads. All the different services co-exist and run together making it like a single stack that organization can adopt for various use-cases. Now organizations can no longer compromise between Security and Adoption and can achieve both with AI excellence.
Before an organization embarks on AI, it needs a strong Data Strategy, and the Snowflake Data Cloud helps the organization to get there. With its new Governance features and the Snowflake marketplace, organizations can now achieve scalable data in a single location that is completely native on the cloud. Snowflake is also pioneering the Data Apps with its new release of Snowpark Container Services and integration with Streamlit.
Thanks to the recent collaboration, Customers can now bring the best of both to deploy the modern Data Application Stack powered by Microsoft and Snowflake.
Data Apps are developed using Power Apps which provides a Low Code / No Code AI infused platform. Power Apps now has a native connector with Snowflake that enables it to operate directly with Data in Snowflake. To know more about the Power Apps connector for Snowflake, checkout Leveraging Power Apps and Snowflake for Your Business Applications.
Machine Learning models are developed with the Azure Machine Learning running with Snowpark. Azure Machine Learning supports orchestration and MLOps capabilities and Snowpark helps to run the models natively on Snowflake Data. To know more about the Azure Machine Learning connector, checkout Empowering Snowflake users with Azure Machine Learning
Prompt Engineering and LLM’Ops are developed using the Prompt Flow and the new Azure AI Studio. The Azure AI Studio helps in evaluating LLMs and deploying flows. It provides secure connections to Azure OpenAI which will be used for Generative AI tasks. Checkout the QuickStart guide on using Azure OpenAI and Prompt flow Getting Started with Azure OpenAI and Snowflake
The Data ingestion, preparation and transformation happens within the Data Cloud which provides a secure and scalable environment to operate with the Data. Additional Microsoft tools like Azure Data Factory and Purview can interact natively with Snowflake, enabling them to perform complex data transformations making it ready for the AI workloads.
The Modern Data Apps Stack opens up new opportunities in exploring AI powered Data Apps and makes it easier to build and deploy apps using Snowflake and Microsoft.
