building a "bridge" from Snowflake

 

Building a "bridge" from Snowflake to a specialized on-premise stack on IBM Power 11 is not a straightforward task. It requires a strategic architectural shift, as Snowflake is a public cloud-only platform that cannot be run on-premise.¹ The "bridge" is therefore a data pipeline and processing workflow that transfers data from Snowflake's cloud environment to the on-premise infrastructure, where Equitus.us and Wallaroo.ai can then process it.

This process can be broken down into two distinct phases, each with a specific purpose:

1. Bridging the Data: From Snowflake Cloud to On-Premise Knowledge with Equitus.us

2. Bridging the AI: From On-Premise Knowledge to Action with Wallaroo.ai

Phase 1: Bridging the Data to On-Premise Knowledge

This initial phase leverages Equitus.us to move data from Snowflake and turn it into a usable, intelligent asset. While Snowflake runs exclusively in the public cloud, it provides mechanisms to load data from on-premise sources.³ The reverse is also true; data can be extracted from Snowflake and staged in an external, on-premise location. This is where the bridge begins.

Equitus's KGNN (Knowledge Graph Neural Network) platform is specifically designed to operate on-premise and is optimized for IBM Power servers with their built-in Matrix Math Accelerator (MMA).⁴ Once the data is extracted from Snowflake and resides on the local network, the Equitus KGNN platform can take over and automatically perform three key functions:

• Automated Data Integration: The platform ingests, cleans, and unifies vast amounts of structured and unstructured data from the Snowflake extract.⁴ This process eliminates the manual ETL (Extract, Transform, Load) work that would traditionally be required, turning fragmented data into a cohesive dataset.⁴

• Semantic Contextualization: Equitus's core technology transforms this siloed data into a self-constructing knowledge graph.⁴ This is the most crucial step of the bridge. The knowledge graph adds a semantic layer that enriches the data with correlations, relationships, and real-world context.⁷ This turns the raw data from Snowflake into "knowledge" that can be used for advanced analytics and AI.⁸

• AI-Ready Data Query: By creating a structured and context-rich knowledge graph, Equitus produces data that is "AI-ready".⁴ This prepared data is essential for improving the accuracy and relevance of AI systems, particularly for applications like Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs).⁴

This phase essentially migrates and transforms the data, making it suitable for on-premise AI applications.

Phase 2: Bridging the AI to On-Premise Action

Once Equitus has created a semantically rich knowledge graph, Wallaroo.ai's platform serves as the final component of the bridge, managing the AI applications that consume this knowledge.

Wallaroo is a high-performance MLOps platform designed to deploy, manage, and observe AI models in production.¹⁰ It is particularly suited for this on-premise bridge for a few key reasons:

• Hardware Versatility: Wallaroo's AI Inference Engine is designed to run on a diverse range of hardware architectures, including IBM Power (PPC).¹¹ This compatibility allows Wallaroo to fully leverage the AI acceleration capabilities of the IBM Power 11's Matrix Math Accelerator (MMA).⁴

• Performance and Low Latency: By deploying the AI models on the same IBM Power 11 servers where the data now resides, Wallaroo ensures low-latency, real-time analytics.¹⁰ This eliminates the network overhead and latency that would be introduced if the AI models were running in a remote cloud.¹⁰ This tight integration significantly boosts inference performance and ensures that AI-driven decisions are made quickly and efficiently.¹⁰

• Automated AI Lifecycle: Wallaroo automates and streamlines the entire AI lifecycle, from model packaging to continuous delivery.¹² This reduces the time to production from months to days or even hours, allowing the enterprise to rapidly operationalize the insights generated from the knowledge graph.¹²

By combining these platforms on an IBM Power 11 server, an organization can effectively build a secure and highly performant "AI bridge." Equitus handles the difficult, time-consuming task of turning fragmented data from the cloud into a contextual knowledge asset, and Wallaroo takes that asset to run and manage mission-critical AI applications, all within a self-contained, on-premise environment that prioritizes security, performance, and long-term ROI.¹⁴