Data stack for AI driven architecture with Crunchy Data

Crunchy Data's suite of Postgres products support data-driven AI workloads, like generative AI, large language use-cases, semantic search, and predictive analytics. Crunchy Data Warehouse pairs well as a data solution for OpenShift AI.

Overview
AI-driven data stack from Crunchy Data

Not only is AI rapidly changing the world, AI is also rapidly changing. AI has progressed away from the implementation of a monolithic model, and now uses multiple models within a single process. For instance, in a semantic search, a prompt is sent to a classification model, then, executes a search on internal data using nearest neighbor search on embeddings, and finally an LLM summarization step based on original prompt and nearest neighbor.

To find success with AI, organizations need a data stack that enables rapid iterations. The Crunchy Data suite of tools offers benefits like zero-ETL, open-file support, foreign data connections, and easy data movement. Combine those with native vector data storage and indexing to have all the tools necessary for AI success.

Crunchy Data has the open data tools that organizations need:

  • Crunchy Postgres: the most advanced open source database for application workloads. Native storage for embeddings, including HNSW indexing.
  • Crunchy Data Warehouse: a data lake house with zero-ETL capabilities

Deployments available on cloud, on-prem, and hybrid cloud:

  • Crunchy Postgres for Kubernetes: on-prem and hybrid cloud deployments
  • Crunchy Bridge: fully-managed public cloud deployments
Turn-key data solution

When paired with OpenShift AI, at your fingertips, have the full suite of models and data necessary to build a modern AI stack.

SQL Data Lake House

Use SQL to orchestrate, extract, and analyze data across the organization. Get to solutions faster with the knowledge and toolset already within your organization.

AI & Vector Optimized

Being the data output from AI classification models, vector data is the backbone of RAG and MCP architecture. Optimize vector data using HNSW indexes to create high-performance queries.

Automated Deployment

In today’s world, data agility is the difference in days versus months. With a single script, create the same data infrastructure as many times as necessary to support your teams and workloads.

Using Crunchy PostgreSQL for Kubernetes allows us and our customers to order databases in self-service mode, enabling rapid development cycles and substantially shortened deployment times.

David JörgProduct Owner, FOITT

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ResourcesFAQs

Does AI need data? What is the role of data in the modern AI stack?

The modern AI stack is not a single entity, but a set of entities behaving in concert. For instance, semantic search may include 2 - 3 different models that include classification, prompt enhancement, and, generative AI. Other purposes will have similar multi-step process. Between each model, the flow is enhanced with data specific to the needs of the next step. In the prior example, after the first classification, a nearest neighbor search will be performed to retrieve content similar to the initial request. That data will be used to enhance the prompt in the following step. In this world, AI further enhances the importance of data within an organization.

What makes Crunchy Data Warehouse a data lake house?

Before we had a data lake, we had a data warehouse. Around 2010, data was stressing the capabilities of the tooling and infrastructure, so new tools were designed to store data on cloud object storage. This data on cloud object storage became known as a “data lake.” However, the new tools for querying data were fragmented and did not have the cohesiveness of the previous generation of data warehouse. The solution is a “data lake house” that is able to serve as the entry point for the data lake. Crunchy Data Warehouse is Postgres with read-write capabilities to a cloud object storage.

What is data agility? Why does it matter with AI?

AI is rapidly changing. As we discussed in the question about the role of data with AI, data is a key component of enterprise AI stacks. Thus, to be able to stand up an AI stack, run performance tests against the stack, and iterate requires technology agility. If the data initialization process is too rigid and formalized, the limitations of iterating on AI will be the ability to iterate on data.
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