Crunchy Data

Trusted by enterprises, loved by Developers, Crunchy Postgres supports 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.

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Overview

Differentiation with AI requires data enablement throughout an organization. AI is rapidly progressing away from a monolithic model to using multi-layered techniques and patterns empowered by precise data at each step. Gone are the days of an AI stack being set-it-and-forget-it, and data agility is the engine that enables iteration of AI workloads.

Crunchy Data has the open data tools that organizations need:

  • Crunchy Postgres: the most advanced open source OLTP database for application workloads. Native storage for embeddings, including HNSW indexing.
  • Crunchy Data Warehouse: a data lake house in Postgres augmented with a high-performance, columnar vectorized query engine. Full read-write access to a data lake, including automated maintenance.
  • Change Data Capture: syncs data from an operational to analytical data stack in near-realtime.

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

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

Today we're able to easily analyze 1000s of raw files without any ETL complexity. We get all the benefits we're used to with Postgres on Crunchy Bridge, but without writing any custom JSON parsing code or specifying a schema for the big chunks of JSON. Instead of causing Third Iron to create engineering solutions to new data analysis problems, Crunchy Data solutions solved that complexity, and now we can focus on solving problems for our customers.

Karl BeckerCo-Founder at Third Iron
SUCCESS STORY

Third Iron Chooses Crunchy Data Warehouse Over Amazon Athena

Third Iron is a library technology company, delivering modern digital access solutions that streamline the user experience through AI models based off of large data sets.

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Benefits to working with Crunchy Data

Turn-key data solution

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

A 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

Vector data is the backbone of RAG and MCP architecture because they are the data outputs of AI classification models. Optimize vector storage using HNSW indexes to create high-performance queries.

Automated Deployment

In our 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.

Interested in working with this partner?

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OfferingsResourcesFAQs

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 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.

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.
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