Welcome to Snowflake’s Startup Spotlight, where we ask startup founders about the problems they’re solving, the apps they’re building and the lessons they’ve learned during their startup journey. In this edition, we’ll learn why the founders of data tools company TDAA, Andrew Curran and Jon Farr, chose Snowflake as the platform to deliver their app Pancake, as well as the ways they’re effectively leveraging the Snowflake Native App model.
It may be cliché to say that we enjoy solving particularly thorny problems, but we do. Even more fundamentally than that, we’re both naturally curious people. Going down a rabbit hole to identify an unsolved puzzle and dream up an innovative solution is incredibly satisfying and genuinely inspiring. Right now we’re focused on raw data quality and accuracy because it’s an issue at every organization and so important for any kind of analytics or day-to-day business operation that relies on data — and it’s especially critical to the accuracy of AI solutions, even though it’s often overlooked.
TDAA is a data tools company focused on delivering solutions that rapidly improve the quality and structure of data.
Processing complex, schema-less, semistructured, hierarchical data can be extremely time-consuming, costly and error-prone, particularly if the data source has polymorphic attributes. For many data sources, the schema of the data source can change without warning. If the data schema changes and the data pipeline to process the data does not change, it can cause data loss and generate errors. Worse, if the structure is highly complex, it can be difficult to know and extract every possible attribute, especially if the records in the data source do not all have the same structure.
When dealing with this type of data, most companies will process only the data that is absolutely required for a given task because of the time and cost issues involved. It’s a problem that we have experienced in the past, and one that we’ve seen become a blocker to product development, reporting, analytics and innovation.
Our unique combination of technology, generative AI and data skill sets gives us the right level of expertise to create a robust solution for customers facing the same data challenges as we did. Between Jon’s multidecade career in technology, including years as a data and information architect, and Andrew’s expertise in gen AI, we have the right blend of experience to create innovative solutions in a fraction of the time it would have taken before.
Snowflake has excellent capabilities for working with semistructured hierarchical data. These capabilities enabled us to design our robust solution, Pancake. Pancake automates the creation of data pipelines to transform complex, polymorphic and hierarchical data into relational streams in the form of Snowflake Dynamic Tables. We can monitor a source data set in multiple environments to alert users to changing schemas, leading to the regeneration of each data pipeline impacted. We’re able to save customers weeks or even months of time, resulting in lower engineering costs and an increased ROI.
We have a very simple business model that allows customers to scale their use of our product with their needs, and the Snowflake Native App Framework facilitates that very cleanly. Creating a Snowflake Native App allows us to bring our technology to Snowflake customers in a more secure way. Additionally, Snowflake Marketplace billing capabilities eliminate many complex operational processes we would have otherwise needed to manage on our own.
We love the fact that our code can run inside a customer’s environment without having to move data out of a customer’s Snowflake account, optimizing security and privacy which eliminates additional risk. Allowing customers to quickly install our Snowflake Native App in their cloud instance — thereby minimizing the sales cycle — is critical to accelerating product adoption.
We use AI extensively internally for most aspects of our business and development. There’s no way we could accomplish what we’ve accomplished in the time we’ve had without AI. The speed of innovation is very high — if you know how to leverage it effectively. It also allows us to do most of the work we need to do with a smaller footprint, so we can stay relatively lean as an organization. Down the road we intend to incorporate AI into our products more deeply, like being able to generate a data dictionary for your Pancaked data.
As someone with a background in AI, from long before ChatGPT, Andrew is especially excited about the attention and interest. However, we’re likely still a ways out from thoughtful integration of AI into the ways we work. For instance, things like “hallucinations” are unavoidable, and there is no way you can guard against adversarial prompt attacks if you expose the LLM directly to a user. At TDAA, we have approaches that address problems like that, but in terms of the landscape of AI, these are far from solved problems.
They should definitely consider it. The team at Snowflake has been very supportive and flexible, and the benefits of the infrastructure make standing up a product much faster and easier. As far as advice, familiarize yourself with the documentation and understand the security boundaries and protections, which may at first appear as limitations. Beyond that, familiarize yourself with the benefits of working with Snowflake as early as possible. Really take the time to engage with your Snowflake team proactively; they’re eager to help. There’s no way we could have gotten here without them. The fewer technical questions you have, the more you’ll be able to effectively use your time with the team at Snowflake for other things.
Learn more about TDAA and the preview program for Pancake, its scan and discovery Snowflake Native App, at datapancake.com or read the company’s post on the Snowflake Builder Blog on Medium for technical details. If you’re a startup building on Snowflake, check out the Powered by Snowflake Startup Program for info on how Snowflake can support your startup goals.
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