How Preql Transforms Data Transformation

More than a million small businesses use the Shopify e-commerce platform to reach a global consumer audience. This includes direct-to-consumer (DTC) stars like Allbirds, Rothy's and Beefcake Swimwear.

But online sellers like these also ingest data from platforms like Google Analytics, Klaviyo, Attentive, and Facebook Ads, which quickly complicates weekly reporting.

dbt and Preql

As the name suggests, data transformation tools help convert data from its raw format into clean, usable data that enables analysis and reporting. Centralizing and storing data is easier than ever, but building report-ready datasets requires aligning with business definitions, designing output tables, and encoding logic in a series of interrelated SQL scripts, or "transformations".

Enterprises invest heavily in data infrastructure tools, such as ingest tools, data storage, and visualization/BI, without having the in-house expertise to effectively transform their data. But they quickly realize that if you can't effectively structure your data for reporting, they won't get any benefit from the data they store or the investment they've made.

The space brings together two major players: dbt and startups.

Founded in 2016, dbt "created the premier tool in the analytics engineering toolkit," as the company puts it, and it's now used by more than 9,000 companies, and is supported by over $414 million.

But dbt is a tool for enterprise developers with established analytical engineering teams.

Preql, on the other hand, is a no-code data transformation tool that creates start-ups that targets business users who may not have expertise in programming languages, but have nevertheless need reliable and accessible data.

Preql's goal is to automate the most difficult and time-consuming steps in the data transformation process so businesses can be up and running in days, unlike the six to 12 month window for others tools.

"We created Preql because the transformation layer is the most critical part of the data stack, but the resources and talent needed to manage it make reliable reporting and analytics inaccessible for businesses without great functions. of data,” said Gabi Steele, Co-Founder and Co-CEO of Preql.

The startup is therefore positioning itself as an alternative to hiring entire teams of analytics engineers just to model and manage business definitions, especially among start-ups that first develop their analytics capabilities. data.

In other words, Preql is the buffer between the engineering team and the people who actually need to use the data.

"Data teams tend to be very reactive. The business is constantly asking for data to guide decision-making, but in today's transformation ecosystem, even small changes to data models take time. and expertise.

If business users can truly manage their own metrics, data talents can free themselves from the constant back and forth of handling report requests and focus on more sophisticated analytics,” said Leah Weiss , co-founder and co-CEO of Preql.

But that doesn't mean that dbt and Preql are fierce rivals. In fact, they are part of the same data transformation community, and integration is planned.

“One way to think about it is that we want to help organizations get up and running very quickly and get the most out of the data they already collect and store without having to rely on specialist talent who are very knowledgeable in debt," Steele added. "But as these companies get more sophisticated, we'll produce dbt, so they can take advantage of it if that's the tool they're most attuned to. ease."

A closer look at Preql

The startup raised a $7 million seed round in May, led by Bessemer Venture Partners, with participation from Felicis.

Preql collects business context and metric definitions, then abstracts the data transformation process. It helps organizations get started and run with a central source of truth for reporting without having a data team or writing SQL.

Preql reads data from the warehouse and rewrites clean, report-ready schemas. It partners with data ingestion tools that move data from source applications to the warehouse, like Airbyte and Fivetran, and cloud data warehouses like Snowflake, Redshift, and BigQuery. For businesses that consume data in BI tools, it also partners with Looker, Tableau, and Sigma Computing.

DTC target

Preql initially focuses on the DTC market, in part because metrics, such as customer acquisition cost (CAC), conversion rate, and lifetime value (LTV), are standardized. They also tend to have lean operations.

“We have found that these companies work very hard to download...