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Fishtown Analysis, the company behind an open source “analytical engineering” tool called dbt (data creation tool), today announced that it has renamed itself Dbt Laboratories and raised $ 150 million in a Series C funding round at a valuation of $ 1.5 billion.

Analytical engineering, for the uninitiated, is a relatively new role that describes the process of taking raw data after entering a data warehouse and preparing for analysis. The role itself serves as a sort of bridge between the spheres of engineering and data analysis, requiring them to transform data into a usable form that can be easily queried by others (e.g. marketing specialists) of the company. Dbt Labs juxtaposes the role with that of a data analyst in this description:

Analytical engineers provide end-user specific data sets, modeling the data in a way that allows end users to answer their own questions. While a data analyst spends their time analyzing data, an analysis engineer spends their time transforming, testing, deploying and documenting data.

Founded in Philadelphia in 2016, Dbt Labs has spent the past five years designing a set of tools to help data analysts “create and disseminate organizational knowledge,” as it puts it. Much of this means offering consulting services in addition to the open source dbt project, used by large companies such as HubSpot, GitLab, and Jetblue.

But what exactly is debt? In a nutshell, dbt is a command line tool that allows data analysts to transform raw data by writing dbt code in their regular text editor and then invoking dbt from their command line. Dbt then compiles the code into SQL and runs it against the company’s database. So, dbt is a development environment that “speaks the preferred language of data analysts” (that’s SQL, in case you were wondering).

Above: User interacting with dbt

For context, a modern enterprise data stack consists of a myriad of components, spanning data ingestion tools like Fivetran and cloud-based data warehouses like Snowflake and Google’s BigQuery. Data can be “transformed” as it enters the data warehouse in a process called “extract, transform, load” (ETL), where companies like Matillion come into play. But data can also come into play. can be transformed later by running SQL scripts directly in the warehouse, through a process called “extract, load, transform” (ELT). The latter allows faster load times, but requires more processing power because data must be transformed on demand. This is where the power of modern analytical databases like Snowflake and BigQuery really shines.

Simply put, dbt is ELT’s “T”: it’s designed to transform data that’s already in a data warehouse. “Dbt is a key part of the modern data stack – it connects to the cloud data platform and harnesses the full computational power of those platforms to transform, test and deploy data sets,” said at VentureBeat CEO and Co-Founder of Dbt Labs, Tristan Handy.

After the transformation, companies can use these datasets as they see fit, whether it’s to train machine learning models or to power business intelligence (BI) tools like Tableau or Looker.

The story so far

According to Handy, he initially developed dbt based on his own experiences as a data analyst.

“I worked as a data analyst for about fifteen years and was always slowed down by appalling workflows: emailing spreadsheets, downloading huge .CSV files, saving SQL files. on my desk, ”he said.

Fast forward five years, and Handy said debt uptake has grown 200% every year since launching, and in the first quarter of 2021, his company’s revenue doubled year on year. ‘other. The main driving force behind this, as is apparently the case with just about all new technology these days, is the rapid transition from on-premises infrastructure to cloud computing – in this case, the platforms of cloud-based data such as Databricks, BigQuery, Snowflake, and Amazon Redshift.

“The big change for our industry is the move to the cloud,” Handy said. “Modern cloud data platforms are all a fundamentally new class of ‘thing’ that just wasn’t possible in the on-premises world of ten years ago. Data grows very quickly, and the processing workloads in addition to that data vary widely. These two factors mean that the elasticity of the cloud is simply extremely important, and we’re confident that all (or nearly all) data workloads will migrate to the cloud over the next decade.

The scalability and elasticity offered by the cloud opens the doors to things that just weren’t an option before, like the ability to perform data transformation in the warehouse, which speeds things up dramatically.

“The fundamental unlocking of the cloud made performance a much smaller issue, allowing data analysts to take charge of the entire information generation process,” Handy continued. “This has, in turn, led to the rise of analytical engineering – the practice by which analysts build modern pipelines on top of cloud data platforms.”

Show me the money

Previously, Dbt Labs had raised around $ 42 million, all of which came in the past 14 months through two separate funding rounds. With its latest cash injection – which was co-led by Sequoia Capital, Andreessen Horowitz and Altimeter – the company has said it will double the development of its main open source platform.

“Right now, our goal is to improve our core offering and support its exponential growth as the foundation of one of the most dynamic areas of all enterprise software,” Handy said. “We also have an eye on new experimental areas of product development, but nothing that we’re not ready to share just yet. “

As far as rebranding goes, well that also makes a lot of sense given how Fishtown Analytics and dbt have evolved over the past five years. Initially, dbt was purely an open source product with no commercial component – Fishtown Analytics was the main contributor and user of the project, and it was selling consulting services in addition to the open source project. In the years that followed, however, the debt got their own Team and Enterprise premium plans, which include API access, single sign-on, professional services, and more. For this reason, it is not necessary for two separate “brands” to monetize the same product, which could also be confusing.

“This confusion has been a big motivation to change our name from Fishtown Analytics,” Handy said. “By renaming the company, we are declaring both our relationship with dbt – which we have created and maintain – and our commitment to its long-term success.”

Debt interface

Above: Dbt interface

The OSS factor

According to Dtb Labs, there are some 15,000 “data professionals” in the dbt Slack community, 5,500 companies using dbt, and 1,000 dbt cloud customers who pay for centralized access through a web interface.

However, since dbt is released under a permissive Apache 2.0 license, this means that there are very few restrictions on how the wider commercial world adopts it. So couldn’t that mean that other companies with deep pockets might look to lean on debt? It’s entirely possible, which is part of the reason why Dbt Labs chose to raise another significant chunk of funding so soon after the previous two rounds.

“Dbt generates a huge amount of usage across several of the major cloud data platforms, which makes dbt and its community very strategic for these platforms,” Handy said. “We also know that the major cloud providers love to sell managed versions of open source software. Put those two things together and we expect at least one, if not more, of the cloud platforms to launch some sort of debt management service in the coming year. Our space is heating up, forcing us to accelerate our ability to build differentiated products. That’s why we relaunched it again.

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