Matillion raises $100M for ETL to enable data middleware

ETL vendor Matillion said Feb. 16 it raised $100 million in a Series D round of funding.

The vendor said the new money will help it develop its data middleware technology and expand its go-to-market efforts, and that the funding marks a major phase in its growth.

Getting data from one place to another, in a format that is usable for data analytics, business intelligence and machine learning, is a challenge for many organizations. Extract, transform and load (ETL) technologies such as Matillion's platform are important tools for attacking the problem of ingesting and making data usable.

Matillion, based in Manchester, England, positions its technology as a cloud data middleware platform that enables data integration.

Among Matillion's products is the company's namesake enterprise ETL platform, which provides integrations with cloud data warehouses such as Snowflake and Amazon Redshift. In March 2020, Matillion's Data Loader tool became generally available, providing a service for loading data to create data pipelines.

Matthew Scullion

The latest funding round was led by Lightspeed Venture Partners, with participation from Battery Ventures, Sapphire Ventures and Scale Venture Partners.

In this Q&A, the vendor's co-founder and CEO, Matthew Scullion, discusses the growing need for data middleware and how the vendor plans to use the funding.

Why are you now raising $100M for Matillion and its data middleware?

Matthew Scullion: We see ahead of us a large market that is being created around data platforms.

The main modus operandi that we operate in with at Matillion is that we are waking up every morning to build a large and consequential software company; that's our goal. This is a large market and we certainly have large customers.

So, if you look at this $100 million investment and what will we use it for, we'll use it to further accelerate the pace of which we're building out our product platform. That means building the small features that customers want to see added every day and the big features and products that they want to see added as well.

Then, of course, we'll continue to scale and refine the go-to-market side of our business to allow us to even better support our enterprise customers on a global basis.

How has Matillion and its data middleware vision changed since you started the company in 2011?

Scullion: What the company was originally incorporated to do back in 2011, was something quite different than what we do today.

We used to build and look after a modern data stack on behalf of each of our customers to give them enterprise-quality analytics and we chose to do that in the cloud. As a byproduct of that we built a lot of data warehouses on behalf of our end customers and we did it in the cloud.

Over the last 12 months, the pandemic has brought an acceleration in the pace at which enterprises are modernizing and migrating data workloads to the cloud.

At the time that wasn't common. If you think back to 2011, neither Snowflake nor Amazon Redshift had been launched yet. What we noticed is that first of all, the main technology you actually use as a person building a data warehouse isn't the data warehouse itself. Rather, it is the technology that allows you to get data into your data warehouse that's needed first.

Back in 2011, we were using commercial off-the-shelf, data middleware ETL technology to do that work. It was good at making data useful, but it wasn't built for the cloud and that was slowing us down. That led us to then build the Matillion ETL. In October 2015, we launched Matillion ETL as a product in its own right and that became the focus of the company.

Over the last 12 months, the pandemic has brought an acceleration in the pace at which enterprises are modernizing and migrating data workloads to the cloud. You know, this was a serious industry in January 2020. It was the thing that everybody needed to do as a survival need by the end of 2020.

How do you define data middleware?

Scullion: Ultimately, it's about being able to get insights with data faster, that gives customers competitive advantage.

In terms of a data middleware platform, what we're really talking about is being able to provide customers, all the parts that are needed. That includes the visual, low-code/no-code capability to build data orchestrations and loading data. It is about transforming data, joining it together, cleaning it up, embellishing it and aggregating it. So, when we talk about data middleware it is really that platform that gives customers accelerated time to insight.

This interview was edited for clarity and conciseness.