UPDATED 19:42 EDT / OCTOBER 12 2022

BIG DATA

Matillion unveils new Data Productivity Cloud with low-code user interface

The well-funded British data integration startup Matillion Ltd. today announced the launch of a new, low-code platform that comes complete with its own ecosystem of third-party tools to help enterprises get critical information ready for business in rapid time.

Matillion hit the headlines in September 2021 when it closed on a $150 million late-stage funding round that took its total amount raised to more than $310 million and its valuation to north of $1.5 billion. It has managed to attract that money because of its compelling offering based around extract, transform and load services that are used to prepare and mix data from multiple sources and make it ready to be analyzed for business insights.

In this way, Matillion overcomes a key pain point for enterprises, since moving data to an analytics environment is challenging to say the least. The issue is that, very often the native format the data is stored in is not supported by the desired analytics tool. So the data has to be modified before it can be processed.

That’s where Matillion comes in with a suite of pre-packaged connectors that can easily bring data from A to B in the right format. It works with dozens of popular databases, as well as software-as-a-service platforms such as Salesforce.

Building on this foundation, Matillion has created an integrated platform called the Data Productivity Cloud, which is a revamped and rebranded version of its offering that can adapt to almost any kind of data environment. With it, data teams have the tools they need to load, transform, synchronize and orchestrate data through a single platform that promises to vastly increase their productivity.

The advantage of the Matillion Data Productivity Cloud is in its simplicity, the company explained, as it’s uniquely ready for any user to connect data and put it to work. Matillion employs a wizard-based approach that enables users to build custom connectors to load any kind of data into the platform, with no coding necessary.

Matillion further explained that its new platform is built on an open and extensible architecture, which means it’s ready to be put to use alongside multiple third-party cloud data solutions. The Data Productivity Cloud comes with native integrations to dozens of popular cloud-based data tools, helping to keep cloud data environments running smoothly. That makes it even easier for teams to create data transformation and orchestration jobs, either through a low-code interface or using their preferred coding method.

Other new features include enterprise application connectors for SAP, Workday and Anaplan, enabling customers to connect to these data sources and glean insights from them with minimal fuss. In addition, Matillion said it’s the first ETL platform to support Snowflake Inc.’s Zero Copy Clone, enabling teams to test against a clone of live data without incurring additional risk or costs. It’s a useful feature that should be embraced by DataOps teams, Matillion said.

In addition, Matillion will soon add support for code orchestration written in Spark notebooks for Databricks, the company said. This will make it easier for data scientists to orchestrate machine learning models inside Matillion ETL for Databricks from any notebook.

Finally, Matillion said that through its Data Productivity Cloud, it can now achieve unlimited scalability with full control. That’s enabled by secure, serverless remote runners that are able to scale every aspect of data workflows, the company said. What that means is that teams can collect, enrich and recirculate data without boundaries from within their cloud environments, with governance and data sovereignty built in.

“Matillion puts data teams at the center of driving competitive advantage by helping them to deliver business-ready data, faster,” said Matillion Chief Product Officer Ciaran Dynes. “The Data Productivity Cloud is designed to meet data practitioners wherever they are – at their skill level, for their data stack and to suit their current and future ambitions.”

Image: Matillion

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