Embrace the code-optional mindset with Matillion
One of the pillars of Matillion Data Productivity Cloud is "everyone-ready", which means everyone on a company's data team is allowed to work in their own way. As data responsibilities are democratized across an organization, we see many different skill sets and technical levels emerge. While Matillion provides a phenomenal platform for non-coders, we also want the coders to come along for the journey. Recently we discussed the ins and outs of low-code, high-code, and your-code approaches, and why companies need flexibility. We have long provided support for both Python and SQL, embedded in Matillion jobs orchestrated alongside low-code and no-code processes. While these capabilities are widely-used, we also know there are other popular coding frameworks that deserve our attention and we are continuing to expand our high-code support to dbt and Spark Notebooks.
dbt: coded transformations and libraries
- dbt has been on the rise and many dbt developers would like to leverage their coding skills to implement custom transformations and add value alongside their low-code colleagues.
- Many companies using dbt have purpose-built libraries they can embed right inside Matillion jobs, enabling new collaboration opportunities between data developers with different styles as well as providing a single point of orchestration.
Spark notebooks: operationalize machine learning with Matillion
- Spark notebook support means we provide a single point of contact for customers leveraging Matillion to prep data for machine learning, supporting the complete loading, transforming, and analyzing of data. This connects the dots between the ELT operations done in Matillion and the subsequent data analysis that, currently, must be carried out in the data ML platform.
- Additionally, the option to run notebooks from Matillion is one less reason for customers to have to switch between two separate user interfaces in order to perform basic operations on their data.