Chartio categorizes data management sophistication into four main stages: source, lake, warehouse and mart. We believe that as companies grow and as data stacks evolve, companies advance through each of these stages. And regardless of what stage you're in, Chartio is here to support you.
Modeling is a key factor in advancement. And while we’ve built plenty of modeling features into Chartio, we strongly believe that true modeling should be done on the database and done in SQL - making it more performant and accessible for your teams.
That’s why we partner with and promote dbt, an open-source, SQL-based modeling language, for database-level modeling.
As your organization gets larger, and more people need to work with data, quality data modeling becomes increasingly important. When your team is ready to build a data warehouse as a single source of truth, Chartio can help. We offer expert advice and trainings, an authoritative book, and a product that flexibly transitions with you.
With Chartio, you don’t have to worry about having your data set up perfectly from the start. And we don’t require three months of extensive modeling to get started. Instead, we strongly encourage data agility. That’s why we’ve built a flexible product that works with your data no matter what stage you’re at and through any transition.
We encourage database-level modeling by creating a schema of clean views on top of your data lake. Open-source libraries like our partners at dbt provide an excellent framework for this, with enormous benefits.
No lock-in. When you create models with SQL on the database level, your work isn’t locked into one platform forever—so you can change tools, use other products, write cron jobs, and more.
No new languages to learn. If your whole data team already knows SQL, why force them to learn a new language?
No magic layers. When you use SQL to write your models, you can know and trust what it’s going to do. That’s not true with specialty modeling languages like LookML.
Extensible. If you want to build on the power of SQL, dbt offers the added functionality of macros.
Performance control. When you write your models in SQL, you can tweak them to be performant. You’ve got full control.
Easily persistent. With materialized views at the database level, it’s incredibly easy to make persistent datasets with just a query—no extra code, products, or data locations.
Compatibility. We’d love to be the only product you ever use for data, but we realize that may not be the case. When you model with SQL on the database level, you can connect and utilize any other compatible tool.
We highly recommend using dbt, an open and vibrant project that makes setting and maintaining SQL views a snap. We use it ourselves, and love it—partly because of its many added features.Learn more
Macro functions. With dbt’s templating language extending SQL with macros, you don’t have to keep repeating yourself.
Automated Testing. Easily validate your models and ensure data integrity.
Version Control. Because dbt is built around Git, your files are part of the repository, so deploying your changes can be as easy as a Git push.
Easy incremental loading. Views can easily be made persistent and instructed to update themselves with only the latest incremental changes.
Environments. With dbt, you can easily create separate staging and production environments for your models, so you can collaborate on and test your work before it’s fully released.
Making data persistent inside of Chartio is also a snap. If you’ve created a nice summary table or a complicated joining of data from multiple sources, and want to save those results to be used by many different charts, you can do so easily with our Data Stores
Agile, quick-and-dirty modeling
Controlled caching with adjustable refresh intervals
Easily store datasets of blended results from multiple sources
Repeat yourself less
We’ve published an entire book on cloud data management, and all the best practices taking people through what we call the 4 stages of data sophistication.Download for free