The Foundational Blog

dbt is a very powerful, user-friendly framework that has strongly emerged in the past few years as a popular choice for many data organizations to build and manage data pipelines, also referred to as “transformations”. At the same time, as dbt projects and deployments grew bigger, pitfalls and certain limitations have become more meaningful—in this article we’ll shed some light on these so we can understand better, along with suggested solutions.

We are excited to go into General Availability and announce our Seed Funding of $8M

With SQL being more popular than ever, so is the increase in SQL bugs and semantic issues that affect data quality. In this article we'll explore a few examples for such bugs, and discuss how an automated approach for code analysis can help in mitigating them.

The topic of Data Contracts has seen a massive surge in the past year, as data quality challenges continue to hinder the efforts of many organizations trying to leverage data for driving business value. In this article, we’ll cover the key concepts around data contracts with a focus on implementation, which is often overlooked, or underrepresented.

In this article we’ll review the main principles of DataOps and data management, and suggest solutions that data organizations can adopt.

Lineage is hot topic again! In this post we cover all the options for getting dbt data lineage to work for your stack

Discover how data lineage tools empower organizations to accelerate development, improve data quality, ensure compliance, and drive smarter business decisions

In this blog post we focus on some of the common pitfalls when working with dbt at scale, and in particular on the limitations of dbt in detecting SQL errors, and how to effectively navigate these challenges.

In the fast-growing reality of the modern data stack – and the modern data team that supports it – common software engineering best practices sometimes arrive late. Adopting long-standing concepts from traditional software engineering, such as SDLC and CI/CD is key for increasing efficiency, scalability, and reliability.

Data contracts have re-emerged as a popular topic when addressing data quality in the modern data stack and as such, received different interpretations and definitions. In this post, we explain the fundamentals of data contracts and describe a pragmatic approach to implementing them.

Today, we're excited to announce our latest addition: Foundational now offers comprehensive support for Tableau, one of the most popular business intelligence platforms. This integration enables organizations using Tableau to get Pre-Merge analysis for every pull request affecting Tableau, as well as automated, code-based column-level lineage for every dashboard and report.

We need to have an accurate data lineage graph every time new code is committed, while also understanding the lineage impact the commit has on the existing system. In this post we discuss some of the challenges in doing source-code based data lineage at scale, focusing on SQL.

Our goal at Foundational is to make it easy, predictable, and straightforward to develop code for data – which is any piece of code that might affect data throughout the data stack. We are proud to announce our full support for Looker, a prominent business intelligence tool.

There’s a counterintuitive difference between writing code for software and writing code for data - in software you are a lot less surprised. Why counterintuitive? Because you’d think that SQL would not surprise you that much, but it’s oftentimes the opposite

Data and code are the two most sensitive assets an organization has. Today we are proud to announce that Foundational is SOC 2 Type II certified, having obtained the industry-leading standard for the security, availability, and confidentiality.

Starting a company is first and foremost personal. Along with many other things, it combines the desire to create a new organization and DNA, along with the passion to impact an industry.

Data lineage is a popular, widely discussed topic, yet incredibly nuanced. It is also surprisingly often misunderstood. It is also an important capability that many products in the data ecosystem are required to develop.