The Foundational Blog

Most lineage tools miss Oracle PL/SQL, triggers, and batch jobs. Here is what closing the application layer governance gap requires.

Data pipeline breaking changes start in Python, Spark, and ORM code, not SQL. Here is how to catch them before they ship.

Regulators test the same evidence for very different rules. See what data lineage compliance actually requires and how to prove it in an audit.

Data catalogs and data lineage tools solve different problems. See how to tell them apart and choose the right one for your team.

NERC CIP-012-2 is now enforceable. See what utilities must prove about control center data governance and how deterministic lineage meets it.

The EU AI Act's high-risk deadline moved to 2027, but the governance work behind it did not get any easier or optional.

SR 26-2 explicitly excludes generative and agentic AI from model risk management scope, leaving banks to build their own governance evidence.

CCAR and BCBS 239 both require proof, not description, of where risk data originated. Here is what that evidence trail actually requires.

Warehouse scans show where PII sits today, not how it got there. Here is what full PII lineage across application layers actually requires.

AI outputs are only as trustworthy as the data foundation underneath them. Here is what that foundation must actually deliver to hold up under scrutiny.

Data governance tools that only catalog the warehouse leave audit and AI risk exposed. See the evaluation criteria that go beyond the catalog layer.

Catalog tools infer lineage from warehouse logs, missing everything that happens in application code. Here is what that gap costs and how to close it.

Governance is going agentic. Discover why source code and lineage are critical for trusted enterprise AI.

A reflection on how data governance evolved in 2025, the practices proving durable in the AI era, and what data and AI leaders should prepare for in 2026.

How integrating code-based data lineage with OpenLineage enhances visibility into rarely executed pipelines, improving compliance, security, and operational efficiency.

Data contracts are formalized agreements that define the relationship between data producers and consumers. In this blog post we'll discuss why they're needed and provide a preliminary guide to implementing them in your organization.

In this blog post we’ll explore Snowflake’s native data quality solution, called Data Quality Monitoring, review how it works, and provide a step-by-step guide to create your first data validation tests in Snowflake.

Data democratization aims to make data accessible to everyone, regardless of their position within the organization or technical expertise. In this blog post we explain why and how data lineage is a critical component for data democratization.

Discover the key differences between dbt Core and dbt Cloud. Explore which version would fit better into your tech stack and workflows to enhance productivity and value.

In today's data-driven world and more so AI, data quality is critical. With more organizations recognizing the powerful effect business intelligence has on growth and company performance, the importance of effective data quality tools and processes is clear. Due to the complexity of typical data stacks, ensuring high data quality standards requires a multifaceted approach that includes automated data lineage, data quality monitoring, and implementing strong data governance practices, ideally throughout all the development lifecycles. This blog post reviews the importance of data quality, common tools and metrics used to maintain it, and some best practices for data engineers and data leaders, focused on maximizing data quality.

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.

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 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.

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 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.