Systems | Development | Analytics | API | Testing

New: Close The Gaps In Your Reporting Stack With Custom Integrations

Most teams work across dozens of tools, and not all of them connect to their reporting workflows out of the box. There are always sources that fall outside the native integrations list: an internal tool your team built, a platform specific to your industry, or a piece of software that a vendor hasn’t prioritized supporting yet. When that data isn’t directly available, teams get it in however they can.

Why every data role needs Open Data Infrastructure

Analysts, data engineers, ML engineers, and data scientists don’t work the same way; they shouldn’t have to. Today’s data ecosystem includes more roles, more tools, and more specialized workflows than ever before. The days of limiting access to a single warehouse or lake — controlled by a small group of data engineers or analysts — are over.

Data Integration Tools Aren't the Problem. Your Source Data Is.

Data integration tools are designed to move and join data. But what they’re not designed to do is burn half their capacity cleaning up what arrives at the input. When a source exposes a schema built for application performance rather than analytics, the pipeline must compensate: Anything typed as a string because it was easier at build time gets cast into numbers or dates before a calculation can touch it. The difficult truth is this is cleanup and not value-added integration work.