Hakkoda

Most organizations have spent the last decade building pipelines. Move data from here to there, land it somewhere, clean it up, hand it off. The work was real, but the output was almost always the same thing: a table, a report, a dataset that someone requ

In the first installment of this series on reverse ETL and its applications, I made the case that reverse ETL is defined by the pattern, not the tool, and that everything falls out of one axis: how tightly the warehouse is coupled to the receiver. At the

As utilities modernize capital planning and execution, enterprise data foundations and AI are changing the economics of technology decisions and opening new paths beyond traditional SaaS-only approaches.Utilities are entering a capital-intensive decade. G

For years, the main question in the data world was simple: Where should we store our data? Companies built data warehouses, data lakes, and increasingly sophisticated platforms to collect information from across the business. Today, that question is chang

Lately almost every architecture conversation I'm in bends toward the same question. Someone has a curated dataset in the warehouse (a risk score, a patient summary, a cleaned-up eligibility table) and they don't want to look at it in a dashboard. They wa

For years, enterprises have been told that cloud modernization is a technology decision: choose the right platform, migrate the right workloads, and unlock scale, performance, and innovation.But any CIO or CDO who has led a major data platform migration k

In previous pieces in this series, we explored both the problem and the foundation for contract intelligence in automotive and beyond.Part one, A Modern Data Foundation for Contract Intelligence in Automotive, examined how critical commercial terms, inclu

For a leading financial services company, ongoing managing tens of thousands of contracts was beginning to pose a significant visibility challenge for legal teams.While the organization had a substantial contract portfolio, it lacked a reliable way to det

Like everywhere else, AI is moving quickly in the life sciences space. In the last two years, AI-powered innovation is already accelerating drug discovery, reshaping clinical trials, modernizing regulatory workflows, and opening new possibilities across m

Data engineering teams handle many small, repetitive, judgment-heavy tasks (writing YAML configs, tuning slow SQL queries, applying correct collations/naming standards, etc.). None of these is individually difficult, but they take real time and are easy t