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Many things in data engineering are this or that. And that is good. But we do deal with some less-than-obvious concepts, like semi-structured data.

Data governance as code and RoboCop have lots in common. Both are cool. Both automate enforcement. And both should be taken seriously on their respective beats.

Time spent managing data does not an exceptionally data-driven organization make. And data maturity models are key for helping decision-makers understand why.

Common data quality issues aren't to be ignored, especially since each compounds under the pressures of big data. But data teams can beat them to the punch.

Serving as the "back of the house" in the restaurant that is the data-driven organization, data quality management (DQM) is the secret to serving up success.

Despite all the upsides, the prospect of automated data governance can be daunting. For good reason. However, data contracts can make all the difference here.

Learn why data leaders are weighing the benefits of federated vs. centralized data governance, in addition to one way of ensuring either can excel in practice.

In theory, tracked data lineage is better than none at all. But in practice, automated data lineage processes are proving essential for modern organizations.

Data engineering best practices are bordering on essential. That said, it's important to understand how data contracts make these best practices even better.