When a team fails with big data, it’s easy to blame the technology—and that’s exactly what usually happens. It’s far more difficult to look inward to determine the real reasons it failed, which are more often than not an artifact of the team itself.
Early and mid-level failures start well before the first line of code is written. They start with the formation of the team. A data engineering team should be multidisciplinary. It should have the appropriate and required skills before starting the project.
Jesse Anderson outlines five of the most common non-technology reasons why data engineering teams fail:
Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. Jesse trains employees on big data—including cutting-edge technology like Apache Kafka, Apache Hadoop, and Apache Spark. He’s taught thousands of students at companies ranging from startups to Fortune 100 companies the skills to become data engineers. He’s widely regarded as an expert in the field and recognized for his novel teaching practices. Jesse is published by O’Reilly and Pragmatic Programmers and has been covered in such prestigious media outlets as the Wall Street Journal, CNN, BBC, NPR, Engadget, and Wired. You can learn more about Jesse at Jesse-Anderson.com.
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