4 data engineering pitfalls and how to avoid them

Best practices for boosting data engineering productivity

As data organizations embark on their modernization journey, there are significant challenges that can derail data engineering efforts and impact desired outcomes.

This ebook highlights the most common issues data engineering teams face trying to operationalize data for analytics, and offers practical tips and best practices on how to avoid them.

Read this ebook to learn:

• 4 common data engineering pitfalls and how they can impact the business
• Why a lakehouse architecture is the ideal foundation for a modern data organization
• How a low-code approach paired with engineering best practice can accelerate innovation