Data Engineering Best Practices

Analytics, machine learning, and artificial intelligence have led to a surge in data, systems, and users. To navigate this field effectively, data engineers should follow best practices to ensure efficiency and avoid reinventing the wheel. The top six best practices include designing efficient and scalable pipelines, being mindful of resource-heavy tasks, automating pipelines, ensuring reliability, embracing DataOps, and focusing on business value. Efficient tools and techniques, avoiding managing connectors, and identifying bottlenecks are key components of successful pipeline design. It's important to isolate "heavy lifting" tasks, use appropriate pipeline methods, and build a single source of truth for data.

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