Data engineering integrates discrete, diverse data systems together so end systems can derive value. At a high level, data engineering is moving data from one location to another. The health of a data information system is subject to the rigidity and health of the data engineering process. Through complementary software tools and Python code, the engineering process can be optimized to promote reliability, simplicity, scalability, and efficiency.

Watch this webinar to learn how Avaap Data & Analytics leaders Erin Sheehan and Emily Suchan built a replicable framework for data ingestion activities that automate scheduling capabilities and provide a reliable collection of ingestion metrics.

  • Understand why a Python-based framework allows for flexibility and customization
  • Learn how this framework is valuable in different scenarios
  • Be able to move beyond simple ingestion tasks and focus on the optimization of workflows

Beyond Pipelines: How a Reliable and Scalable Ingestion Framework Elevates the Data Engineering Process

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