Spark, Hadoop, and Snowflake for Data Engineering

Duke surveys several platforms used for data processing and operations. PySpark and Snowflake connect with Databricks and MLflow before the outline turns to DataOps. It is a platform-oriented continuation for learners who already have the programming and deployment foundations needed to follow the work.

What you will learn

• Compare scalable data-processing platforms.
• Connect PySpark and Databricks workflows.
• Explain operational approaches for data and model pipelines.

Who this suits

Learners with Python, Git, Docker and Kubernetes experience, following the stated prerequisites.

Before you enroll

Check the software versions, cloud-account requirements and lab access terms before enrolling. Running cloud resources can incur separate charges. Use fictional or public sample data, and evaluate reliability, permissions and data quality alongside whether a pipeline runs.

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Put it into practice

Compare two fictional processing architectures and document how each would handle scaling, versioned changes and operational monitoring.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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