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Advanced Data Engineering with Databricks

In this course, participants will build upon their existing knowledge of Apache Spark, Delta Lake, and Delta Live Tables to unlock the full potential of the data lakehouse by utilizing the suite of tools provided by Databricks. This course places a heavy emphasis on designs favoring incremental data processing, enabling systems optimized to continuously ingest and analyze ever-growing data. By designing workloads that leverage built-in platform optimizations, data engineers can reduce the burden of code maintenance and on-call emergencies, and quickly adapt production code to new demands with minimal refactoring or downtime. The topics in this course should be mastered prior to attempting the Databricks Certified Data Engineering Professional exam.

Skill Level
  • Intermediate programming experience with PySpark
  • Extract data from a variety of file formats and data sources
  • Apply a number of common transformations to clean data
  • Reshape and manipulate complex data using advanced built-in functions
  • Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions, etc.)
  • Beginner experience configuring and scheduling data pipelines using the Delta Live Tables (DLT) UI
  • Beginner experience defining Delta Live Tables pipelines using PySpark
  • Ingest and process data using Auto Loader and PySpark syntax
  • Process Change Data Capture feeds with APPLY CHANGES INTO syntax
  • Review pipeline event logs and results to troubleshoot DLT syntax


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