Databricks Certified Data Engineer Professional -Preparation

By the end of this course, you should be able to:

• Model data management solutions, including:
  • Lakehouse (bronze/silver/gold architecture, tables, views, and the physical layout)
  • General data modeling concepts (constraints, lookup tables, slowly changing dimensions)
• Build data processing pipelines using the Spark and Delta Lake APIs, including:
  • Building batch-processed ETL pipelines
  • Building incrementally processed ETL pipelines
  • Deduplicating data
  • Using Change Data Capture (CDC) to propagate changes
  • Optimizing workloads
• Understand how to use and the benefits of using the Databricks platform and its tools, including:
  • Databricks CLI (deploying notebook-based workflows)
  • Databricks REST API (configure and trigger production pipelines)
• Build production pipelines using best practices around security and governance, including:
  • Managing clusters and jobs permissions with ACLs
  • Creating row- and column-oriented dynamic views to control user/group access
  • Securely delete data as requested according to GDPR & CCPA
• Configure alerting and storage to monitor and log production jobs, including:
  • Recording logged metrics
  • Debugging errors
• Follow best practices for managing, testing and deploying code, including:
  • Relative imports
  • Scheduling Jobs
  • Orchestration Jobs