Databricks Certified Data Engineer Professional -Preparation

If you are interested in becoming a Certified Data Engineer Professional from Databricks, you have come to the right place! This study guide will help you with preparing for this certification exam. By the end of this <b><u>Databricks Certified Data Engineer Professional -Preparation course</u></b>, 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 With the knowledge you gain during this <b>Databricks Certified Data Engineer Professional -Preparation course</b>, you will be ready to take the certification exam. I am looking forward to meeting you!

What you'll learn:

• Learn how to model data management solutions on Databricks Lakehouse
• Build data processing pipelines using the Spark and Delta Lake APIs
• Understand how to use and the benefits of using the Databricks platform and its tools
• Build production pipelines using best practices around security and governance
• Learn how to monitor and log production jobs
• Follow best practices for deploying code on Databricks