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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) exam is a certification program designed to validate the skills and expertise of data engineers in developing and managing big data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer exam is ideal for data engineers, ETL developers, and data architects who work with Databricks and want to showcase their skills and proficiency.
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Databricks Certified Professional Data Engineer certification is designed for data engineers who work with the Databricks platform and have a deep understanding of data engineering concepts. Databricks Certified Professional Data Engineer Exam certification exam tests the candidate’s ability to design, build, and maintain data pipelines using Databricks, as well as their knowledge of data modeling, data warehousing, and data governance. Databricks Certified Professional Data Engineer Exam certification is recognized globally and indicates that the candidate has the skills and expertise needed to work with Databricks.
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Databricks Certified Professional Data Engineer exam measures a candidate's ability to design, build, and manage data pipelines using Databricks. It covers a wide range of topics, including data ingestion, transformation, storage, and analysis. Candidates must demonstrate their proficiency in using Databricks tools and techniques to solve real-world data engineering problems. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers who want to validate their skills and expertise in using Databricks to build and manage data pipelines.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Production Pipelines and Orchestration | - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling |
| Topic 2: Data Modeling and Storage | - Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures |
| Topic 3: Data Ingestion and Transformation | - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs |
| Topic 4: Security, Governance, Monitoring, and Optimization | - Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control |
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