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Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.

Reference: https://www.databricks.com/learn/certification/data-engineer-professional

Databricks Certified Professional Data Engineer exam is a comprehensive certification exam that assesses an individual's knowledge and skills in working with big data and cloud computing technologies. Databricks-Certified-Professional-Data-Engineer exam is designed for data professionals who are proficient in using Databricks Unified Analytics Platform for managing and analyzing large volumes of data. Databricks-Certified-Professional-Data-Engineer exam covers a broad range of topics such as data engineering, data transformation, data modeling, and machine learning.

Databricks Certified Professional Data Engineer exam covers a wide range of topics, such as data ingestion, transformation, storage, and processing. Databricks-Certified-Professional-Data-Engineer exam tests the candidates' ability to use Databricks tools and technologies to solve real-world problems and challenges. Candidates who pass Databricks-Certified-Professional-Data-Engineer exam demonstrate their proficiency in designing, building, and managing data pipelines with Databricks, which is a leading cloud-based platform for big data processing and analytics.

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion15-20%- Streaming ingestion
  • 1. Structured streaming fundamentals
  • 2. Kafka integration
- Batch ingestion methods
  • 1. Integration with external systems
  • 2. Spark APIs for ingestion
  • 3. DBR autoloader
Topic 2: Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Spark APIs in Python
  • 2. Built-in and user-defined functions
  • 3. Performance optimization techniques
- Spark DataFrames and Spark SQL
  • 1. DataFrame operations and transformations
  • 2. Window functions
  • 3. Spark SQL queries and functions
Topic 3: Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Data governance fundamentals
  • 2. Bronze, silver, gold data layers
  • 3. Differences between data lake, data warehouse, and lakehouse
Topic 4: Delta Lake20-25%- Delta Lake operations
  • 1. Schema evolution and enforcement
  • 2. Delta Live Tables
  • 3. Merge, update, delete operations
- Delta Lake fundamentals
  • 1. Time travel and data versioning
  • 2. ACID transactions
  • 3. Optimize and Z-order
Topic 5: Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Task dependencies and orchestration
  • 2. Jobs and job scheduling
  • 3. Monitoring and alerting

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