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Databricks Data Engineer Professional Certification Course

Databricks Data Engineer Professional Certification Course

Take your data engineering skills to the next level with this comprehensive course focused on mastering advanced techniques on the Databricks Lakehouse Platform. Learn how to build, automate, secure, and optimize complex data pipelines that scale with enterprise requirements.

Join live sessions led by experienced Databricks-certified engineers and access a structured curriculum for the Databricks Certified Data Engineer Professional exam.

Course Highlights

Live, interactive sessions guided by Databricks certification holders and industry professionals Numerous hands-on projects and practice quizzes grounded in practical challenges Instruction covering the entire professional exam scope including focused exam strategies Access to specialized communities and targeted technical discussion forums

Course Overview

This program delivers advanced training for data engineers seeking to manage and optimize production-grade pipelines on Databricks. The curriculum addresses advanced Spark features, Delta Lake management, MLflow for model tracking, automation with APIs and CLI, and robust governance techniques. Through a blend of instructor-led sessions, detailed project work, and certification preparation, participants will acquire the capacity to handle demanding data workflows and validate mastery via the professional exam.

What You’ll Learn

The course comprises eight targeted modules designed for depth and practical use:

    • Navigating Databricks Tools and Automation : Mastery of workspace features, command-line interfaces, APIs, and deployment automation
    • High-Speed Data Processing : Optimization strategies for both streaming and batch analytics using advanced Spark capabilities
    • Deep Dive into Delta Lake : Management of data versioning, schema evolution, and change data capture for reliable workflows
    • Advanced Data Modeling : Development of high-concurrency, performance-driven schemas tailored for complex analytic and machine learning scenarios
    • Security & Compliance Essentials : Implementation of Unity Catalog, data masking, granular access controls, encryption, and compliance for regulated environments
    • Monitoring and Observability : Establishment of comprehensive logging, monitoring, and alerting frameworks to ensure pipeline reliability
    • Testing and Continuous Delivery : Creation of automated testing regimes and CI/CD pipelines supporting reproducible data operations
    • Capstone Project: Build and deploy a complete production-grade pipeline consolidating all key skills learned throughout the course.

    • Mastery of Spark streaming and batch process optimization and troubleshooting
    • Advanced administration of Delta Lake features, including schema enforcement, time travel, and CDC
    • End-to-end expertise in access controls, data masking, and compliance auditing
    • Design and maintenance of high-visibility monitoring and alerting systems
    • Automation of deployment and lifecycle management for data pipelines with CI/CD integration
    • Delivery of scalable, robust, and business-aligned data engineering solutions
    • Strategic preparation for certification through scenario-based learning and simulation

    • Design and architect data solutions on the Lakehouse platform for throughput, reliability, and resilience
    • Build and refine advanced streaming and batch pipelines for production environments
    • Lead the enforcement of enterprise security and compliance within the data ecosystem
    • Automate deployments for streamlined updates and minimal disruptions
    • Monitor pipeline integrity continuously, identifying and addressing issues before impacting operations
    • Collaborate with multidisciplinary teams to ensure seamless analytics and machine learning integration
    • Develop technical documentation and champion best practices across implemented projects

    • Careers This Certification Opens Doors To

      Senior Databricks Data Engineer
      Lakehouse Solution Architect
      Data Engineering Manager
      Platform Reliability Engineer with data specialization
      Data Security and Governance Specialist
      DevOps Engineer focusing on data workflows

      Core Focus Areas

    • Spark Structured Streaming & Batch Processing
      Delta Lake Data Modeling & Management
      Security, Access Control, and Compliance Frameworks
      Monitoring, Logging, and Alerting
      Automation with CLI, APIs, and CI/CD
      Comprehensive Testing Strategies
      Real-world Pipeline Implementation

      • Sample Projects You’ll Complete

    • Construction of real-time streaming analytics pipelines optimized for low latency and scalability
    • Deployment of enterprise-level security measures, incorporating access control and audit logging
    • Development of integrated dashboards for monitoring and automated alerting
    • Incorporation of MLflow for effective machine learning workflow management
    • Automation of data flows with robust CICD pipelines including error handling and rollback procedures
    • Delivery of comprehensive compliance automation, including sensitive data masking
    • Execution of a capstone project simulating complex, enterprise-class data engineering challenges

    Career Support Included

    Dedicated instruction on technical communication, solution documentation, and platform best practices

    Access to ongoing peer collaborations, Databricks knowledge-sharing sessions, and industry workshops

    Engagement in specialized analytics forums and continuous skill development programs

    About the Certification

    The Databricks Certified Data Engineer Professional credential confirms advanced capability in designing, building, and managing production-scale data pipelines on the Lakehouse platform.

    Recognized across modern analytics environments, the certification illustrates expertise in handling complex challenges, ensuring robust performance, and driving quality and compliance in data engineering projects.

FAQ

The professional program covers more advanced technical challenges including large-scale data management, production troubleshooting, extensive automation, and enterprise compliance. Assessment centers on practical, hands-on ability to deploy and optimize complex solutions.

This program is structured for participants with practical grounding in data engineering or Spark; foundational knowledge is advised before enrollment to enable full engagement with the course material.

Practical familiarity with Databricks or Spark-based data workflows supports effective application of advanced topics and engagement with in-depth project work.

Certification affirms advanced capacity in Databricks data engineering and facilitates advancement in complex and large-scale technical environments, with an emphasis on quality, reliability, and innovation in data projects.
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