Databricks Professional Data Engineer Certification

Module 1: Professional Certification Deep Dive

• Professional exam vs Associate: complexity shift from recall to architectural design
• Exam domains: advanced Delta Lake, pipeline optimisation, and data modelling
• Prerequisite audit: essential knowledge required before starting this course
• Study strategy for working engineers: 30-day intensive preparation schedule

Module 2: Advanced Delta Lake Architecture

• Delta transaction log internals: JSON and Parquet checkpoint file mechanics
• Table optimisation: OPTIMIZE frequency, Z-ORDER column selection strategy
• Concurrency control: how Delta handles simultaneous reads and writes
• Professional scenario: designing a Delta architecture for high-frequency NSE trading data

Module 3: Advanced Streaming Pipelines

• Watermark strategies: late data handling for Indian payment processing pipelines
• Stateful streaming: aggregating session data across unbounded event streams
• Multi-stream joins: correlating events from Kafka and Delta change data feeds
• Professional scenario: exactly-once guarantee design for a banking settlement system

Module 4: Spark Internals & Performance

• Catalyst optimizer: understanding query plan transformations end-to-end
• Tungsten execution engine: whole-stage code generation and memory efficiency
• Data skew remediation: advanced techniques beyond basic repartitioning
• Professional drill: optimising a slow pipeline from Spark UI evidence alone

Module 5: Advanced DLT & Pipeline Patterns

• DLT expectations: WARN vs DROP vs FAIL and monitoring quarantined records
• Change Data Capture with DLT: APPLY CHANGES INTO for SCD Type 1 and 2
• Pipeline parametrisation: configuration-driven DLT for multi-tenant deployments
• Professional scenario: designing a DLT pipeline with complex CDC requirements

Module 6: Security & Governance (Professional Depth)

• Unity Catalog advanced: privilege inheritance, denies, and policy combinations
• Network architecture: VPC design for Databricks in regulated Indian environments
• Secrets management: scopes, ACLs, and rotation patterns for production pipelines
• Professional scenario: least-privilege architecture for a healthcare data platform

Module 7: Professional Scenario Practice

• Scenario set: 20 Professional Data Engineer questions -- architecture and design focus
• Dissecting wrong answers: understanding why distractors are designed to mislead
• Time management: approaching 3-paragraph scenario questions efficiently
• Peer review: defending architecture decisions in group scenario discussions

Module 8: Full Mock Exam & Certification Strategy

• 60-question timed mock mirroring Professional exam length and difficulty
• Score analysis: identifying the delta between current score and passing threshold
• Targeted revision: domain-specific reading list and practice labs for weak areas
• Certification maintenance: renewal requirements and continuing education in

Experience in the Industry Gain expertise from Azure-certified data engineers who have built cloud data platforms using Azure Databricks, ADLS Gen2, Azure Data Factory, and Azure Synapse for enterprises on the Microsoft Azure ecosystem.

Backgrounds at the Top Our Azure Databricks trainers have delivered cloud data engineering projects at financial institutions, retail companies, and healthcare organisations leveraging the full Microsoft Azure data platform stack.

Clear & Effective Teaching Azure Databricks workspace setup, ADLS Gen2 integration, Delta Lake, ADF orchestration, Structured Streaming, Azure ML integration, and Unity Catalog governance are taught with real Azure data platform examples.

Hands-On Learning Focus Students build Azure data pipelines, work with Delta Lake on ADLS Gen2, orchestrate notebooks with ADF, stream from Event Hubs, and configure Unity Catalog through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest Azure Databricks releases, Microsoft Fabric integration considerations, Unity Catalog on Azure enhancements, and evolving Azure data platform best practices.