Data Engineer Professional

Course Overview

• Duration: 16 Hours

• Format: Hands-on Course

• Level: Advanced

The Databricks Certified Data Engineer Professional credential is the pinnacle validation for data engineers on the cloud data landscape. This certification proves your advanced mastery in building, optimizing, securing, and maintaining production-grade, enterprise-scale data engineering solutions.

Unlike the associate-level exam, the Professional blueprint tests real-world architectural design trade-offs, advanced programmatic optimization, and complex DevOps automation. This course is built directly from the official Exam Guide blueprint, ensuring complete coverage of the platform’s cutting-edge capabilities—including Lakeflow Declarative Pipelines, Databricks Asset Bundles (DABs), Liquid Clustering, and Row/Column-level security.

Moving far beyond basic ETL execution, this course will prepare you to manage dependencies programmatically, write automated unit tests for distributed pipelines, build robust data purging frameworks for compliance, and systematically eliminate complex runtime bottlenecks using the Spark UI and Query Profiler. By the end of this course, you will possess both the deep theoretical insight needed to clear the 59-question proctored exam and the senior-level engineering skills required to architect enterprise systems.

What You Will Learn

Our curriculum maps perfectly to the 10 distinct domains and exact weightings tested on the official exam:

• Domain 1: Developing Code for Data Processing using Python and SQL

  • Design and implement scalable Python project structures optimized for Databricks Asset Bundles (DABs) to achieve modular CI/CD.

  • Troubleshoot and manage external third-party library installations (PyPI, local wheels, source archives).

  • Develop advanced User-Defined Functions (UDFs) utilizing Pandas/Vectorized UDF variants.

  • Build resilient batch and streaming declarative pipelines using the APPLY CHANGES APIs for complex Change Data Capture (CDC).

  • Compare Spark Structured Streaming vs. Lakeflow Declarative Pipelines to pick the optimal ETL architecture.

  • Build programmatic unit tests using assertDataFrameEqual, assertSchemaEqual, and DataFrame.transform frameworks.

• Domain 2: Data Ingestion & Acquisition

  • Ingest a wide variety of structured and semi-structured formats (Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text, and Binary) from cloud storage and message buses.

  • Implement append-only, high-throughput ingestion pipelines utilizing advanced Auto Loader configurations.

• Domain 3: Data Transformation, Cleansing, and Quality

  • Write highly efficient PySpark and Spark SQL logic executing complex window functions, advanced multi-way joins, and aggregations.

  • Construct production-grade quarantining processes to isolate and remediate corrupt or "bad" data within streaming and batch flows.

• Domain 4: Data Sharing and Federation

  • Configure secure data sharing utilizing Delta Sharing for Databricks-to-Databricks (D2D) and Databricks-to-Open (D2O) protocols.

  • Deploy Lakehouse Federation to govern and query data in-place across disparate enterprise database systems.

• Domain 5: Monitoring and Alerting

  • Harness Databricks System Tables for observability into platform resource utilization, billing, auditing, and workload health.

  • Deep-dive into Lakeflow pipeline Event Logs and the REST API/CLI to programmatically monitor active jobs.

  • Design advanced SQL Alerts and Workflows UI notifications to capture data quality deviations and job failures instantly.

• Domain 6: Cost & Performance Optimisation

  • Understand how Unity Catalog managed tables drastically reduce operational data layout overhead.

  • Leverage sophisticated delta optimization patterns including Deletion Vectors, Liquid Clustering, Data Skipping, and File Pruning.

  • Apply Change Data Feed (CDF) mechanics to optimize low-latency consumers and bypass streaming table limitations.

• Domain 7: Ensuring Data Security and Compliance

  • Secure platform workspace objects using precise Access Control Lists (ACLs) to strictly enforce the principle of least privilege.

  • Implement Row Filters and Column Masks to filter and anonymize sensitive tabular data natively.

  • Architect production pipelines that support anonymization, pseudonymization (hashing, tokenization), PII detection, and programmatic right-to-be-forgotten data purging rules.

• Domain 8: Data Governance

  • Standardize enterprise-level metadata, tags, and asset descriptions to maximize corporate data discoverability.

  • Deeply master the Unity Catalog permission inheritance model across catalogs, schemas, and assets.

• Domain 9: Debugging and Deploying

  • Diagnose complex errors using Spark UI diagnostic metrics, cluster driver/worker logs, and raw query profiles.

  • Resolve failed production workloads gracefully using programmatic job repairs and runtime parameter overrides.

  • Deploy version-controlled resources via Git Folders and production-grade CI/CD pipelines.

• Domain 10: Data Modelling

  • Design highly scalable data lakehouse models using Delta Lake.

  • Analyze the architectural benefits of Liquid Clustering over legacy manual partitioning and Z-Order strategies to minimize file management overhead.

What’s Included in This Course

• Advanced Code-Along Labs: Step-by-step technical builds including writing Spark unit tests, configuring a DAB pipeline, setting up row-level filtering, and writing Pandas UDFs.

• Professional Mock Exams: Realistic practice questions built to mirror the complex, scenario-based structure and difficulty of the real Professional exam.

• Architecture: Reference diagrams mapping out multi-task Lakeflow workflows, delta optimization mechanics, and security compliance trees.

Who This Course Is For

• Senior Data Engineers & Solution Architects who want to formalize their expert-level platform skills with a top-tier certification.

• Certified Databricks Associate Data Engineers ready to progress to advanced, platform-wide optimization, automation, and infrastructure-as-code deployment.

• Enterprise Consultants looking to prove their competency in deploying complex compliance frameworks, data federation, and secure cross-company data sharing.

Course Requirements

• Strong proficiency in Python/PySpark and intermediate-to-advanced SQL (joins, windows, subqueries).

• Familiarity with standard Databricks abstractions (such as the basic Medallion Architecture and foundational Spark configuration concepts).

• Access to a Databricks Workspace (or AWS/Azure/GCP Databricks Free Trial account) to perform the advanced labs.

Disclaimer

This course is an independent preparation resource designed to help students pass the certification exam. The course creator is not affiliated with, sponsored by, or endorsed by Databricks, Inc. Databricks®, Delta Lake®, Lakeflow®, and Unity Catalog® are registered trademarks of Databricks, Inc.