Course Objectives
1. Master Advanced Data Engineering on Databricks
Learn how to design, build, and manage scalable data engineering solutions using the Databricks Lakehouse Platform.
2. Build Production-Grade Data Pipelines
Develop expertise in creating reliable batch and streaming pipelines for enterprise-level data processing.
3. Optimize Big Data Processing with Spark
Understand advanced Apache Spark concepts including performance tuning, partitioning, caching, and execution optimization.
4. Work with Delta Lake in Production
Learn how to use Delta Lake for ACID transactions, data versioning, schema enforcement, and reliable data storage.
5. Implement End-to-End Data Architectures
Design modern Lakehouse architectures integrating data ingestion, transformation, storage, and analytics.
6. Prepare for Databricks Professional Certification Exam
Gain exam-focused knowledge, real-world scenarios, and hands-on skills required to pass the certification successfully.
Popular Techniques Covered in This Course
1. Advanced Apache Spark Development
2. Delta Lake Engineering
3. Data Pipeline Architecture
4. Spark Performance Optimization
5. Structured Streaming
6. Databricks Workspace Administration
7. Data Modeling for Analytics
8. Workflow Automation
9. Data Quality Management
10. Cloud Data Engineering Best Practices
Get Hands-on Knowledge about Real-Time Projects
Project 1
1. Enterprise Lakehouse Architecture Implementation
Description: Build a complete Lakehouse system using Databricks and Delta Lake to unify structured and unstructured data for analytics and reporting.
Project 2
2. Real-Time Streaming Analytics Platform
Description: Design a streaming pipeline that processes live data events and generates real-time dashboards for business insights.
Project 3
3. Large-Scale ETL Optimization Project
Description: Optimize existing data pipelines by improving Spark performance, reducing runtime, and enhancing resource utilization.
Project 4
4. Customer 360 Data Platform
Description: Create a unified customer analytics platform by integrating multiple data sources into a single Delta Lake architecture.
Project 5
5. Production Data Pipeline Monitoring System
Description: Develop monitoring and alerting systems to track pipeline failures, performance issues, and data quality problems.
Key Features
Edubrights offers Databricks Professional Data Engineer Certification Training in virtual mode with expert trainers. Here are the key features,
40 Hours Course Duration
100% Job Oriented Training
Industry Expert Faculties
Free Demo Class Available
Completed 500+ Batches
Certification Guidance
Curriculum
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
Receive Training From Our Skilled and Effective Trainers
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.
Databricks Certified Professional Data Engineer
Our institution offers a recognized Databricks Professional Data Engineer certification. This certification enhances your portfolio and prepares you for collaborative projects in real-world environments. Gain practical skills through hands-on training and assessments.
Course FAQs
1. What is the Databricks Professional Data Engineer Certification?
It is an advanced certification that validates expertise in designing and managing large-scale data engineering solutions using Databricks.
2. Who should take this certification?
Experienced Data Engineers, Cloud Engineers, Big Data Developers, and professionals working with Databricks or Spark.
3. What are the prerequisites?
Strong knowledge of SQL, PySpark, Apache Spark, and basic cloud data engineering concepts.
4. What will I learn in this course?
You will learn advanced Spark, Delta Lake, streaming pipelines, performance tuning, and Lakehouse architecture.
5. Is this certification difficult?
Yes. It is a professional-level certification requiring strong practical and theoretical knowledge.
6. What is Delta Lake?
Delta Lake is a storage layer that provides reliability, ACID transactions, and version control for data lakes.
7. What is a Lakehouse architecture?
It combines the flexibility of data lakes with the structure and performance of data warehouses.
8. Does this certification include streaming data?
Yes. Structured streaming and real-time data processing are key topics.
9. What tools are used in Databricks?
Apache Spark, Delta Lake, SQL, PySpark, Databricks Jobs, and notebooks.
10. Are hands-on projects included?
Yes. The course includes real-world enterprise data engineering projects.
11. What industries use Databricks professionals?
Finance, healthcare, retail, telecom, e-commerce, and technology sectors.
12. What job roles can I apply for?
Senior Data Engineer, Cloud Data Engineer, Big Data Engineer, Analytics Engineer, and Data Architect.
13. Is Databricks in demand?
Yes. It is widely adopted for modern cloud data engineering and analytics.
14. Does this help in career growth?
Yes. It supports senior-level data engineering and architecture roles.
15. Does this course help pass the certification exam?
Yes. It is designed to align with the Databricks Professional certification objectives.
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