Advanced Certification in Data Engineering with GenAI



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Why enroll for Advanced Certification in Data Engineering with GenAI?



The Future of AI Depends on the Engineers who Build its Data Foundation




Why Advanced Certification in Data Engineering with GenAI from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Advanced Certification in Data Engineering with GenAI
Skills Covered
Lakehouse Architecture Unity Catalog Governance Batch & Streaming Ingestion CI/CD for Data Pipelines Azure Databricks Administration GenAI & Agentic Data Engineering
Tools Covered
Advanced Certification in Data Engineering with GenAI Course Curriculum
Curriculum Designed by Experts
Data Engineering Foundations and the Modern Data Landscape
Topics
- Data Warehouse Evolution
- Lakehouse Architecture
- Data Intelligence Platform
- DE Lifecycle Stages
- ETL vs ELT
- Batch vs Streaming
- Spark & Delta
- Databricks Ecosystem
- Multi-Cloud Positioning
- Data Roles Compared
- AI-Era Data Platforms
![Hands On Experience skill]()
Hands-on
- Building Databricks Workspace
- Comparing SQL Notebooks
![skill you will learn skill]()
Skills
- Lakehouse Concepts
- Databricks Platform
- ETL vs ELT
- Data Architecture
Python for Data Engineering
Topics
- Python Refresher
- Comprehensions & Generators
- Decorators & Functions
- File Handling Formats
- REST API Consumption
- Pagination & Auth
- Virtual Environments
- Packaging Basics
- Exception Handling
- Pipeline Logging
- Pandas Wrangling
- Pytest Basics
![Hands On Experience skill]()
Hands-on
- Building REST API Pipeline
- Testing and Logging Pipelines
![skill you will learn skill]()
Skills
- Python
- REST APIs
- Pandas
- pytest
Advanced SQL for Data Engineering
Topics
- Window Functions
- CTEs & Recursion
- PIVOT/UNPIVOT
- Advanced Joins
- Correlated Subqueries
- DDL Fundamentals
- MERGE & Upserts
- Query Optimization
- Star Schema Modeling
- SCD Type 1/2
- Spark SQL Differences
![Hands On Experience skill]()
Hands-on
- Implementing SCD Type 2
- Designing Star Schema
- Optimizing SQL Queries
![skill you will learn skill]()
Skills
- Window Functions
- MERGE/Upserts
- Star Schema
- Query Optimization
Apache Spark Architecture & PySpark Fundamentals
Topics
- Spark Architecture
- Driver & Executors
- DAG Scheduler
- RDDs vs DataFrames
- Transformations vs Actions
- Lazy Evaluation
- PySpark DataFrame API
- UDFs & Joins
- Reading/Writing Formats
- Partitioning Basics
- Catalyst & Tungsten
![Hands On Experience skill]()
Hands-on
- Analyzing PySpark Jobs
![skill you will learn skill]()
Skills
- Spark Architecture
- PySpark DataFrame API
- Lazy Evaluation
- Partitioning
Databricks Lakehouse Platform & Compute Fundamentals
Topics
- Control-Data Plane
- Workspace Architecture
- Unity Catalog Overview
- Compute Types
- SQL Warehouses
- Cluster Configuration
- Notebook Features
- Git Folders
- CLI & API
- Compute Cost Basics
![Hands On Experience skill]()
Hands-on
- Comparing Databricks Compute
- Automating Git CLI Workflows
![skill you will learn skill]()
Skills
- Databricks Workspace
- Compute Types
- SQL Warehouses
- Git Folders
Spark SQL, Performance & Troubleshooting
Topics
- Spark SQL Interop
- Join Strategies
- Repartition vs Coalesce
- Caching Strategies
- Spark UI Basics
- DAG Visualization
- Skew & Spill
- Shuffle Bottlenecks
- Adaptive Query Execution
![Hands On Experience skill]()
Hands-on
- Optimizing Skewed Joins
- Benchmarking AQE Performance
![skill you will learn skill]()
Skills
- Spark UI
- Join Strategies
- Skew/Shuffle Diagnosis
- Adaptive Query Execution
Delta Lake Deep Dive
Topics
- Delta Transaction Log
- ACID on Lakehouse
- Schema Enforcement
- Schema Evolution
- Time Travel
- OPTIMIZE & Z-Order
- Liquid Clustering
- VACUUM & Retention
- Deletion Vectors
- Table Constraints
- Deep/Shallow Clone
![Hands On Experience skill]()
Hands-on
- Managing Delta Schema Evolution
- Restoring Delta Table Versions
- Benchmarking Liquid Clustering
![skill you will learn skill]()
Skills
- Delta Lake
- Time Travel
- OPTIMIZE/Z-Order
- Liquid Clustering
Data Ingestion Patterns — Batch
Topics
- Full/Incremental Load
- COPY INTO Basics
- Idempotent Ingestion
- Auto Loader
- Schema Inference/Evolution
- Rescued Data Column
- Lakeflow Connect
- File Format Choices
- Checkpointing Design
![Hands On Experience skill]()
Hands-on
- Building Auto Loader Pipeline
- Performing Historical Data Backfill
![skill you will learn skill]()
Skills
- Auto Loader
- COPY INTO
- Lakeflow Connect
- Idempotent Ingestion
Data Ingestion Patterns — Streaming & CDC
Topics
- Structured Streaming Basics
- Sources, Sinks, Triggers
- Windowing & Watermarking
- Stateful Aggregations
- Change Data Feed
- CDC Merge Patterns
- Exactly-Once Semantics
- Kafka/Event Hubs
![Hands On Experience skill]()
Hands-on
- Building Streaming CDC Pipeline
- Consuming Change Data Feed
![skill you will learn skill]()
Skills
- Structured Streaming
- Change Data Feed
- CDC Merge Patterns
- Watermarking
Medallion Architecture & Lakehouse Data Modeling
Topics
- Medallion Layers
- Data Contracts
- Dimensional Modeling
- SCD via MERGE
- Data Vault Concepts
- Star Schema Comparison
- Partitioning Strategy
- Dual-Consumption Design
- Schema Governance
![Hands On Experience skill]()
Hands-on
- Building Medallion Data Pipeline
![skill you will learn skill]()
Skills
- Medallion Architecture
- Dimensional Modeling
- SCD via MERGE
- Data Vault
Lakeflow Declarative Pipelines & Data Quality
Topics
- Declarative Pipelines
- Streaming Tables/MVs
- Pipeline DAG Design
- Data Quality Expectations
- Dev/Prod Modes
- Event Logs/Lineage
- Lakehouse Monitoring
- DQX/GE Patterns
![Hands On Experience skill]()
Hands-on
- Building Lakeflow Quality Pipeline
![skill you will learn skill]()
Skills
- Lakeflow Declarative Pipelines
- Expectations
- Lakehouse Monitoring
- Data Quality
Orchestration with Lakeflow Jobs & Databricks Workflows
Topics
- Lakeflow Jobs Overview
- Task Types
- dbt Job Task
- Task Dependencies
- Conditional Execution
- Job/Shared Clusters
- Parameters & Widgets
- Job Scheduling
- Retries & Alerting
- Run Monitoring
- Airflow Integration
![Hands On Experience skill]()
Hands-on
- Orchestrating Lakeflow Workflows
![skill you will learn skill]()
Skills
- Lakeflow Jobs
- Task Orchestration
- Scheduling & Alerting
- Airflow Integration
Unity Catalog — Governance, Security & Lineage
Topics
- UC Metastore Hierarchy
- Managed/External Tables
- GRANT/REVOKE Access
- Row-Level Security
- Column Masking
- Tags & ABAC
- Data Lineage
- Audit Logging
- PII Classification
- Compliance Patterns
- Metric Views
- Delta Sharing
- System Tables
![Hands On Experience skill]()
Hands-on
- Implementing Unity Catalog Governance
![skill you will learn skill]()
Skills
- Unity Catalog
- Row/Column Security
- PII Classification
- Metric Views
CI/CD, DevOps & Databricks Asset Bundles
Topics
- Git Branching Strategy
- CLI Deep Dive
- Asset Bundles (DABs)
- Environment Targets
- CI/CD Integration
- PR Review Workflows
- PySpark Unit Testing
- CI Pattern Comparison
- Environment Promotion
- Secrets Management
![Hands On Experience skill]()
Hands-on
- Deploying Databricks Asset Bundles
![skill you will learn skill]()
Skills
- Databricks Asset Bundles
- CI/CD
- GitHub Actions
- PySpark Testing
Performance Tuning, Monitoring & Cost Optimization
Topics
- File Sizing
- Predictive I/O
- Photon Engine
- Lakehouse Monitoring
- System Tables (Billing)
- Cost Optimization
- Autoscaling Policies
- Query Optimization Revisit
- Cost Dashboards
![Hands On Experience skill]()
Hands-on
- Optimizing Databricks Platform Costs
![skill you will learn skill]()
Skills
- Performance Tuning
- System Tables
- Cost Optimization
- FinOps
Azure Databricks — Environment, Networking & Security
Topics
- Workspace Deployment
- Pricing Tiers
- VNet/Private Link
- Network Security Groups
- Entra ID Integration
- Secret Scopes
- UC on Azure
- Azure Monitor
![Hands On Experience skill]()
Hands-on
- Securing Azure Databricks Workspace
![skill you will learn skill]()
Skills
- Azure Databricks
- Networking & Security
- Entra ID
- Key Vault
Azure Data Integration — ADF, ADLS Gen2 & Monitoring
Topics
- ADLS Gen2 Basics
- External Locations
- Azure Data Factory
- ADF/Lakeflow Jobs
- Log Analytics
- Cross-Platform Cost Mgmt
- Disaster Recovery
![Hands On Experience skill]()
Hands-on
- Integrating ADF with Databricks
![skill you will learn skill]()
Skills
- ADLS Gen2
- Azure Data Factory
- Log Analytics
- Hybrid Orchestration
Generative AI & LLM Foundations for Data Engineers
Topics
- GenAI Landscape
- Foundation Models
- Prompt Engineering Basics
- Mosaic AI APIs
- Unstructured Data Ingestion
- Chunking Strategies
- Embeddings Overview
- MLflow for GenAI
- AI-Ready Feature Eng
- Responsible AI Practices
![Hands On Experience skill]()
Hands-on
- Processing Documents with GenAI
![skill you will learn skill]()
Skills
- Foundation Models
- Mosaic AI
- Chunking & Embeddings
- MLflow
Context Engineering for AI Data Pipelines & Agents
Topics
- Context vs Prompting
- Context Pillars
- Context Offloading
- Compression Techniques
- Context Isolation
- Agent Memory Patterns
- MCP Architecture
- MCP Servers/Clients
- Unity AI Gateway
- Context Rot
- Prompt Injection Risks
![Hands On Experience skill]()
Hands-on
- Building Governed MCP Server
![skill you will learn skill]()
Skills
- Context Engineering
- MCP
- Unity AI Gateway
- Agent Memory
Semantic Layer & Knowledge Engineering for AI
Topics
- Catalog as Decision-Maker
- Business Semantics Layer
- Glossary & Domains
- Genie Ontology
- Knowledge Graphs
- Entity Resolution
- Cross-Tool Semantics
- Semantic Asset Governance
![Hands On Experience skill]()
Hands-on
- Building Governed Semantic Layer
![skill you will learn skill]()
Skills
- Semantic Layer
- Business Glossary
- Knowledge Graphs
- Genie Ontology
Building RAG & Vector Search Pipelines for Enterprise Data
Topics
- RAG Architecture
- Vector Search Index
- Embedding Pipeline Design
- Chunking for Retrieval
- Vector Index Governance
- RAG Evaluation
- RAG App Build
![Hands On Experience skill]()
Hands-on
- Building Enterprise RAG Pipeline
![skill you will learn skill]()
Skills
- RAG Architecture
- Vector Search
- Embedding Pipelines
- RAG Evaluation
Agentic AI for Data Engineering
Topics
- Agentic AI Concepts
- Mosaic Agent Framework
- Agent Bricks
- Agent Use Cases
- Genie AI/BI Spaces
- LLMOps Basics
- AI Gateway Governance
- Token-Based FinOps
- DE Role Evolution
![Hands On Experience skill]()
Hands-on
- Configuring Governed Genie Space
- Building Agentic Incident Summarizer
![skill you will learn skill]()
Skills
- Agentic AI
- Mosaic Agent Framework
- Genie/AI-BI
- Unity AI Gateway
Databricks SQL, AI/BI, Genie & dbt for Analytics Engineering (Self-paced)
Topics
- SQL Warehouse Tuning
- Dashboards & Alerts
- Genie Self-Service
- dbt Models & Sources
- dbt Tests & Docs
- dbt-Databricks Adapter
- dbt + Unity Catalog
- dbt Slim CI
- dbt vs Lakeflow
![Hands On Experience skill]()
Hands-on
- Building Genie Analytics Dashboard
- Deploying dbt on Databricks
![skill you will learn skill]()
Skills
- Databricks SQL
- Genie
- dbt Core
- dbt-Databricks Adapter
- Slim CI
Data Products, Feature Serving & Emerging Platform Capabilities(Self-paced)
Topics
- Data-as-a-Product
- Reverse ETL Patterns
- Lakebase Serving
- Feature Store Fundamentals
- Multi-Region DR
![Hands On Experience skill]()
Hands-on
- Deploying Lakebase API
- Planning Multi-Region Disaster Recovery
![skill you will learn skill]()
Skills
- Data Products
- Lakebase
- Feature Store
- Reverse ETL
- Disaster Recovery
Snowflake for Data Engineering - Part 1: Architecture & Core Engineering (Self-paced)
Topics
- Snowflake Architecture
- Object Model Basics
- COPY INTO & Snowpipe
- Semi-Structured Data (VARIANT)
- Time Travel & Cloning
- Query Performance
- Cost Model & Warehouses
- Snowflake vs Databricks
![Hands On Experience skill]()
Hands-on
- Processing JSON in Snowflake
- Benchmarking Warehouse Cost Performance
![skill you will learn skill]()
Skills
- Snowflake Architecture
- Snowpipe
- Query Profile
- Warehouse Sizing
Snowflake for Data Engineering — Part 2: Advanced Snowflake & Lakehouse Interoperability(Self-paced)
Topics
- Snowpark DataFrame API
- Dynamic Tables
- Streams & Tasks
- Iceberg Interoperability
- Secure Data Sharing
- RBAC & Data Masking
- Cortex AI Functions
- Platform Decision Framework
![Hands On Experience skill]()
Hands-on
- Building Dynamic Table Pipeline
- Querying Cross-Platform Iceberg Tables
![skill you will learn skill]()
Skills
- Snowpark
- Dynamic Tables
- Apache Iceberg
- Cortex AI
dbt for Analytics Engineering — Part 1: Fundamentals & Modeling(Self-paced)
Topics
- dbt Project Structure
- Staging/Intermediate/Marts
- Jinja Templating
- Sources & Freshness
- Generic & Singular Tests
- Materializations
- Incremental Models
- Multi-Adapter Setup
![Hands On Experience skill]()
Hands-on
- Building dbt Analytics Project
- Creating Incremental Model Snapshot
![skill you will learn skill]()
Skills
- dbt Core
- Jinja
- Materializations
- Incremental Models
dbt for Analytics Engineering — Part 2: Advanced dbt, Semantic Layer & Deployment(Self-paced)
Topics
- dbt Fusion Engine
- Reusable Packages/Macros
- dbt Semantic Layer
- Environment Promotion
- dbt Slim CI/State
- dbt Cloud vs Lakeflow Orchestration
- Testing & CI Gating
- dbt vs Lakeflow Revisited
![Hands On Experience skill]()
Hands-on
- Defining dbt Semantic Metrics
- Building Slim CI Pipeline
![skill you will learn skill]()
Skills
- dbt Fusion
- dbt Semantic Layer
- dbt Slim CI
- CI/CD for Analytics
Advanced Certification in Data Engineering Course Description
Why should learners choose the Advanced Certification in Data Engineering with GenAI?
What is covered in Edureka’s Advanced Certification in Data Engineering with GenAI?
What are the prerequisites for the Data Engineering with GenAI program?
Who should enroll in the Advanced Certification in Data Engineering with GenAI?
- Data Engineers and ETL Developers
- Analytics Engineers and BI Developers
- Cloud and Platform Engineers
- Database and SQL Professionals
- Data Architects and Technical Leads
- Professionals entering GenAI data engineering
What is the duration of the program?
What will learners achieve after completing the program?
- Design modern lakehouse architectures
- Build batch, streaming, and CDC pipelines
- Develop Bronze, Silver, and Gold data layers
- Process distributed data using Apache Spark
- Optimize Spark and Delta Lake workloads
- Build pipelines using Databricks Lakeflow
- Implement Unity Catalog governance
- Deploy secure Azure Databricks environments
- Automate deployments using CI/CD
- Build governed vector search and RAG pipelines
- Connect AI agents with enterprise data using MCP
- Monitor data quality, lineage, performance, and costs
What tools and technologies are covered?
- Python and SQL
- Apache Spark and PySpark
- Delta Lake and Auto Loader
- Databricks Lakeflow
- Unity Catalog
- Azure Databricks
- Azure Data Factory and ADLS Gen2
- Databricks Asset Bundles
- GitHub Actions
- Microsoft Entra ID and Azure Key Vault
- MLflow and Mosaic AI
- Databricks Vector Search
- Model Context Protocol
- Unity AI Gateway
- Snowflake and Snowpark
- dbt Core and dbt Fusion
How does the program combine data engineering with GenAI?
What does the Azure Databricks training cover?
What does the program cover under Databricks Lakeflow?
Is the Data Engineering with GenAI program hands-on?
What are the system requirements?
Advanced Certification in Data Engineering Projects
Advanced Data Engineering with GenAI Certification
The certification validates program completion and practical knowledge of:
- Python and advanced SQL
- Apache Spark and PySpark
- Delta Lake engineering
- Batch, streaming, and CDC pipelines
- Databricks Lakeflow
- Unity Catalog governance
- Azure Databricks administration
- CI/CD and Asset Bundles
- Vector search and RAG
- MCP and agentic data engineering
No. The Edureka Advanced Certification in Data Engineering with GenAI has lifetime validity.
Learners should continue updating their technical knowledge as Databricks, Microsoft Azure, and GenAI technologies evolve.
Yes. The curriculum covers Apache Spark, Delta Lake, data ingestion, pipeline development, orchestration, data quality, Unity Catalog, and performance optimization.
These skills can support preparation for Databricks Data Engineer certification exams. However, the program is not an official Databricks certification course and does not guarantee exam success.
Yes. The program covers Azure Databricks deployment, security, networking, identity, ingestion, monitoring, Azure Data Factory, and production pipeline management.
These topics can help learners prepare for the Microsoft DP-750 exam. However, the program is not officially aligned with or endorsed by Microsoft, and learners should review the latest official exam guide separately.
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Course FAQs
Is data engineering still a strong career choice in the GenAI era?
Yes. GenAI can automate repetitive coding and documentation tasks, but organizations still require skilled Data Engineers to build, govern, secure, monitor, and optimize enterprise data systems.
Demand is increasingly shifting toward professionals with expertise in data governance, lakehouse architecture, cloud data platforms, real-time pipelines, and AI-ready data engineering.
Is prior Databricks or Apache Spark experience required?
Is this a Databricks-only data engineering course?
The 22 live modules primarily focus on Databricks and Azure Databricks to provide structured, certification-aligned learning.
The program also includes self-paced electives covering Snowflake, Snowpark, dbt Core, dbt Semantic Layer, analytics engineering, and lakehouse interoperability.
What is the Model Context Protocol?
The Model Context Protocol, or MCP, is an open standard that connects AI applications with enterprise tools, data sources, and services.
It enables controlled access to approved schemas, tables, metrics, APIs, and business systems without exposing unrestricted backend access.
Why should Data Engineers learn MCP?
MCP enables Data Engineers to securely connect governed enterprise data with GenAI applications and AI agents.
In this program, learners build an MCP server over Unity Catalog and apply runtime controls through Unity AI Gateway.
Does the program cover Retrieval-Augmented Generation?
Yes. Learners build and evaluate RAG pipelines using governed enterprise data, vector search indexes, embeddings, chunking strategies, and foundation model endpoints.
The curriculum also explains when RAG is appropriate and when semantic layers or long-context models may provide a better solution.
Is RAG still relevant with long-context AI models?
Yes. RAG remains valuable when AI systems require current, traceable, secure, or domain-specific information.
Long-context models and RAG serve different use cases. The program teaches learners to evaluate retrieval quality, context relevance, latency, cost, and governance before selecting an architecture.
How does the program combine data engineering and Generative AI?
Why are Snowflake and dbt included as self-paced electives?
The live curriculum remains focused on Databricks and Azure Databricks to provide deeper platform and certification coverage.
Snowflake and dbt are included as self-paced electives because they are widely used alongside Databricks in modern enterprise data stacks.


