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Advanced Certification in Data Engineering with GenAI

Advanced Certification in Data Engineering with GenAI
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    Why enroll for Advanced Certification in Data Engineering with GenAI?

    pay scale by Edureka courseThe global data lake market, valued at USD 22.8B in 2026, is projected to reach USD 61.84B by 2031, at a CAGR of 22.08% โ€” Mordor Intelligence
    IndustriesBy 2030, half of all AI agent deployment failures will trace back to insufficient governance-platform enforcement โ€” Gartner
    Average Salary growth by Edureka courseDatabricks-certified Data Engineers earn an average of USD 129,716 annually in the US, with top earners exceeding USD 162,000 โ€” ZipRecruiter

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

    As businesses embrace AI at scale, the need for skilled Data Engineers has become one of the fastest-growing opportunities in technology. Master modern Data Engineering with GenAI and build the expertise to power tomorrow's intelligent enterprises. One future-ready skill set. Multiple high-growth career opportunities. Choose the path that aligns with your ambitions.
    Annual Salary
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    Why Advanced Certification in Data Engineering with GenAI from edureka

    Live Interactive Learning

    Live Interactive Learning

    • World-Class Instructors
    • Expert-Led Mentoring Sessions
    • Instant doubt clearing
    24x7 Support

    24x7 Support

    • One-On-One Learning Assistance
    • Help Desk Support
    • Resolve Doubts in Real-time
    Hands-On Project Based Learning

    Hands-On Project Based Learning

    • Industry-Relevant Projects
    • Course Demo Dataset & Files
    • Quizzes & Assignments
    Industry Recognised Certification

    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

    • skillLakehouse Architecture
    • skillUnity Catalog Governance
    • skillBatch & Streaming Ingestion
    • skillCI/CD for Data Pipelines
    • skillAzure Databricks Administration
    • skillGenAI & Agentic Data Engineering

    Tools Covered

    • Databricks
    • Azure EventHub
    • dbt
    • Delta Lake
    • Lakeflow
    • Model Context Protocol
    • Microsoft Azure
    • Microsoft Fabric
    • MLFlow
    • Mosaic AI
    • Open Lineage
    • Snowflake
    • Spark
    • Unity Catalog
    • Python
    • Azure SQL
    • Azure Pipelines
    • GitHub Actions

    Advanced Certification in Data Engineering with GenAI Course Curriculum

    Curriculum Designed by Experts

    AdobeIconDOWNLOAD CURRICULUM

    Data Engineering Foundations and the Modern Data Landscape

    11 Topics

    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

    skillHands-on

    • Building Databricks Workspace
    • Comparing SQL Notebooks

    skillSkills

    • Lakehouse Concepts
    • Databricks Platform
    • ETL vs ELT
    • Data Architecture

    Python for Data Engineering

    12 Topics

    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

    skillHands-on

    • Building REST API Pipeline
    • Testing and Logging Pipelines

    skillSkills

    • Python
    • REST APIs
    • Pandas
    • pytest

    Advanced SQL for Data Engineering

    11 Topics

    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

    skillHands-on

    • Implementing SCD Type 2
    • Designing Star Schema
    • Optimizing SQL Queries

    skillSkills

    • Window Functions
    • MERGE/Upserts
    • Star Schema
    • Query Optimization

    Databricks Lakehouse Platform & Compute Fundamentals

    10 Topics

    Topics

    • Control-Data Plane
    • Workspace Architecture
    • Unity Catalog Overview
    • Compute Types
    • SQL Warehouses
    • Cluster Configuration
    • Notebook Features
    • Git Folders
    • CLI & API
    • Compute Cost Basics

    skillHands-on

    • Comparing Databricks Compute
    • Automating Git CLI Workflows

    skillSkills

    • Databricks Workspace
    • Compute Types
    • SQL Warehouses
    • Git Folders

    Apache Spark Architecture & PySpark Fundamentals

    11 Topics

    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

    skillHands-on

    • Analyzing PySpark Jobs

    skillSkills

    • Spark Architecture
    • PySpark DataFrame API
    • Lazy Evaluation
    • Partitioning

    Spark SQL, Performance & Troubleshooting

    9 Topics

    Topics

    • Spark SQL Interop
    • Join Strategies
    • Repartition vs Coalesce
    • Caching Strategies
    • Spark UI Basics
    • DAG Visualization
    • Skew & Spill
    • Shuffle Bottlenecks
    • Adaptive Query Execution

    skillHands-on

    • Optimizing Skewed Joins
    • Benchmarking AQE Performance

    skillSkills

    • Spark UI
    • Join Strategies
    • Skew/Shuffle Diagnosis
    • Adaptive Query Execution

    Delta Lake Deep Dive

    11 Topics

    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

    skillHands-on

    • Managing Delta Schema Evolution
    • Restoring Delta Table Versions
    • Benchmarking Liquid Clustering

    skillSkills

    • Delta Lake
    • Time Travel
    • OPTIMIZE/Z-Order
    • Liquid Clustering

    Data Ingestion Patterns โ€” Batch

    9 Topics

    Topics

    • Full/Incremental Load
    • COPY INTO Basics
    • Idempotent Ingestion
    • Auto Loader
    • Schema Inference/Evolution
    • Rescued Data Column
    • Lakeflow Connect
    • File Format Choices
    • Checkpointing Design

    skillHands-on

    • Building Auto Loader Pipeline
    • Performing Historical Data Backfill

    skillSkills

    • Auto Loader
    • COPY INTO
    • Lakeflow Connect
    • Idempotent Ingestion

    Data Ingestion Patterns โ€” 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

    skillHands-on

    • Building Streaming CDC Pipeline
    • Consuming Change Data Feed

    skillSkills

    • Structured Streaming
    • Change Data Feed
    • CDC Merge Patterns
    • Watermarking

    Medallion Architecture & Lakehouse Data Modeling

    9 Topics

    Topics

    • Medallion Layers
    • Data Contracts
    • Dimensional Modeling
    • SCD via MERGE
    • Data Vault Concepts
    • Star Schema Comparison
    • Partitioning Strategy
    • Dual-Consumption Design
    • Schema Governance

    skillHands-on

    • Building Medallion Data Pipeline

    skillSkills

    • Medallion Architecture
    • Dimensional Modeling
    • SCD via MERGE
    • Data Vault

    Lakeflow Declarative Pipelines & Data Quality

    8 Topics

    Topics

    • Declarative Pipelines
    • Streaming Tables/MVs
    • Pipeline DAG Design
    • Data Quality Expectations
    • Dev/Prod Modes
    • Event Logs/Lineage
    • Lakehouse Monitoring
    • DQX/GE Patterns

    skillHands-on

    • Building Lakeflow Quality Pipeline

    skillSkills

    • Lakeflow Declarative Pipelines
    • Expectations
    • Lakehouse Monitoring
    • Data Quality

    Orchestration with Lakeflow Jobs & Databricks Workflows

    11 Topics

    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

    skillHands-on

    • Orchestrating Lakeflow Workflows

    skillSkills

    • Lakeflow Jobs
    • Task Orchestration
    • Scheduling & Alerting
    • Airflow Integration

    Unity Catalog โ€” Governance, Security & Lineage

    13 Topics

    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

    skillHands-on

    • Implementing Unity Catalog Governance

    skillSkills

    • Unity Catalog
    • Row/Column Security
    • PII Classification
    • Metric Views

    CI/CD, DevOps & Databricks Asset Bundles

    10 Topics

    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

    skillHands-on

    • Deploying Databricks Asset Bundles

    skillSkills

    • Databricks Asset Bundles
    • CI/CD
    • GitHub Actions
    • PySpark Testing

    Performance Tuning, Monitoring & Cost Optimization

    9 Topics

    Topics

    • File Sizing
    • Predictive I/O
    • Photon Engine
    • Lakehouse Monitoring
    • System Tables (Billing)
    • Cost Optimization
    • Autoscaling Policies
    • Query Optimization Revisit
    • Cost Dashboards

    skillHands-on

    • Optimizing Databricks Platform Costs

    skillSkills

    • Performance Tuning
    • System Tables
    • Cost Optimization
    • FinOps

    Azure Databricks โ€” Environment, Networking & Security

    8 Topics

    Topics

    • Workspace Deployment
    • Pricing Tiers
    • VNet/Private Link
    • Network Security Groups
    • Entra ID Integration
    • Secret Scopes
    • UC on Azure
    • Azure Monitor

    skillHands-on

    • Securing Azure Databricks Workspace

    skillSkills

    • Azure Databricks
    • Networking & Security
    • Entra ID
    • Key Vault

    Azure Data Integration โ€” ADF, ADLS Gen2 & Monitoring

    7 Topics

    Topics

    • ADLS Gen2 Basics
    • External Locations
    • Azure Data Factory
    • ADF/Lakeflow Jobs
    • Log Analytics
    • Cross-Platform Cost Mgmt
    • Disaster Recovery

    skillHands-on

    • Integrating ADF with Databricks

    skillSkills

    • ADLS Gen2
    • Azure Data Factory
    • Log Analytics
    • Hybrid Orchestration

    Generative AI & LLM Foundations for Data Engineers

    10 Topics

    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

    skillHands-on

    • Processing Documents with GenAI

    skillSkills

    • Foundation Models
    • Mosaic AI
    • Chunking & Embeddings
    • MLflow

    Context Engineering for AI Data Pipelines & Agents

    11 Topics

    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

    skillHands-on

    • Building Governed MCP Server

    skillSkills

    • Context Engineering
    • MCP
    • Unity AI Gateway
    • Agent Memory

    Semantic Layer & Knowledge Engineering for AI

    8 Topics

    Topics

    • Catalog as Decision-Maker
    • Business Semantics Layer
    • Glossary & Domains
    • Genie Ontology
    • Knowledge Graphs
    • Entity Resolution
    • Cross-Tool Semantics
    • Semantic Asset Governance

    skillHands-on

    • Building Governed Semantic Layer

    skillSkills

    • Semantic Layer
    • Business Glossary
    • Knowledge Graphs
    • Genie Ontology

    Building RAG & Vector Search Pipelines for Enterprise Data

    7 Topics

    Topics

    • RAG Architecture
    • Vector Search Index
    • Embedding Pipeline Design
    • Chunking for Retrieval
    • Vector Index Governance
    • RAG Evaluation
    • RAG App Build

    skillHands-on

    • Building Enterprise RAG Pipeline

    skillSkills

    • RAG Architecture
    • Vector Search
    • Embedding Pipelines
    • RAG Evaluation

    Agentic AI for Data Engineering

    9 Topics

    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

    skillHands-on

    • Configuring Governed Genie Space
    • Building Agentic Incident Summarizer

    skillSkills

    • Agentic AI
    • Mosaic Agent Framework
    • Genie/AI-BI
    • Unity AI Gateway

    Databricks SQL, AI/BI, Genie & dbt for Analytics Engineering (Self-paced)

    9 Topics

    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

    skillHands-on

    • Building Genie Analytics Dashboard
    • Deploying dbt on Databricks

    skillSkills

    • Databricks SQL
    • Genie
    • dbt Core
    • dbt-Databricks Adapter
    • Slim CI

    Data Products, Feature Serving & Emerging Platform Capabilities(Self-paced)

    5 Topics

    Topics

    • Data-as-a-Product
    • Reverse ETL Patterns
    • Lakebase Serving
    • Feature Store Fundamentals
    • Multi-Region DR

    skillHands-on

    • Deploying Lakebase API
    • Planning Multi-Region Disaster Recovery

    skillSkills

    • Data Products
    • Lakebase
    • Feature Store
    • Reverse ETL
    • Disaster Recovery

    Snowflake for Data Engineering - Part 1: Architecture & Core Engineering (Self-paced)

    8 Topics

    Topics

    • Snowflake Architecture
    • Object Model Basics
    • COPY INTO & Snowpipe
    • Semi-Structured Data (VARIANT)
    • Time Travel & Cloning
    • Query Performance
    • Cost Model & Warehouses
    • Snowflake vs Databricks

    skillHands-on

    • Processing JSON in Snowflake
    • Benchmarking Warehouse Cost Performance

    skillSkills

    • Snowflake Architecture
    • Snowpipe
    • Query Profile
    • Warehouse Sizing

    Snowflake for Data Engineering โ€” Part 2: Advanced Snowflake & Lakehouse Interoperability(Self-paced)

    8 Topics

    Topics

    • Snowpark DataFrame API
    • Dynamic Tables
    • Streams & Tasks
    • Iceberg Interoperability
    • Secure Data Sharing
    • RBAC & Data Masking
    • Cortex AI Functions
    • Platform Decision Framework

    skillHands-on

    • Building Dynamic Table Pipeline
    • Querying Cross-Platform Iceberg Tables

    skillSkills

    • Snowpark
    • Dynamic Tables
    • Apache Iceberg
    • Cortex AI

    dbt for Analytics Engineering โ€” Part 1: Fundamentals & Modeling(Self-paced)

    8 Topics

    Topics

    • dbt Project Structure
    • Staging/Intermediate/Marts
    • Jinja Templating
    • Sources & Freshness
    • Generic & Singular Tests
    • Materializations
    • Incremental Models
    • Multi-Adapter Setup

    skillHands-on

    • Building dbt Analytics Project
    • Creating Incremental Model Snapshot

    skillSkills

    • dbt Core
    • Jinja
    • Materializations
    • Incremental Models

    dbt for Analytics Engineering โ€” Part 2: Advanced dbt, Semantic Layer & Deployment(Self-paced)

    8 Topics

    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

    skillHands-on

    • Defining dbt Semantic Metrics
    • Building Slim CI Pipeline

    skillSkills

    • 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?

    Many traditional data engineering courses focus only on pipelines, storage, and processing. This program also covers the AI-ready data layer that modern enterprises require, including vector search, RAG pipelines, semantic layers, context engineering, and data governance.

      Learners gain practical experience with Apache Spark, Delta Lake, Databricks Lakeflow, Unity Catalog, Azure Databricks, CI/CD, MCP, and agentic data workflows. With 25+ hands-on activities and dedicated Snowflake and dbt electives, the program helps professionals build secure, governed data platforms for analytics and Generative AI applications.

        It also develops relevant technical skills that can support preparation for Databricks Data Engineer certification exams and the Microsoft DP-750 exam.

          Note that, RAG quality begins with data quality, governance, and reliable retrievalโ€”not only the language model.

            What is covered in Edurekaโ€™s Advanced Certification in Data Engineering with GenAI?

            Edurekaโ€™s Advanced Certification in Data Engineering with GenAI is a live, instructor-led program covering modern data engineering, lakehouse architecture, cloud platforms, data governance, and Generative AI.

              The curriculum includes Python, SQL, Apache Spark, PySpark, Delta Lake, Databricks Lakeflow, Unity Catalog, Azure Databricks, CI/CD, MLflow, vector search, RAG, MCP, and agentic data engineering. Self-paced electives also introduce Snowflake, Snowpark, dbt, Databricks SQL, Genie, and data products.

                What are the prerequisites for the Data Engineering with GenAI program?

                Learners should have a working knowledge of Python and SQL. Prior experience with Apache Spark, Databricks, Microsoft Azure, Snowflake, dbt, or Generative AI is not required.

                  The foundational modules introduce the core data engineering and Databricks concepts required for the advanced topics.

                    Who should enroll in the Advanced Certification in Data Engineering with GenAI?

                    The program is suitable for:
                    • 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?

                    The program includes 66 hours of live, instructor-led training across 22 modules and five courses. It also provides six self-paced elective modules covering Databricks analytics, data products, Snowflake, and dbt.

                      What will learners achieve after completing the program?

                      Learners will be able to:
                      • 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?

                      The program covers:
                      • 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?

                      The program first builds skills in data ingestion, transformation, distributed processing, orchestration, governance, cloud integration, and deployment.

                        It then applies these skills to GenAI use cases involving unstructured data, embeddings, vector search, semantic layers, RAG, MCP, and AI agents. This approach helps learners build governed data platforms for analytics and AI applications.

                          What does the Azure Databricks training cover?

                          Unity Catalog is Databricksโ€™ centralized governance solution for data and AI assets.

                            Learners use Unity Catalog to manage permissions, row-level security, column masking, PII classification, lineage, audit logs, metric views, and secure data sharing.

                              What does the program cover under Databricks Lakeflow?

                              The program covers Lakeflow Connect, Lakeflow Declarative Pipelines, and Lakeflow Jobs.

                                Learners build production workflows with data-quality rules, task dependencies, scheduling, retries, alerts, and pipeline monitoring.

                                  Is the Data Engineering with GenAI program hands-on?

                                  Yes. The program is hands-on driven and enables learners towork on REST API ingestion, Spark optimization, Delta Lake, streaming pipelines, Lakeflow orchestration, Unity Catalog, Azure Databricks, CI/CD, RAG, MCP, and AI agents.

                                    What are the system requirements?

                                    Learners need a Windows, macOS, or Linux computer with at least 8 GB RAM, although 16 GB is recommended.

                                      The system should also have 50 GB of free storage and a stable internet connection of at least 5 Mbps. Cloud-based Databricks lab environments are provided.

                                        Advanced Certification in Data Engineering Projects

                                         certification projects

                                        Governed Retail Lakehouse Pipeline

                                        Build an end-to-end Bronze โ†’ Silver โ†’ Gold pipeline for a retail dataset, including an SCD2 dimension table, and defend the layout choices in a short architecture note.
                                         certification projects

                                        Diagnose and Fix a Skewed Join

                                        Deliberately trigger a skewed Spark join, diagnose it in the Spark UI, then fix it with salting or a broadcast join and quantify the improvement.
                                         certification projects

                                        Multi-Task Lakeflow Workflow

                                        Chain ingestion โ†’ a Lakeflow Declarative Pipeline โ†’ a Databricks SQL task, with a file-arrival trigger and failure alerting.
                                         certification projects

                                        Governed Namespace with a Certified Metric

                                        Build a 3-level Unity Catalog namespace, apply a row filter and column mask, tag a column for automated PII classification, and publish a certified Metric View.
                                         certification projects

                                        Azure Databricks Secure Landing Zone

                                        Deploy an Azure Databricks workspace with a Key Vault-backed secret scope, an ADLS Gen2 external location, and an Entra ID service principal.
                                         certification projects

                                        Ship a Pipeline Through CI/CD

                                        Package a pipeline as a Databricks Asset Bundle, deploy it to dev and staging via CLI, and wire up a GitHub Actions workflow for automated deployment.
                                         certification projects

                                        RAG-Powered Document Q&A App

                                        Build a Delta Sync vector search index over a governed document table and connect it to a Foundation Model endpoint for end-to-end retrieval-augmented Q&A.
                                         certification projects

                                        MCP Server Over Unity Catalog

                                        Expose a Unity Catalog schema's metadata and a query tool to an agent via MCP, with context offloading and a Unity AI Gateway runtime guardrail that blocks an out-of-policy action.
                                         certification projects

                                        Genie-Grounded Incident Response Agent

                                        Configure a Genie space over a governed Gold layer and build a lightweight Mosaic AI agent that monitors pipeline system tables and drafts incident summaries.

                                        Data Engineering Course Certification

                                        Learners must complete all 22 live modules, pass the required assessments, and successfully pass the final assessment to earn the certificate.

                                        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
                                        The certification demonstrates that the learner has completed structured training, practical labs, assessments, and data engineering projects.

                                        It can be added to rรฉsumรฉs and professional profiles to highlight skills in data engineering, Azure Databricks, lakehouse architecture, governance, and GenAI.

                                        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.

                                        Edureka Certification
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                                        Clean, simple and a fantastic learning resource. The courses are a great resource for personal development and continual learning. They are well structured and provided both ease of access and depth while allowing you to go at your own pace.Support team people is also really very cooperative and helps out at there best.

                                        December 09, 2017
                                         testimonials
                                        Gagan Maheshwari
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                                        Thanks a lot for your Android course. Right from the point of the start of the demo class, until the end of the complete course, the ride has been truly joyous and full of learning . I had no insight into Android development but with the help of your excellent instructor, I now stand a chance to explore wider into this. The webinars were truly awesome. Live Online sessions were highly interactive. The edureka! methodology for Online classes has changed the way I look at webinars now. Plus point is the knowledgeable course material. I am falling short of words describing the great experience of my Android training at edureka!. Thanks Team edureka!

                                        December 09, 2017
                                         testimonials
                                        Joga RaoPrincipal Data Architect at AEMO
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                                        I am a Customer at Edureka. I attended the AWS Architect Certification Training, I found the training to be very informative. The course content was excellent, just what I was after. The trainer was very knowledgeable. I found him to be very patient, he listened and answered everyone's questions. I especially liked the way he repeated and summarised the previous day's leanings at the start of each new day. I also liked his interactive style of training. Edureka demonstrated the highest standard of professionalism in delivering the course content and their support to me in helping complete the project has been exceptional. Thanks Edureka!

                                        December 09, 2017
                                         testimonials
                                        Anitha GuruswamiQA Consultant
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                                        This company has been heaven sent to anyone interested in learning the newer technologies that are changing by the day. Their instructors are top notch and above all their customer service is unparalleled. The student experience was amazing for me. I took the Selenium course and the content was perfect. My instructor obviously had wealth of experience in the material he was teaching. He had answers of all questions we had asked. I will surely take more courses with them and I have recommended edureka to several of my colleagues. Great Job! edureka.

                                        December 09, 2017
                                         testimonials
                                        Venkat RamanaTest Architect at NCR corporation pvt ltd
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                                        Thanks for the quick reply and solving the issue. I was really impressed by your 24/7 support even during the festive period. I appreciate your service and the trainer for Bigdata is very friendly and we've learnt the subject in very effective way. Thanks Edureka for doing such a great things.

                                        December 09, 2017
                                         testimonials
                                        Gnana Sekhar VangaraTechnology Lead at WellsFargo.com
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                                        Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best.

                                        December 09, 2017

                                        Hear from our learners

                                         testimonials
                                        Balasubramaniam MuthuswamyTechnical Program Manager
                                        Our learner Balasubramaniam shares his Edureka learning experience and how our training helped him stay updated with evolving technologies.
                                         testimonials
                                        Sriram GopalAgile Coach
                                        Sriram speaks about his learning experience with Edureka and how our Hadoop training helped him execute his Big Data project efficiently.
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                                        Vinayak TalikotSenior Software Engineer
                                        Vinayak shares his Edureka learning experience and how our Big Data training helped him achieve his dream career path.

                                        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?

                                        No. The program begins with Python, SQL, lakehouse architecture, and Databricks platform fundamentals.

                                        Advanced concepts in Apache Spark, PySpark, Delta Lake, streaming, governance, and orchestration are introduced progressively.

                                        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?

                                        The program first builds expertise in data pipelines, Spark, Delta Lake, Lakeflow, Unity Catalog, Azure integration, and CI/CD.

                                        It then extends these capabilities into GenAI through context engineering, semantic layers, vector search, RAG, MCP, Mosaic AI, agent development, and AI governance.

                                        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.

                                        Is the Advanced Certification in Data Engineering with GenAI suitable for working professionals?

                                        Yes. The program is designed for working professionals seeking structured, instructor-led learning with guided labs, recordings, and self-paced electives.
                                        Have more questions?
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