Ch 1: SQL Database Job Roles

• Database Intro
• OLTP, DWH, OLAP
• DBMS Basics
• Data Engineer Job Roles

Ch 2: Database Intro & Installations

• SQL Server Installations
• Instance & Collations
• SSMS Tool Installation
• Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

• SQL Basics (DDL, DML, etc..)
• Creating Databases, Tables
• Data Inserts (GUI, SQL)
• Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

• DDL: Create, Alter, Drop
• DML: Insert, Update, Delete
• DQL: Select, Fetch
• Add, Truncate Statements
• SQL Operators

Ch 5: Data Types & Variables

•  Integer Data Types
•  Character, MAX Data Types
• Decimal & Boolean Data Types
• Date and Time Data Types
• SQL_Variant Type

Ch 6: Data Imports

• Data Imports with Excel
• Data Imports with CSV
• Data Types Detection
• OLE-DB Connections
• ORDER BY, TOP, OFFSET

Ch 7: Schemas & Batches

• Schemas & Table Grouping
• Real-world Banking Database
• 2 Part, 3 Part & 4 Part Naming
• Batch Concept & “Go” Command

Ch 8: Constraints, Keys & RDBMS

• Null, Not Null Constraints
• Unique Key & Check
• Primary Key Constraint
• Foreign Keys, Default
• DB Diagrams & ER Models

Ch 9: Normal Forms & RDBMS

• Normal Forms: 1 NF, 2 NF
• 3 NF, BCNF and 4 NF
• 1:1, 1:M, M:1 Cardinality
• Cascading Keys, Self Referencing Keys

Ch 10: Joins & Queries

• Joins: Table Comparisons
• Inner Joins & Matching Data
• Outer Joins: LEFT, RIGHT
• Full Outer Joins & Aliases
• Self Joins & Aliases

Ch 11: Sub Queries

• Basic Sub Queries
• Aggregations
• Combining Queries
• Correlated Sub Queries
• UNION, UNION ALL

Ch 12: Group By Queries

• Group By, Distinct
• GROUP BY, HAVING
• Cube( ) and Rollup( )
• Sub Totals & Grand Totals
• Grouping( ) & Usage
• ISNULL, COALESCE

Ch 13: Joins with Group By, Sub Queries

• 3 Table, 4 Table Joins
• Join Queries & WHERE
• Join & Group By, Sub Queries
• IIF(), CASE Statement
• EXISTS, NOT EXISTS
• Query Execution Order

Ch 14: Views & RLS

• Views: Realtime Usage
• DML, SELECT with Views
• WITH CHECK OPTION
• Row Level Security (RLS)
• Important System Views

Ch 15: Functions & Queries

• User Defined Functions
• Scalar, Table Value Functions
• Variables & Parameters
• Date & Time Functions
• String Functions
• Aggregated Functions
• Data Conversion Functions

Ch 16: Advanced SQL & Window Functions

• Window Functions (Rank)
• Row_Number, DenseRank
• Partition By & Order By
• Lag & Lead Functions
• Pivot, UnPivot
• Running Totals
• Moving Average

Ch 17: Stored Procedures – 1

• Stored Procedures: Realtime Use
• Parameters Concept with SPs
• Procedures with SELECT
• System Stored Procedures
• Stored Procedures, Tuning

Ch 18: Stored Procedures – 2

• Merge Statement (Upsert)
• Merge with OLTP & DWH
• Matched and Not Matched
• Merge & SP Recompilations

Ch 19: Triggers & Automations

• Need for Triggers in Real-world
• DDL & DML Triggers
• For / After Triggers
• Instead Of Triggers
• Disabling DMLs Triggers

Ch 20: Transactions & ACID

• Auto Commit Transaction
• Explicit Transactions
• COMMIT, ROLLBACK
• Checkpoint & Query Blocking
• READPAST, LOCKHINT
• TRY…CATCH & Error Handling

Ch 21: SQL Query Performance & Indexing

• Clustered vs Nonclustered Indexes
• Index Seek vs Scan
• Execution Plans
• SARGable Queries
• Tuning Queries
• Avoiding SELECT *
• Indexing JOIN/WHERE columns
• Statistics Basics
• Query Optimization Examples

Ch 22: CTEs & Tuning

• Common Table Expression
• CTEs for Data Retrieval
• CTEs for DML Operations
• Data Cleansing Techniques
• Duplicate Detection/Removal

Ch 23: Temp Tables & Query Techniques

• Local & Global Temp Tables
• SELECT..INTO Statement
• Cursor Basics – When to Use & Avoid
• NULLIF
• Basic Execution-Plan Reading

Ch 24: Bonus / Advanced Module: SQL Server Internals

• Database Engine Components
• Parser, Compiler & Optimizer
• Parsing and Compilation
• Memory Manager & IO Managers

Real-Time Project on HealthCare Domain
Project Requirement:
Solve 20+ Real-World Healthcare Business Requirements.

Project Workflow:
Database Design → SQL Development → Data Analysis → Performance Optimization

Project Operational Flow:
Healthcare DB → Patients → Doctors → Appointments → Treatments → Billing → Insurance → SQL
Analysis → Stored Procedures → Performance Tuning → Business Reports

Business Requirements:
Monthly Hospital Revenue, Top Doctors By Patients Treated, Repeat Patients, Department
Performance, Average Treatment Cost, Unpaid Bills, Insurance Claim Analysis, Patient Trends,
Month-Over-Month Revenue And Top Procedures…

Phase 1: Databricks Fundamentals, Architecture & Unity Catalog
Ch 1: Databricks Introduction

•  Cloud ETL, DWH
• Cloud Computing
• Databricks Concepts
• Databricks Account
• Big Data in Cloud

Ch 2: Databricks Architecture

• Databricks Runtime (DBR)
• RDD & DAG
• Databricks Lakehouse Architecture
• Spark Compute Concepts
• Workers & Drivers
• Databricks Framework
• Databricks APIs

Ch 3: Unity Catalog

• Unity Catalog Concepts
• Metastore
• Catalogs & Schemas
• Managed & External Tables
• Storage Credentials
• External Locations
• Volumes
• GRANT / REVOKE
• Data Permissions
• Lineage, Data Governance
• Databricks Workspace UI
• Spark Table Creations

Phase 2: Spark SQL, Python ETL & PySpark

Ch 4: Spark SQL: Basics

• Spark SQL Notebooks
• Creating Catalog
• Creating Schemas
•  Creating Tables
• Spark Data Types
• PySpark API: SQL Queries
• Dropping Objects
• Notebooks: Exports, Clone

Ch 5: Spark SQL: Joins & Table Types

• Delta Tables
• Managed Tables
• Data Partitioning
• Union, Views in Spark
• Spark SQL Joins
• Aggregations
• Data Analytics

Ch 6: Spark SQL: Functions

• Math, Sort Functions
• String, DateTime Functions
• Conditional Statements
• SQL Expressions with expr()
• Volume for our Data Assets
• File Formats, Schema Inference
• Spark SQL Aggregations

Ch 7: Spark SQL: Time Travel

•  Time Travel Concepts
• Spark DB: Logical Architecture
• Spark DB: Physical Store
• Data File & Log File Store
• Time Travel
• DESCRIBE, EXTENDED
• HISTORY
•  Version Numbers

Ch 8: Python: Introduction, Print

• Python Introduction
• Python Versions
•  Python Implementations
• Python in Spark (PySpark)
• Python Print()
• Single, Multiline Statements

Ch 9: Python: Variables

• Python Variables
• Variable Declarations
•  Variable Values
• Multi Variable Values
•  Common Variable Values
• Realtime use of Variables

Ch 10: Python: Operators

• Need for Operators
• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Operator Precedence
• Operands in Python

Ch 11: Python: Control Statements

• Python Control Structures
• If … Else Statement
• Short Hand If
• ELIF & ELSE IF Statements
• OR, AND Concepts
• Python Loops

Ch 12: Python: Data Types

• Python Data Types
• Integer / Int Data Types
• Float, String Data Types
• List Data Type
• Dictionary Data Type
• Tuple Data Type

Ch 13: Python: Modules & DataFrames

• Python Modules
• Pandas, NumPy
• DataFrame Concepts
• Handling Nulls
• Data Cleansing Concepts
• Python Functions

Phase 3: Medallion Architecture, Delta Lake, Auto Loader, Lakeflow & Airflow
Ch 14: PySpark DataFrames & Transformations

• Creating PySpark DataFrames
• Reading Data Sources
• select(), filter(), withColumn()
• Handling NULL Values
• Data Type Casting
• Joins, Aggregations
• Window Functions
• Writing DataFrames

Ch 15: Medallion Architecture – 1

• Medallion Architecture
• Aggregated Data Loads
• Bronze, Silver and Gold
• Temp Views
• Spark Tables (Parquet)
• Work with File Sources

Ch 16: Medallion Architecture – 2

• Medallion Architecture
• Azure SQL DB Connections
• Joining Source Tables
• Data Frames, Temp Views
• Aggregated Data Loads
• Gold Data Consumption
• Raw Sources → Bronze → Silver → Gold → BI/Analytics

Ch 17: Delta Lake

• Databricks Delta Lake
• Schema Evolution
• Azure SQL DB Connections
• DataFrames, Temp Views
• Delta Table API
• MERGE / Upsert Operations
• Version History & Data Retention
• Delta Transaction Log

Ch 18: PySpark: Widgets

• PySpark Parameters
• Text Widgets
• User Parameters
• Manual Executions
• Automations
• UI & JSON For Widgets

Ch 19: Lake Flow Jobs

• Workflows & CRON
• Job Compute, Running Tasks
• Python Script Tasks
• Parameters into Notebook Tasks
• Parameters into Python Script Tasks
• Concurrent Executions, Dependencies
• Branching Control

Ch 20: PySpark: Auto Loader

• Cloud Files Architecture
• Checkpoint Configurations
• Schema Location
• Checkpoint Location
• Data Stream
• Schema Evaluation Modes
• Incremental File Ingestion
• Rescued Data Column

Ch 21: LakeFlow Declarative Pipelines

• SDP: Spark Declarative Pipelines
• Delta LIVE Tables
• Streaming Data Loads
• Bronze, Silver, Gold Data
• Materialized Views
• Pipeline Clusters
• Data Quality Checks

Ch 22: Databricks Optimizations

• Lazy Evaluation
• Explain Plan
• Caching, Data Shuffling
• Broadcast Joins
• Partitions, Data Skew
• Liquid Clustering
• VACUUM
• OPTIMIZE, Z Ordering

Ch 23: Databricks Security, AI

• ACL: Access Control List
• Workspace Access Control
• Catalog Security
• Schema, Volume Security
• Table Security
• Column Security

Ch 24: Databricks with Power BI

• Workspace Settings
• Access Keys (Tokens)
• Server Host
• HTTP Path
• Spark Connectors
• Databricks Connectors

Ch 25: Genie AI

• Genie AI Concepts
• Unity Catalog
• AI Components in Databricks
• Debugging Controls
• Using AI for Notebook Design

Ch 26: GitHub & Asset Bundle

• GitHub Concepts
• GIT: Main, Branches
• Asset Bundle
• Integrating GIT with Asset Bundle
• Process Integration

Ch 27: Apache Airflow

• Apache Airflow Concepts
• Airflow Operators & DAG
• Python Scripts For Airflow
• DatabricksRunNowOperator
• DatabricksSubmitRunOperator
• Airflow Jobs

Part 1: Fabric Concepts, DWH & Fabric Data Factory
Ch 1: Fabric Introduction

• Need for Fabric, Big Data
• Fabric Data Engineering Model
• Fabric Components (Items)
• Microsoft Fabric: Advantages
• Cloud Warehouse & AI
• AI with CoPilot
• Azure Versus Fabric DWH

Ch 2: Fabric Account, Workspace

• Need for Fabric Workspace
• Workspace Creation Process
• ETL, Storage, Analytical
• Streaming, Monitoring
• Compute & Separation

Ch 3: Fabric OneLake Architecture

• Intelligent Data Foundation
•  Polaris Distributed Engine
• Stateless & Stateful
• Cache, Metadata, Xact & Data
• Fabric Tasks, Inputs & DAG
• State Machine & Statistics
• Hot Spot Recovery

Ch 4: Fabric Warehouse

• Fabric Warehouse Creation
• Fabric Warehouse Features
• Fabric Warehouse Properties
• Fabric Warehouse Limitations
•  DWH Internal Operations
• Default Schemas & Objects
• SSMS Connections

Ch 5: Fabric Data Types

• Realtime use of Fabric Houses
• Exact, Approximate Numbers
• Date and Time Data Types
• Fixed & Variable Length
• Binary & String Data Types
• Fabric Type Limitations
• Save As table, Save As View

Ch 6: Fabric Caching

• Fabric Caching Process
• In-memory Cache, Disk Cache
• Cache Types: LRU /MRU
• Cold Cache / Cold Run
• Realtime use of Caching
• Performance Advantages
• Warehouse Optimizations

Ch 7: Fabric Statistics

• Query Engine Options
• Statistics Types
• Leverage Statistics
• Auto, Manual Statistics
• Update Statistics
• Statistics Consistency
• Statistics Lists & Reports

Ch 8: Time Travel

• Continuous Data Protection
• Data Storage, Retention
• FOR TIMESTAMP AS OF
• Time Travel Scenarios
• Time Travel Implementation
• Time Travel on Queries
• Time Travel Limitations

Ch 9: Zero Copy Cloning

• User Layer, Storage Layer
• Cloning & Parquet Files
• Synapse Data Warehouse
•  Data History Retention
• Point In Time , Schema Level
• Zero Copy Cloning Limitations

Ch 10: Fabric Security

• Workspace Security
• Warehouse Security
• Item Security & Roles
• Adding AD Users
• Item Security Limitations
• MFA & Client Security

Ch 11: Fabric Copy Job

• ETL Implementation Options
• Copy Job Item
• Data Loads with Copy Job
• Full Loads
• Testing Copy Jobs

Ch 12: Fabric Copy Job

• Incremental Loads with Copy Job
•  Business Key Concept
• DWH: Data Storage
• Testing Initial Loads
• Testing Incremental Loads
• Copy Job Limitations

Ch 13: Fabric Data Factory

• Need for Fabric Data Factory
• ETL Operations in FDF
• Data Sources, Transformations
• Activities and Connections
• Data Destinations (Sinks)
• Creating Pipelines

Ch 14: Fabric Pipelines Design

• Creation Options for Pipelines
• Azure SQL DB Data Loads
• Creating Data Sets
• Copy Command Usage
• Run ID & Monitoring
• Pipeline Creation, Verification

Ch 15: Data Loads with Azure

• Azure Data Lake Storage (ADLS)
• Azure BLOB Containers
• Fabric Data Loads From Azure Files
• Fabric Warehouse with Azure
• Run IDs and Activity
• Compressions & Advantages

Ch 16: ETL Staging

• Staging: Advantages
• Caching & Storing Concept
• Staging Types in Fabric
• Workspace & External
• External Stages in Pipelines
• Pipeline Trigger, Monitor

Ch 17: Fabric Aggr Data Loads

• Aggregation Scenarios
• Creating Views in TSQL
• Using Views in FDF Pipelines
• Using Pipeline Editor
• Data Loads to Warehouse
• Pipeline Verifications

Ch 18: Fabric Incremental Loads – 1

• Upsert (Incremental Loads)
• Business Key Concept
• SCD: Slowly Changing Dimension
• Full Loads Vs Incr Loads
• Testing Incrementing Loads
• Pipeline Execution Tests

Ch 19: Fabric Incremental Loads – 2

• Control Tables
• Watermark Columns
• Lookup Activity
• Stored Procedure Activity
• Parameters & Runs
•  Concurrency & Batch Count

Ch 20: OnPrem Gateways

• Need for On-Premise Gateway
• Installing & Configuring
• Authentication, Usage
• On-Premise Connections
• Pipelines for Data Loads
• Warehouse Data Storage
• Data Refresh with Gateways

Ch 21: Data Factory Pipeline Tuning

• Intelligent Throughput
• DOCP & Optimizations
• Staging & In-Memory
• Spark Compute Options
• Concurrent Connections
•  ETL Partitions in Real-world

Part 2: Fabric Data Flow, Lake House
Ch 1: Fabric Lakehouse

• Fabric Lakehouse Architecture
• Files and Tables Storage
• Direct Lake & AI
• Creating Lakehouse
• Azure SQL Database Source
• UI: Limitations

Ch 2: Lakehouse File Loads

• Creating Lakehouse
• Incremental Refresh
• Computed Tables
• Reusable Transformations
• Scheduling
• Monitoring
• Best Practices

Ch 3: Power Query Level 1

• Power Query Concept
• Fabric Lake House
• ETL, ELT Process with AI
• Data Combine: Union, Append
• Duplicate / Reference Queries
• Warehouse Data Loads

Ch 4: Power Query Level 2

• Table Transformations
• Group By, Transpose
• Header Row Promotion
• Reverse Rows, Count Rows
• Any Column Transformations
• Data Type, Fill & Pivot

Ch 5: Power Query Level 3

• Text Transformations
• Format, SubString
• Number Transformations
• Date Time Transformations
• Add Column Transformation

Ch 6: Power Query Level 4

• Column From Examples
• Conditional Column
• Index Transformation
• Duplicate Rows, Errors
• Advanced Editor

Ch 7: Power Query Level 5

•  ETL Parameters
• Big Data Access
• Static Parameters
• Dynamic Parameters
• List Queries
• M Language Expressions

Ch 8: Stream House, KQL

• Need for Stream House
• Auto creation of KQL
• Manual KQL Databases
• Differences with Warehouse
• Differences with Lakehouse

Ch 9: KQL Query Sets

• KQL Database Extraction
• File Imports – on Premises
• Metadata Edit Options
• Query Analytics
• Exports, Visualizations
•  Query Sets Versus Notebooks

Ch 10: Fabric Data Activator

•  Need for Alerts, Notifications
• Fabric Data Activator Options
• Alert Conditions, Thresholds
• Email Notifications
• Events & Notifications
• Edit / Enable / Disable

Ch 11: Mirror Database

• Need for Mirror Databases
• Configure Mirror Databases
•  Data Replication
• Schema Replication
• Connections & Usage
• Mirror DB Practical uses

Part 3: Python, PySpark, DWH
Ch 1: Fabric Notebooks

• Need for Notebooks
• Fabric Notebook Types
• Creating Environment
• Creating Spark Clusters
• Standard, High Concurrency
• Magic Command
• Freeze Cells

Ch 2: Spark SQL – 1

• Spark SQL Notebooks
• Creating Schemas
•  Delta Tables
• Parquet Tables
• Spark Joins
• Data Partitioning
• Union, Views in Spark

Ch 3: Spark SQL – 2

• Math, Sort Functions
• String, Date Time Functions
• Conditional Statements
• Data Recovery & Undo
• Version Number
• Describe Extended

Ch 4: Python Intro & Print

• Python Introduction
• Python Versions
• Python in Spark (PySpark)
• Python Print()
• Single, Multiline Statements

Ch 5: Python Variables

• Defining Variables
• Using Variables
• Printing Variables
• Display Variables
•  Variable Types
• Multi Value Variables
• If … Else Statement

Ch 6: Python Operators

• Integer Operators
• String Operators
• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Formatted Strings
• Indexing Operators
• ELIF, ELSE IF Statements

Ch 7: Python Data Types

• Python Data Types
• Integer / Int Data Types
• Float, String Data Types
• List Data Type
• List Items, Indexes
• Tuple Data Type
• Dictionary Data Type

Ch 8: Python Dataframes

• Pandas Module (Python)
• Dataframes from Lists
• Dataframe from Dict
• Pandas Dataframes
• Dataframe print, display

Ch 9: Python Dataframes Transformations

• Append
• Append with NoIndex
• Merge with ON, KIND
• spark.read.csv()
• spark.read.format()

Ch 10: Medallion Architecture

• Bronze, Gold and Silver
• Raw Data
• Data Preparation (Prepping)
• Temporary Views
• Big Data Analytics

Ch 11: PySpark: Medallion Loads 1

• Data Prep (Silver)
• Filtering DataFrame Records
• Removing Duplicate Records
• Sorting and Limiting Records
•  Spark SQL Dataframes
• Gold Layer Implementation
• Testing Aggregated Loads

Ch 12: PySpark: Medallion Loads 2

• Azure SQL DB Connections
• SQL Queries in PySpark
• Data Prep (Silver)
• Filtering Null Values
• Grouping and Aggregating
• Spark SQL Dataframes
• Gold Layer Implementation
• Notebook Utilities

Ch 13: PySpark: SCD

• Slowly Changing Dimension
• Merge Into Statement
• Error Handling
• Merge with OLTP Data Sources

Ch 14: PySpark: Widgets

• Notebook Parameters
• Text Widgets
• Parameters & JSON
• Notebook Schedules, Retry
• Modular Notebook Design
• Notebook Chaining

Ch 15: LakeHouse Architecture Optimizations

• Delta Log
• Delta Versioning
• Partition Strategy
• Small File Problem
• Adaptive Query Execution
• Explain Plan, Spark UI
• mssparkutils

Ch 16: LakeHouse Optimizations

• VACUUM, OPTIMIZE
•  ZORDER
• Broadcast Join
• Cache, Persist
• Shuffle Optimization

Ch 17: Semantic Models

• Creating Semantic Model
• Spark SQL: DDL, DML
• Adding Refences, Keys
• Using Model Layouts

Ch 18: Fabric Security

• Workspace Security
• Lakehouse Security
• Notebook Security
• Security Principals
• Authentication Options
• MFA (Multi Factor Authentication)

Project 1 : E-Commerce Domain on Databricks
Project Title

Retail Sales Analytics Platform (Medallion Architecture)

Project Overview
Students will build a production-style data platform using Lakehouse Architecture (Bronze, Silver,
and Gold), implementing modern ETL practices, data quality validation, incremental processing,
orchestration, optimization, and reporting.

Business Scenario
A multinational retail company receives daily data from multiple operational systems. The
company wants to build a centralized analytics platform to:

• Consolidate data from different business domains
• Improve data quality and consistency
• Track sales performance across regions
• Analyse customer purchasing behaviour
• Monitor inventory levels
•  Generate business KPIs for decision-makers
• Deliver Power BI dashboards with near real-time insights

Source Systems
Students will work with multiple datasets including:

• Customer Master
• Product Master
• Orders
• Inventory
• Region Master

Technologies Covered

• Apache Spark
• PySpark
• Spark SQL
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Git Integration
• Fabric ETL
• Fabric ELT
• OneLake

Resume Highlights
After completing this project, you can confidently showcase experience in:

• PySpark & Spark SQL
• Delta Lake
• Medallion Architecture
• ETL Pipeline Development
• Incremental Data Processing
• Data Quality & Validation
• Unity Catalog
• Workflow Automation
• Performance Optimization

Project 2 : Microsoft Fabric Data Engineering
Business Scenario

A multinational e-commerce company wants to centralize sales, customers, products, orders,
inventory, logistics and payment data into Microsoft Fabric.

Goal:
Create a scalable Fabric Data Platform capable of processing millions of records daily.
Skills Gained:

• Fabric Pipelines
• Fabric Notebooks
• Fabric Data Flow
• Semantic Models
• Medallion Architecture (Bronze/Silver/Gold)
• Real-Time Industry Experience
• Fabric APIs (REST, Workspace)

Components For Project (From Resume Perspective):

•  Bronze
• Silver
• Gold
• PySpark
• CI/CD
• Deployment
• End to End Integrations
• DP-700 Exam: Complete Guidance

Bonus Module: Databricks Data Engineer Certification Guidance

• Exam Q & A, Scenarios
• Certification Exam Objectives
• Topic-Wise Revision
• Scenario-Based Questions
• Practice Assessments
• Mock Exams
• Databricks Interview Questions
• Real-Time Scenario Discussions
• Resume Project Explanation
• Technical Mock Interview

The course is designed for beginners as well as working professionals who want to build practical Data Engineering skills. The syllabus states that there are no prerequisites and that training starts from the basics before progressing to advanced concepts.

The program covers SQL Server, T-SQL, Databricks, Spark SQL, Python, PySpark, Delta Lake, Unity Catalog, Medallion Architecture, Databricks Workflows, Microsoft Fabric, Fabric Data Factory, OneLake, Warehouse, Lakehouse, KQL, Power Query, Mirroring and related Data Engineering concepts.

The training is positioned as 100% practical and project-based, with hands-on labs, assignments, production-style scenarios and end-to-end Data Engineering projects.

Yes. Databricks coverage includes architecture, Unity Catalog, Spark SQL, Python, PySpark, Delta Lake, Medallion Architecture, Auto Loader, LakeFlow Declarative Pipelines, optimization, security, Genie AI, GitHub integration and Apache Airflow. Databricks FabricDataEngineer

The Fabric module includes Fabric architecture, workspaces, OneLake, Warehouse, security, Data Factory, pipelines, incremental loads, gateways, Lakehouse, Power Query, KQL, Data Activator, Mirroring, notebooks, Spark SQL, Python, PySpark and Lakehouse optimization. Databricks FabricDataEngineer

Yes. The program includes certification guidance, topic-wise revision, scenario-based questions, practice assessments, mock exams, Databricks interview questions, resume project explanation and technical mock interviews. Databricks FabricDataEngineer