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#Azure Databricks

Azure Databricks is a unified analytics platform that combines Apache Spark with the scalability of Microsoft Azure. It enables seamless data engineering, machine learning, and real-time analytics on massive datasets — helping organizations build smarter, data-driven solutions with speed and efficiency.

Training Highlights

✅ Databricks Architecture
✅ PySpark & Spark SQL
✅ Delta Lake & Delta Tables
✅ Databricks Notebooks
✅ Python, PySpark, SQL
✅ Event Hub, Kafka
✅ Job Workflows & Automation
✅ CI-CD Integrations (DevOps)
✅ Real Time Project
✅ 1:1 Mentorship, Resume

Modules We Learn
✅ Module 1: SQL Server (MSSQL), TSQL
✅ Module 2: Azure Databricks
✅ Module 3: End-to-End (ECommerce Platform)
✅ Module 4: Databricks Certifications

Course Duration: 2 Months

Azure Databricks Course Content

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Module 1: SQL Server (MSSQL), TSQL

Ch 1: SQL Database Job Roles

Introduction to Data
Database Intro, Types
OLTP, DWH, OLAP
DBMS Concepts
Database Job Roles
Data Engineer Job Roles

Ch 2: Database Intro & Installations

SQL Server Installations
Instance Concepts
Authentication Types
Authentication Modes
SSMS Tool Installation
Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

Creating Databases (GUI)
Creating Tables, Columns (GUI)
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, Add
DML: Insert, Update, Delete
DQL: Select, Fetch
SQL Operators
Special Operators

Ch 5: Excel Data Imports

Data Imports with Excel
Order By: Asc, Desc
Order By with WHERE
TOP & OFFSET
UNION, UNION ALL

Ch 6: Schemas & Batches

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

Ch 7: Constraints, Keys & RDBMS

Null, Not Null Constraints
Unique Key Constraint
Primary Key Constraint
Foreign Key & References
Default Constraint & Usage
DB Diagrams & ER Models

Ch 8: Normal Forms & ERD

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Self Referencing Keys
Cascading Keys
Database Diagrams

Ch 9: Joins Queries – Level 1

Joins: Table Comparisons
Inner Join & Outer Joins
Cross Join & Cross Apply
Table Combination
Table & Column Aliases

Ch 10: Joins Queries – Level 2

Group By & Aggregations
Joins with Group By
3 Table, 4 Table Joins
Join Queries with Aliases
WHERE & HAVING
Query Execution Order

Ch 11: Sub Queries

Distinct & Union, Union All
Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Correlated Queries

Ch 12: Views & Data Analytics

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
Data Analytics

Ch 13: Stored Procedures – Level 1

Stored Procedures: Realtime Use
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
Stored Procedures, Tuning

Ch 14: Stored Procedures – Level 2

Merge Statement
Upsert Operations with Merge
Merge with OLTP & DWH
Matched and Not Matched
Merge Statement inside SPs

Ch 15: Functions – Level 1

Using Defined Functions (UDF)
Scalar Functions in Real-world
Table Valued Functions
Parameterized Queries
Returns and Return
SP Versus Functions

Ch 16: Functions – Level 2

Aggregated Functions
Date & Time Functions
String Functions
Window Functions
Rank, Row_Number
DenseRank, Partition By

Ch 17: Triggers & Automations

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

Ch 18: Transactions & ACID

Transaction Concepts in OLTP
Auto Commit Transaction
Explicit Transactions
COMMIT, ROLLBACK
Lock Hints & Query Blocking
READPAST, LOCKHINT

Ch 19: Indexes Basics, Tuningwith Excel

Important System Views
Indexes & Tuning
Clustered Index, Primary Key
Non Clustered Index & Unique
Creating Indexes Manually
Composite Keys, Query Optimizer
Composite Indexes & Usage

Ch 20: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
IIF(), CASE Statement
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage

Ch 21: Data Types & Variables

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type
Variables in SQL
Cursor Variable & Fetch

Ch 22: Temp Tables

Local Temp Tables
Global Temp Tables
Testing Temp Tables
SELECT..INTO Statement
Bulk Copy Operations

Ch 23: SQL Server Architecture

Network Protocols
Query Execution Engine
Parser, Compiler, Checkpoint
SQL Manager, DB Manager
Storage Engine, Locks
SQL OS Components

Ch 24: Real-Time SQL Server Case Studies (2)

✅Healthcare Management System

Patient Records Management
Doctor Appointment Scheduling
Billing & Insurance Processing
Medical Reports Analysis

✅E-Commerce Database

Customer & Product Management
Order Processing
Inventory Tracking
Sales Reporting

✅Banking & Financial Systems

Customer Accounts
Transaction Processing
Loan Management
Fraud Detection Queries

Module 2: Azure Databricks

Ch 1: Databricks Introduction

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

Ch 2: Databricks Architecture

Unity Catalog, Volume
Spark Clusters
Apache Spark and Databricks
Apache Spark Ecosystem
Compute Operations
Hadoop, MapReduce, Apache Spark

Ch 3: Unity Catalog

Unity Catalog Concepts
Workspace Objects
Databricks Notebooks
Databricks Workspace UI
Organizing Workspace Objects
Creating Volumes
Spark Table Creations
Spark UI: Limitations

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: Table Types

Delta Tables
Managed Tables
External Tables
Data Partitioning
Union, Views in Spark
External Volumes

Ch 6: Spark SQL: Functions

Math, Sort Functions
String, DateTime Functions
Conditional Statements
S QL 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 Store
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
Value Types
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
List Items, Indexes
Tables Versus Dictionaries

Ch 13: Python: Modules & Dataframes

Python Modules
Pandas
NumPy
Dataframe Concepts
Handling Nulls
Data Cleansing Concepts
Pandas Series, arrays
Indexes, Indexed Lists

Ch 14: PySpark Concepts

Constructing Dataframes
Single List Dataframes
Multi List Dataframes
Pandas Dataframes
Contact & Union
Merge
Join Options with Dataframes

Ch 15: Medallion Architecture – 1

Medallion Architecture
Aggregated Data Loads
Broze, 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
Dataframes, Temp Views
Aggregated Data Loads
Gold Data Consumption

Ch 17: Delta Lake

Databricks DeltaLake
Schema Evolution
Azure SQL DB Connections
Dataframes, Temp Views
Delta Table API
Deleting Records
Updating Records
Merging Records
Old History 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

Worksflows & CRON
Job Compute, Running Tasks
Python Script Tasks
Parameters into Notebook Tasks
Parameters into Python Script Tasks
Concurrent Executions, Dependencies
Branching Control with the If-Else Task

Ch 20: Pyspark: Auto Loader – 1

AutoLoader Concept
Cloudfiles Architecture
Checkpoint Configurations
Creating Directories
Reading Databricks Cloud Sources
Initial Loads

Ch 21: PySpark: Auto Loader – 2

Reading Streams with Auto Loader
Reading a Data Stream
Manually Cancel your Data Streams
Writing to a Data Stream
Schema Evaluation Modes
Adding New Columns
Workspace Modules

Ch 22: Lake Flow Declarative Pipelines

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

Ch 23: Databricks Optimizations

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

Ch 24: Security Concepts

Overview of ACLs
Adding a New User to Workspace
Workspace Access Control
Cluster Access Control
Groups & LakeBridge
Access Keys (Tokens)

Ch 25: Azure Databricks

Deployment Modes
Classic Deployment
Azure Databricks Account
Azure Databricks Workspace
Databricks Compute
Photon Acceleration
Scaling & Tuning
Open Source Databricks Vs Azure Databricks

Ch 25: Databricks Data Engineer Associate Exam

Databricks Data Engineer Associate Exam
AVRO Formats
Exam Guidance
Databricks Exam Pattern
Exam Q & A, Scenarios

Module 3: End-to-End Project For Resume (ECommerce Platform)

Project Objective:

Build an end-to-end Azure Data Engineering solution to process, transform, and analyze e-
commerce business data from multiple sources.

Technologies Used:

Azure Databricks (Apache Spark)
Azure SQL Database
Azure Blob Storage
Azure Monitor
Azure Monitor Logs

Skills Gained:

Data Ingestion & ETL Development
Azure Data Factory Pipelines
Databricks & PySpark Transformations
Data Lake Architecture
Medallion Architecture (Bronze/Silver/Gold)
Real-Time Industry Experience

Components For Project (From Resume Perspective):

Source Systems
Bronze
Silver
Gold
PySpark
Power BI Reporting
Monitoring
Alerting
Deployment
End to End Integrations

Module 4: Databricks Certifications

Azure Databricks (DP-750)

Exam Pattern
Exam Q & A, Scenarios

Databricks Data Engineer Associate Exam

Exam Pattern
Exam Q & A, Scenarios

Who should join this Azure Databricks course?

This course is ideal for freshers, developers, data analysts, and data engineers looking to build a career in cloud data engineering. No prior experience is necessary.

What are the prerequisites for this course?

There are no prerequisites. The course starts from the basics of SQL Server and gradually builds up to advanced Databricks and PySpark concepts.

What is the duration of the course?

The program runs for 2 months and is delivered as 100% practical, real-time training.

What will I learn in this course?

You’ll cover four modules: SQL Server (MSSQL) & T-SQL fundamentals, Azure Databricks (architecture, Unity Catalog, Spark SQL, PySpark, Delta Lake, Auto Loader, optimizations), an end-to-end real-time E-Commerce data engineering project, and certification guidance for DP-750 and the Databricks Data Engineer Associate exam.