Phase 1: Installations, Configurations & Design Concepts
Ch 1: SQL Database Job Roles

• Database Intro
• OLTP, DWH, OLAP
• DBMS Concepts
• 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
• Auto Detection of DataTypes
• OLE-DB Connections
• Order By, TOP, OFFSET

Ch 7: Schemas & Batches

• Schemas: Creation, Usage
• 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

Phase 2: Queries & Data Analytics
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
• UNION, UNION ALL

Ch 12: Views & RLS

• Views: Realtime Usage
• DML, SELECT with Views
• Excel Analytics with Views
• Important System Views

Ch 13: Stored Procedures – 1

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

Ch 14: Stored Procedures – 2

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

Ch 15: User Defined Functions – 1

• Scalar Functions in Real-world
• Inline & Multiline Functions
• Parameterized Queries
• Variables & Parameters
• Function Executions

Ch 16: User Defined Functions – 2

• Date & Time Functions
• String Functions & Queries
• Aggregated Functions & Usage
• Window Functions (Rank)
• Row_Number, DenseRank
• Partition By & Order 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: Group By Queries

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

Ch 19: Joins with Group By

• 3 Table, 4 Table Joins
• Join Queries & WHERE
• Join Queries & Group By
• IIF(), CASE Statement
• Query Execution Order

Phase 3: Query Optimization & Performance Tuning

Ch 20: Transactions & ACID

• Auto Commit Transaction
• Explicit Transactions
• COMMIT, ROLLBACK
• Checkpoint & Query Blocking
• READPAST, LOCKHINT

Ch 21: Indexes Basics, Tuning

• Clustered Index, Primary Key
• Non Clustered Index
• Query Optimizer
• Tuning Join Queries
• Tuning Group By Queries

Ch 22: CTEs & Tuning

• Common Table Expression
• CTEs for Data Retrieval
• CTEs for DML Operations
• CTEs for Data Cleansing
• Using CTEs with Row Number

Ch 23: Cursors

• Cursors & Fetch
• Cursor Life Cycle
• Scroll, Forward Only Types
• Local & Global Cursors
• Realtime Use

Ch 24: Temp Tables

• Purpose of Temp Tables
• Local Temp Tables
• Global Temp Tables
• Testing Temp Tables
• SELECT..INTO Statement (Bulk Copy)

Ch 25: Real-Time Case Study – Retail/E-Commerce Database

• Data Validations
• Query Writing
• Excel Analytics

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 Concepts

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
• 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 Store
• Log File Store
• Time Travel
• DESCRIBE, EXTENDED
• HISTORY
•  Version Numbers

Phase 3: Python ETL & Analytics Concepts
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

Ch 13: Python: Modules & DataFrames

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

Phase 4: PySpark, Medallion Architecture, Delta Lake, Auto Loader & Lakeflow
Ch 14: PySpark DataFrames & Transformations

• Creating PySpark DataFrames
• Reading Data Sources
• select(), filter(), withColumn()
• Handling NULL Values
• Data Type Casting
• Joins
• union() / unionByName()
• Aggregations
• Window Functions
• Writing DataFrames
• DataFrame vs Spark SQL

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
• Update, Delete Records
• 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 with the If-Else Task

Ch 20: PySpark: Auto Loader – 1

• Auto Loader Concept
• Cloud Files Architecture
• Checkpoint Configurations
• Schema Location
• Checkpoint Location
• Initial Loads

Ch 21: PySpark: Auto Loader – 2

• Reading Streams
• Manually Cancel your Data Streams
• Writing to a Data Stream
• Schema Evaluation Modes
• Workspace Modules
• Incremental File Ingestion
• Schema Evolution
• Rescued Data Column
• Trigger Options
• Exactly-once processing concepts

Ch 22: LakeFlow 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
• Liquid Clustering
• VACUUM
• OPTIMIZE, Z Ordering

Ch 24: Databricks Security, AI

• Overview of ACLs
• Adding a New User to Workspace
• Workspace Access Control
• Cluster Access Control
• Groups & Lake Bridge
• Access Keys (Tokens), Genie AI

Ch 25: GitHub Concepts

• Creating GitHub Account
• GIT Project Concept
• GIT Project Creation
• GIT: Main, Branches
• GIT Credentials
• Connecting with ADF
• Connecting with Databricks

Project Title
Retail Sales Analytics Platform using Databricks (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

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

• 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

Why Choose This Training?

• Industry-Oriented Curriculum
• End-to-End Real-Time Projects & Solutions
• Medallion Architecture Implementation
• 100% Hands-on Practical Sessions
• Production-Ready ETL Pipelines
• PySpark & Spark SQL from Basics to Advanced
• Interview Preparation
• Assignments & Practice Labs
• 100% Practical • No Unnecessary Theory
• Learn Like You’re Working in a Real Company
• EXPLORE OUR FREE DATABRICKS LEARNING SERIES

Watch practical Databricks content before you enroll:

• Technical Tutorials
• Short & Full Demo Classes
• Real-Time Project Walkthroughs
• PySpark, Spark SQL & Delta Lake
• Interview Questions & Scenarios
• Job Roles & Career Guidance

🚀Watch a Free Class Before You Decide..
Databricks Data Engineering Free Classes: https://www.youtube.com/playlist?list=PLT3eLIlc1sgg

BUILD REAL SKILLS. WORK ON REAL PROJECTS. BECOME JOB READY.

At SQL School, our focus goes beyond completing a syllabus. You will learn through hands-on
labs, production-style scenarios, end-to-end projects, assignments, interview preparation, and
certification guidance designed to build practical Databricks Data Engineering skills.

• Practical Skill Development
• Real-Time Project Guidance
• Resume & Interview Preparation
• Certification Guidance