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Trainer Sai Phanindra

Meet Your Trainer – Mr. Sai Phanindra
Mr. Sai Phanindra is the Chief Trainer at SQL School with 20+ years of real-world IT experience in Data Engineering, Business Intelligence, and Database Technologies. He has successfully trained thousands of professionals and helped them build successful careers in leading MNCs.
He specializes in delivering 100% practical, project-based training in:

Microsoft Power BI
Azure Data Engineering
Fabric Data Engineer
Databricks Data Engineer
SQL Server (MSSQL & T-SQL)
SQL Server DBA (Administration)

His training focuses on real-time projects, industry best practices, interview preparation, certification guidance, and job-ready skills to help learners confidently succeed in today’s data-driven industry.

Trainer Profile

Training Highlights

✅ Cloud ETL, DWH with Big Data Analytics
✅ Azure Data Factory (ADF) for ETL
✅ Azure Synapse For DWH, Analytics
✅ Azure Stream Analytics For IoT, Insights
✅ Azure Key Vault, RBAC For Security
✅ Azure Databricks for ETL, ELT, Analytics

Modules We Learn
✅Module 1: SQL Server (MSSQL) & T-SQL
✅Module 2: Azure Data Engineering
✅Module 3: Integrations, DevOps
✅Module 4: End-to-End Industry Project
✅Module 5: Azure Certifications
✅Module 6: Microsoft Fabric

An Azure Data Engineer designs and manages modern data solutions on Microsoft Azure. At SQL School, you’ll master ADF, Azure Synapse, ADLS, Azure Databricks, Apache Spark, PySpark, and Microsoft Fabric through hands-on projects, expert-led training, certification guidance, and interview preparation to become job-ready.(sqlschool.com)

Who Should Join?
✅Freshers
✅SQL Developers
✅ETL Developers
✅BI Developers
✅Database Administrators
✅Data Analysts
✅Software Engineers

Prerequisites
✅No Azure experience required
✅SQL knowledge is helpful but not at all mandatory.
We start from the basics and build your skills step by step.

Azure Data Engineer
Course Contents:

Brochure Download Request a Callback

Module 1: SQL Server (MSSQL), T-SQL

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
pecial 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 with Excel
Important System Views

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, Tuning

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
.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

Module 2: Azure Data Engineer

Part 1: Fundamentals, ADF & Synapse

Ch 1: Azure Fundamentals

Cloud Introduction
Azure Concepts
Cloud Implementations: IaaS, PaaS, SaaS
Azure Account, Subscription
Azure Resources & Resource Groups
Azure ETL & DWH Resources
Azure Storage, IoT Resources

Ch 2: Azure Deployments, Azure SQL

Azure SQL Server, SQL DB
Azure SQL Database (OLTP)
Azure SQL Pool (DWH)
Connections from SSMS Tool
Source Data Configurations

Ch 3: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS (Data Movement Service)
Connection Strings
Pause / Resume SQL Pool
Scale Up / Scale Down

Ch 4: Azure SQL Pool Operations (DWH)

Creating Tables with TSQL
Partitioned Tables
Distributions
DOP Concept
Big Data Loads with TSQL

Ch 5: Azure Data Factory (ADF)

Need for ADF & Pipelines
Data Orchestration with IR
Integration Runtime Engine
Linked Services, Datasets
Pipelines: Copy Data Activity
Data Flow Activity with IR

Ch 6: Azure SQL DB Loads

ADF: Author, Azure SQL DB Reads
Azure SQL Pool Writes
Synapse Analytics with IR
Pipeline Design, Validation
Pipeline Runs, Monitoring

Ch 7: Pipeline Settings

ADF Pipeline Settings
Staging: Advantages
Reliable Logging
Best Effort Logging
DIU & DOCP with IR
Compressions, Health Check

Ch 8: File Incremental Loads

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
Initial, Incremental Loads
Schema & Data Changes

Ch 9: Table Incremental Loads

Implement SCD with ADF
Self Hosted IR: Realtime Use
On-premise Data: Incr Loads
Copy Method: Upsert, Keys
Staging & ADF Optimizations
Pipeline Runs, Activity IDs

Ch 10: ADF: Data Flow – 1

Data Flow Concepts
Data Flow Protypes
Data Flow Workflow
Data Flow Transformations
Spark Clusters
Optimized Clusters, Preview
ADF Debug Options

Ch 11: ADF Data Flow – 2

Creating Data Flow Items
Using Multiple Sinks
Conditional Split Transformation
SELECT Transformation
Sort, Union Transformations
Pipelines with Data Flow

Ch 12: ADF Data Flow – 3

Working with Multiple Tables
Join Transform, Broadcast
Row Filters, Column Filters
Surrogate Keys, Derived Cols
ETL Loads Dates, Sink Options
Aggregated Data Loads

Ch 13: ADF Data Flow – 4

Pivot Transformation
Group By & Pivot Keys
Column Pattern, Deduplicate
Lookup, Cached Lookup
Tuning Transformations
Tuning Data Flow, Spark

Ch 14: ADF Data Flow – 5

Lookup Transformation
Cache Lookup
Inline Datasets
Data Validations
Lookup Versus Joins

Ch 15: ADF Metrics, Alerts

Azure Insights
Azure Metrics for ADF
Azure Metrics for Synapse
CPU, Memory Metrics
Alerts and Notifications
Action Groups, Tuning Options

Ch 16: ADF with Azure Functions

Azure Functions
Function Activity in ADF
Linked Services
Pipeline Debug
ADF Activity Controls

Ch 17: ADF Optimizations

Synapse SQL Pool Partitions
ADF Partitions
Broadcast Options
Staging, Logging
DIU, DOCP
Spar Cluster Optimizations

Ch 18: ADF Parameters, Security

Linked Service Parameters
Creating Logins
Users and ETL Permissions
Parameterize Logins
Parameterize Users
Dynamic Linked Services

Ch 19: SCD & ETL with Control Tables – 1

ADF Templates in Realtime
Implementing ADF SCD
Table Incremental Loads
Creating Control Tables
Creating Watermark Columns
Creating ETL Stored Procedures

Ch 20: SCD & ETL with Control Tables – 2

ADF Lookup Activity
Delta Data Expressions
SP Activity & Parameters
Control Tables, Watermarks
Pipeline Parameters, SPs
Dynamic Data Sets, SCD

Ch 21: Synapse Analytics

Azure Synapse Analytics
Synapse Deployments
Synapse Configurations
ADLS Containers
Workspace Server Setup
Synapse Studio (GUI)

Ch 22: Synapse: Dedicated SQL Pools

Dedicated SQL Pools
BLOB Data Imports
Data Source Creations
TSQL Queries
Big Data Analytics

Ch 23: Synapse: Serverless Pools

Serverless Pools
Serverless Architecture
Serverless Vs Dedicated Pools
BLOB Data Imports
OPENROWSET Operations
Big Data Analytics

Ch 24: Synapse: Apache Spark Pools

Apache Spark Pools
Spark Cluster Concepts
Nodes and Executors
PySpark Notebooks
Notebook Operations
BLOB Data Imports
Big Data Analytics
Pipeline Integrations

Ch 25: CDC in ADF

CDC: Change Data Capture
Using CDC in ADF
CDC Source Configurations
Incremental Loads with CDC
New Rows, Net Changes
CDC Advantages & Performance

Part 2: Databricks

Ch 1: Databricks Introduction

Cloud ETL, DWH
Cloud Computing
Databricks Concepts
Big Data in Cloud
LakeHouse & Spark Compute

Ch 2: Databricks Architecture

Unity Catalog, Volume
Spark Clusters
Apache Spark and Databricks
Apache Spark Ecosystem
LakeHouse Architecture
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
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
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
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
Dataframes, Temp Views
Aggregated Data Loads
Gold Data Consumption

Ch 17: Delta Lake

Databricks Delta Lake
Schema Evolution
Azure SQL DB Connections
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

Auto Loader Concept
Cloud files 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: AI Assisted Cloud Data Engineering 

GitHub Copilot
Databricks Genie
Genie Assistant (AI)
AI Assisted ETL Development

Ch 26: Azure Databricks

Azure Cloud Concepts 
Azure Databricks 
Azure Regions, Pricing Tiers 
Azure Databricks Workspace 
Classic Deployment 
Driver Nodes, Worker Nodes 
DBR Versions, RDD & DAG 
Open Source Databricks Vs Azure Databricks

Ch 27: Databricks Data Engineer Associate Exam

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

Module 3: Integrations, DevOps for Azure Data Engineering

Ch 1: Azure Databricks with Data Factory

Connecting ADF with Databricks
ADF: Notebook Activity
Comparing ADF with Databricks
When to use ADF?
When to use Databricks?
How to use Databricks and ADF together?

Ch 2: GitHub Concepts

Creating Github Account
GIT Project Concept
GIT Project Creation
GIT: Main, Branches
Connecting with ADF
Connecting with Databricks

Ch 3: Azure DevOps For Data Engineers

Azure DevOps Repos
Azure Boards
Azure Pipelines
Release Pipelines
CI/CD for ADF
CI/CD for Databricks
Environment Promotion (Dev, QA, UAT, Prod)

Module 4: End-to-End Industry 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 Data Factory (ADF)
Azure Data Lake Storage (ADLS Gen2)
Azure Databricks (Apache Spark)
Azure SQL Database
Azure Blob Storage
Azure Monitor
Azure Purview
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
ADF Pipelines
Synapse Analytics
PySpark
Power BI Reporting
Monitoring
Alerting
CI/CD
Deployment
End to End Integrations

Module 5: Azure Certifications

1. Azure Data Fundamentals (DP 900)

Exam Pattern
Exam Q & A, Scenarios

2. Azure Databricks (DP 750)

Exam Pattern
Exam Q & A, Scenarios

3. Databricks Data Engineer Associate Exam

Exam Pattern
Exam Q & A, Scenarios

Module 6: Microsoft Fabric for Data Engineering

Microsoft Fabric Concepts

Fabric Architecture
Fabric ETL Components
Fabric One Lake Components
Fabric Analytics Components

Microsoft Fabric Implementation

Fabric Workspace
Fabric Warehouse Creation
Fabric Lakehouse Creation

Microsoft Fabric Migrations

Azure SQL Pool to Fabric Migrations
Azure Data Factory with Fabric Pipelines
Azure Versus Fabric Implementations

What is the Azure Data Engineer course and who should join this program?

This course is designed for Data Engineers, Developers, Analysts, Architects, and anyone who wants to build end-to-end data pipelines using Azure services like ADF, Databricks, Data Lake, Synapse, and Power BI. It covers complete ETL, ELT, DWH, Big Data, and Analytics workflows.