#Power Apps

Power Apps is a powerful low-code platform from Microsoft that enables users to design, develop, and deploy custom business applications with ease. It integrates seamlessly with Power BI, Microsoft 365, and Dynamics 365, helping organizations automate workflows, improve efficiency

✅ Power Apps & Microsoft Power Platform
✅ Canvas Apps, Model-Driven Apps
✅ Data Connections with SharePoint
✅ App Design, Layouts & UI Customization
✅ Business Logic Implementation
✅ Power Automate & Power FX Integration
✅ Security, Permissions & Environment Management
✅ Integration with Power BI, Dataverse & SQL Server
✅ End-to-End Real-Time Power Apps Project
✅ Exam Prep, Mock Interviews & 1:1 Mentorship

ETL Admin course banner highlighting SQL DBA, Azure DBA, ETL, and DWH roles with salary growth visuals and logos of top companies like TCS, HCL, Accenture, Tech Mahindra, and ADP.

ETL Developer Training
Course Contents:

Brochure Download Register Now

Module 1 : Microsoft SQL (TSQL)

Ch 1: SQL SERVER INTRODUCTION

Database Introduction
Types of Databases
Need for & ETL, DWH
BI Implementations
SQL Server Advantages
Version, Editions of MSSQL
Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

SQL Server 2019, 2017
SSMS Tools Installation
Database Engine (OLTP)
SCM, Configuration Tools
Instance Types, Uses
Authentication Modes
Collation, File Stream

Ch 3: SQL BASICS – 1

Need for Databases, Tables
Need for SQL Commands
DDL, DML & DQL Statements
Database Creation @ GUI
Data Operations @ GUI
Session ID, SQL Context
DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

DDL Variants in MSSQL
DML Variants in MSSQL
INSERT & INSERT INTO
SELECT & SELECT INTO
Basic Operators in SQL
Special Operators in MSSQL
ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

Data Imports with Excel
ORDER BY & UNION
UNION ALL For Sorting Data
Creating, Using Schemas
Real-world Banking Database
Table Migrations @ Schemas
2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

Need for Constraints, Keys
NULL, NOT NULL, UNIQUE
Primary Key & Foreign Key
RDBMS and ER Models
Identity Property, Default
Clustered Index, Primary Key
Non Clustered Index, Unique

Ch 7: Joins & Views Basics

JOINS: Purpose. Inner Joins
Left / Right / Full Outer Joins
Cross Joins, Query Tuning
Creating & Using Views
DML, SELECT with Views
RLS : WITH CHECK OPTION
System Views & Metadata

Ch 8: Functions(UDF), Data Types

Using Functions in MSSQL
Scalar Value Functions
Inline & Multiline Functions
Date & Time Functions
String, Aggregate Functions
Data Types : Integer, Char, Bit
SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

Stored Procedures & Usage
Creating, Testing Procedures
Encryption, Deferred Names
SPs for Validations, Analysis
System SPs, Recompilation
Normal Forms & Types
Data Models, Self-References

Ch 10: Triggers, Temp Tables

Need for Triggers
DDL & DML Triggers
Using Memory Tables
Data Replication, Automation
Local & Global Temp Tables
Testing & Using Temp Tables
SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

Planning VLDBs : Files, Sizing
Filegroups, Extents & Types
Log Files : VLF, Mini LSN
Table Location, Performance
Schemas, Transfer, Synonyms
Transactions Types, Lock Hint
Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

Cursors : Realtime Use
Fetch & Access Cursor Rows
CTEs for SELECT, DML
CTEs: Scenarios & Tuning
Linked Servers, Remote Joins
Linked Servers: MSDTC, RPC
Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

Need for Merge in ETL
Incremental Loads with SQL
MERGE and RANK Functions
Window Functions, Partition
Identify, Remove Duplicates

Ch 14: Grouping & Cube

Group By & HAVING
Cube, Rollup & Grouping
Joins with Group By
3 Table, 4 Table Joins
Query Execution Order

Ch 15: Self Joins, Excel Analysis

Self Joins & Self References
UNION, UNION ALL
Sub Queries with Joins
IIF, CASE, EXISTS Statements
Excel Analytics, Pivot Reports

Module 2: Azure Data Engineer

Ch 1: ETL, DWH Introduction

Database Introduction Data Warehouse (DWH)
Data Engineering Work Flow
Cloud Concepts: IaaS, PaaS
SaaS & Azure Cloud Concepts
Azure Resources & Groups
Storage, ETL, IoT Resources

Ch 2: Azure Intro, Azure SQL

Azure SQL Server, SQL DBA
Azure SQL Database (OLTP)
Azure SQL Pool (DWH)
Connections from SSMS Tool
Connections from ADS Tool
Pause / Resume SQL Pool
Source Data Configurations

Ch 3: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS & Partitioned Tables
Creating Tables with TSQL
Distributions: RR, Hash, Repl
Big Data Loads with TQL
Important DMFs & DMVs

Ch 4: Azure Data Factory (ADF)

Need for ADF & Pipelines
Linked Services & IRs
Datasets, Pipelines, Triggers
Copy Data Activity & CDT
Data Loads Pipelines, DTUs
Pipeline Monitoring, Edits

Ch 5: ADF Incremental Loads – 1

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
DOCP, Logging & Consistency
Polybase Concept & Tuning

Ch 6: ADF Incremental Loads – 2

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 7: ADF Data Flow – 1

Data Flow Transformations
Spark Clusters for Debugging
Optimized Clusters, Preview
Conditional Split, SELECT
Sort, Union Transformations
Pipelines with Data Flow

Ch 8: ADF Data Flow – 2

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

Ch 9: ADF Data Flow – 3

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

Ch 10: Synapse Analytics – 1

Azure Synapse Analytics
Dedicated SQL Pools
TSQL: Stored Procedures
Synapse Pipelines, Tuning
SP Activity in Pipelines, Jobs
Comparing ADF & Synapse

Ch 11: Synapse Analytics – 2

Serverless Pools in Synapse
TSQL Scripts with Serverless
ADLS Data Imports & ELT
Synapse Aggregation, Analytics
Synapse Optimizations
Synapse Security & Logins

Ch 12: Synapse Analytics – 3

Apache Spark Pool & Usage
Synapse Analytics with Pools
PySpark Staging, Aggregations
Spark Queries & Python ETL
Python Notebooks, Pipelines
Integrating Python with DWH

Ch 13: Parameters, SCD & ETL

ADF Templates in Realtime
Table Incremental Loads
Control Tables, Watermarks
Pipeline Parameters, SPs
Dynamic Data Sets, SCD

Ch 14: CDC @ ETL, ELT & Tuning

Using CDC in ADF
Control Tables (CT): Upserts
Handling Inserts, Updates
SCD Type 1 & Type 2
ADF, Synapse: Limitations

Ch 15: Azure Intro & Storage

Storage, ETL, IoT Resources
Azure Storage Components
Azure Storage Account, HNS
Azure Data Lake Storage
Azure Storage Explorer Tool
Storage Explorer Config
Storage Account Properties

Ch 16: Azure Storage Operations

BLOB Storage: Containers
Storage Browser, Explorer
File & Folder Uploads, Edits
Azure Tables: Row Key
Partition Key, Timestamp
Use Cases of BLOB Storage
Use Cases of Azure Tables

17: Azure Storage Security

Realtime use of Keys
Access Keys & Admin Access
SAS Keys Generation, Ips
Creating, Using Entra Users
Azure AD Users, Groups
IAM & RBAC with Entra Users
ACLs and ADLS Security

Ch 18: Azure SQL DB Migrations

On-Premise SQL DB bacpac
Azure SQL Deployment
Azure Storage from SSMS
Azure SQL DB Migration
Migration Verifications
Testing Migrations in SQL

Ch 19: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 20: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 21: Azure Key Vaults, Alerts

Azure Encryptions @ REST
Azure Key Vaults & Keys
SMK & CMK Encryptions
Azure Metrics: Ingress
Egress, E2E Latency Issues
Performance Tuning Options

Ch 22: Azure Storage Optimization

BLOB Types & Content Types
Hot, Cool, Cold, Archive Types
Creating, Using Access Policies
Immutable Storage, Rotation
Containerization, Indexing
Replication: LRS, ZRS, RA-GRS

Ch 23: Azure Pricing, Functions

Azure Logic Apps: Usage
Log Apps Usage in ETL
Snapshots, Azure Functions
Azure Functions Realtime Use
ETL & DWH with Functions
Azure Resource Pricing

Ch 24: Azure Big Data & Spark

Azure Big Data & Spark
Azure ETL & DWH Databases
Azure Spark, HIVE Metastore
Azure Databricks Service
Spark Cluster (Personal)
Unity Catalog & Azure VM

Ch 25: Spark Cluster Operations

DBFS: Flat File Imports
Table Conversions using GUI
Spark Clusters: Table Creations
Basic Transformations in Spark
SQL Notebooks: Creation
Default DB Queries, Cloning

Ch 26: Python & PySpark, ETL

Python Fundamentals
Python Data frames: ETL
Python for Big Data, Pandas
Python Notebooks, Views
Aggregated Loads to Spark
Spark DB Creations, Tables

Ch 27: PySpark & ADLS, Widgets

Creating Spark Databases
Spark Tables, Catalog Info
PySpark with ADLS Storage
Using Widgets for ADLS Keys
PySpark Variables & Widgets
Using Variables in Functions
Spark SQL with Control Text
Using Variables in Spark SQL

Ch 28: ADB Jobs, Delta Tables

Azure Databrick Jobs
Azure Workflows & Tasks
Notebook Schedule Options
Continuous Jobs, Notifications
Delta Tables & Data Cleansing
SCD (Merge Into), Contact, etc.
Creating, Using Data frames
Multi Data frame Joins

Ch 29: Scala Notebooks & ETL

Scala Notebooks: Purpose
Aggregated Data Loads
Incremental Data Loads
Widgets & Jobs with Scala
Python Versus Scala
Converting Python to Scala
JVM Benefits, SQL DB Conn”
SQL DB Loads with Scala

Ch 30: Databricks Architecture

Azure Databricks Services
Cluster Components & DBFS
RDD, DAG, Photon, Spotlight
Spark Partitioned Tables
Cluster Manager: Spark Jobs
Databricks Runtime (DBR)
Databricks Security
Workspace Security
Notebook & Job Security

Ch 31: Medallion Architecture

Medallion Architecture in ETL
DWH Data Loads & Incr Loads
Bronze, Silver & Gold Data
Processing Raw Data Files
Data Cleansing, Formatting
Aggregation Advantages
DBES & Node Architecture
Unity Catalog Concept
LUNs and Unity Catalog

Ch 32: Delta LIVE Tables (DLT)

Creating Delta LIVE Tables
DLT Pipelines in ETL, DWH
Automated Incr Loads
Control Tables, Timestamp
SCD Type 1 with DLT
SCD Type 2 with DLT
Automated Merge Into Stmt
Delta Tables Vs DLT
Merge Into Vs DLT Pipeline

Module 3: Python for ETL

Ch 1. Python Introduction

Need for Data Analytics
Python in Data Analysis
History of Python
Python Versions
Python Implementations
Python Installations
Python IDE & Usage
Jupyter Notebooks

Ch 2. Python Basics, Architecture

Python Scripting Options
Basic Operations in Python
Python Scripts, Print()
Single, Multiline Statements
Adding Cells, Saving Notebook
Single, Multi Line Comments
Python : Internal Architecture
Compiler Versus Interpreter

Ch 3. Data Types & Variables

Integer / Int Data Types
Float & String Data Types
Boolean, Binary Types
Sequence Types: List, Tuple
Range, Complex & memview
Retrieving Data Type: type()
Multi Assignments & Casting
Unpack Collection, Outputs

Ch 4. Python Operators

Arithmetic, Assignment Ops
Comparison Operators
Logical, Identity Operators
Member, Bitwise Operators
Operator Precedence
If … Else Statement, Pass
Short Hand If, OR, AND
ELIF and ELSE IF Statements
Expressions, Ternary OPs

Ch 5: Python Loops, Iterations

Python Loop & Realtime Use
Python While Loop Statement
Break and Continue Statement
Using Print with While()
Iterations & Conditions
Exit Conditions & For Loops
Break, Continue & Range
__iter__() and __next__()
iter() and Looping Options

Ch 6: Python Collections

Python Collections (Arrays)
list() Constructor, print()
Python Tuples, Tuple Items
tuple() Constructor, Usage
Python Sets : Syntax Rules
Duplicates, Types, Ordered
Python Dictionaries: Usage
Changeable, Ordered Data
Dictionary Construct, type()

Ch 7: Python Functions

Python Functions & Usage
Function Parameters
Arguments, **kwargs
Default & List Parameters
Python Lambda Functions
Anonymous Functions
Recursive Functions, Usage
Return & Print @ Lamdba

Ch 8: Python Classes & Arrays

Python Classes & Objects
__init__() Function
__str__() Function
Self Parameters & Objects
Python Inheritance & Classes
Parent & Child Classes
__init__() & super() Function
Polymorphism in Python

Ch 9: Python Modules

import Python Modules
Variables in Modules
Built In Modules & dir
datetime module in Python
Date Objections Creation
strftime Method & Usage
imports & datetime.now()
Using Python Constructors

Ch 10: : Python JSON & RegEx

JSON Concepts, Usage
Dictionary & import json
Python Objects into JSON
Formatting & Ordering
json.dumps, print options
Python Regular Expressions
RegEx Module & Function
search() & span() , Strings
Using RegEx with JSON

Ch 11: Python User Inputs & TRY

Try Except, Exception Handling
NameError Resolution
Python Finally Block, Usage
Raise an exception method
TypeError, Scripting in Python
Python User Inputs
Python Index Numbers
Named Indexes, Usage
input() & raw_input()

Ch 12: Python File Handling

File Handling, Activities
r, a, w, x modes
t, b Operations
Read Only Parts
Loop, Write, Close Files
Appending, Overwriting
import os, path.exists
f.open, f.write
f.read, f.close

Ch 13: Data Analytics – Pandas

Python Modules & Pandas
Pandas Codebase & Usage
Installation of Pandas
import pandas.DataFrame
Checking Pandas Version
Pandas Series, arrays
Labels : Creation, Use
series(), print()

Ch 14: Data Analytics – DataFrames

Indexes & Named Options
Locate Row and Load Rows
Row Index & Index Lists
Load Files Into a DataFrame
pd.read_csv() Function
pd.options.display.max_rows
df.to_string() Function
tail() & null() Function

Ch 15: Data Analytics – Pandas

Pandas – Cleaning Data
Replace, Transform Columns
Data Discovery & Column Fill
Identify & Remove Duplicates
dropna(), fillna() Functions
Pandas – Data Correlations
Good & Bad Correlation
Data Plotting & matlib Lib

Ch 16: : SQL DB & Python – 1

SQL & Databases
Azure Data Studio Tool
sp_execute_external_script
Input Data & Result Sets
DDL & DML with Python
SQL_out, SQL_in
Variables & Parameters
Versions, Package List
WITH RESULT SETS Options

Ch 17: SQL & Python – 2

pandas.Series with SQL DBs
Indexing Methods in Realtime
Convert series to data frame
Output values into data.frame
pymssql package in SQL Server
pip list & Package Manager
Python runtime, Py Package
pymssql.connect & Usage
Cursor Variables & Usage

Ch 18: Realtime Case Study

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Training FAQs

Who is SQL School? How far you have been in the training services ?

SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL Server, T-SQL, SQL Server DBA and MSBI (SSIS, SSAS, SSRS) Courses. All our training services are completely practical and real-time. CREDITS of SQL School Training Center

We are Microsoft Partner. ID# 4338151
ISO Certified Training Center
Completely dedicated to Microsoft SQL Server
All trainings delivered by our Certified Trainers only
One of the few institutes consistently delivering the trainings for more than 19+ Years online as inhouse
Real-time projects in
- Healthcare
- Banking
- Insurance
- Retail Sales
- Telecom
- ECommerce

I registered for the Demo but did not get any response?

Make sure you provide all the required information. Upon Approval, you should be receiving an email containing the information on how to join for the demo session. Approval process usually takes minutes to few hours. Please do monitor your spam emails also.

Why you need our Contact Number and Full Name for Demo/Training Registration?

This is to make sure we are connected to the authenticated / trusted attendees as we need to share our Bank Details / Other Payment Information once you are happy with our Training Procedure and demo session. Your contact information is maintained completely confidential as per our Privacy Policy. Payment Receipt(s) and Course Completion Certificate(s) would be furnished with the same details.

What is the Training Registration & Confirmation Process?

Upon submitting demo registration form and attending LIVE demo session, we need to receive your email confirmation on joining for the training. Only then, payment details would be sent and slot would be allocated subject to availability of seats. We have the required tools for ensuring interactivity and quality of our services.

Please Note: Slot Confirmation Subject to Availability Of Seats.

Will you provide the Software required for the Training and Practice?

Yes, during the free demo session itself.

How am I assured quality of the services?

We have been providing the Trainings – Online, Video and Classroom for the last 19+ years – effectively and efficiently for more than 100000 (1 lakh) students and professionals across USA, India, UK, Australia and other countries. We are dedicated to offer realtime and practical project oriented trainings exclusively on SQL Server and related technologies. We do provide 24×7 Lab and Assistance with Job Support – even after the course! To make sure you are gaining confidence on our trainings, participans are requested to attend for a free LIVE demo based on the schedules posted @ Register. Alternatively, participants may request for video demo by mailing us to contact@sqlschool.com Registration process to take place once you are happy with the demo session. Further, payments accepted in installments (via Paypal / Online Banking) to ensure trusted services from SQL School™

YES, We use Enterprise Edition Evaluation Editions (Full Version with complete feature support valid for SIX months) for our trainings. Software and Installation Guidance would be provided for T-SQL, SQL DBA and MSBI / DW courses.

Why Choose SQL School

Why Choose SQL Student learning coding on laptop in a futuristic environment representing career-focused database and data engineering training at SQL School

100% Real-Time and Practical
ISO 9001:2008 Certified
Concept wise FAQs
TWO Real-time Case Studies, One Project
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance