cloud architect slider 1

#Cloud Architect

Cloud Architecture is one of the most stable and in-demand domains in the modern technology platform. Cloud systems are everywhere around us – from Banking to Healthcare, from Petro Chemicals to Retail. Organizations across the world rely on scalable and secure cloud infrastructure to run their applications, manage data, and support digital transformation.

Training Highlights

✅ 100% Practical, Job-Oriented Training
✅ Multi-Cloud Architecture with Azure, AWS
✅ End-to-End Enterprise Architecture Design
✅ Hands-on Real-Time Projects (E-Commerce)
✅ Data Engineering, Data Warehousing
✅ Industry Best Practices for Cloud Security
✅ Resume Building, Mock Interviews & Career Mentorship
✅ Certificate of Completion & Placement Assistance

Modules We Learn

✅ Module 1: SQL Server TSQL (MS SQL) Queries
✅ Module 2: Azure Data Engineer
✅ Module 3: Snowflake (Cloud ETL, DWH)
✅ Module 4: Power BI
✅ Module 5: ERWIN
✅ Module 6: Fabric Data Engineer
✅ Module 7: AWS Data Engineer
✅ Module 8: End to End Project

Course Duration: 8 Months

Cloud Architect
Course Contents:

Brochure Download Request a Callback

Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Data Engineer Job Roles

Introduction to Databases
Cloud Data Architect Job Roles
Cloud Data Architect Challenges
Cloud Data Architect technologies
Data and Databases Intro

Ch 2: Database Intro & Installations

Database Types (OLTP, DWH, ..)
DBMS: Basics
SQL Server 2025 Installations
SSMS Tool Installation
Server 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, modify, etc..
DML: Insert, Update, Delete, select into, etc..
DQL: Fetch, Insert… Select, etc..
SQL Operations: LIKE, BETWEEN, IN, etc..
Special Operators

Ch 5: Data Types

Integer Data Types
Character, MAX Data Types
Decimal & Money Data Types
Boolean & Binary Data Types
Date and Time Data Types
SQL_Variant Type, Variables

Ch 6: Excel Data Imports

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

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 – Level 1

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

Ch 9: Normal Forms & RDBMS

Normal Forms: 1 NF, 2 NF
3 NF, BCNF and 4 NF
Adding Keys to Tables
Cascading Keys
Self Referencing Keys
Data Modelling (OLTP)
ER Models

Ch 10: Joins & Queries

Joins: Table Comparisons
Inner Joins & Matching Data
Outer Joins: LEFT, RIGHT
Full Outer Joins & Aliases
Cross Join & Table Combination
Joining more than 2 tables

Ch 11: Views & RLS

Views: Realtime Usage
Storing SELECT in Views
DML, SELECT with Views
RLS: Row Level Security
WITH CHECK OPTION
Important System Views

Ch 12: Stored Procedures

Stored Procedures: Realtime Use
Parameters Concept with SPs
Procedures with SELECT
System Stored Procedures
Metadata Access with SPs
SP Recompilations

Ch 13: User Defined Functions

Using Functions in MSSQL
Scalar Functions in Real-world
Inline & Multiline Functions
Date & Time Functions
String Functions & Queries
Aggregated Functions & Usage

Ch 14: 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 15: Transactions & ACID

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

Ch 16: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
Using CTEs for DML Operations
Using CTEs for Tuning
CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

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

Ch 18: Group By Queries

Group By, Distinct Keywords
GROUP BY, HAVING
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage
Group By with UNION
Group By with UNION ALL

Ch 19: Joins with Group By

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

Ch 20: Sub Queries

Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Sub Queries with Aliases
Sub Queries, Joins, Where
Correlated Queries

Ch 21: Cursors & Fetch

Cursors: Realtime Usage
Local & Global Cursors
Scroll & Forward Only Cursors
Static & Dynamic Cursors
Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

IIF Function and Usage
CASE Statement Usage
Window Functions (Rank)
Row_Number( )
Rank( ), DenseRank( )
Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

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

Module 2: Azure Data Engineer

Section 1: ADF, Synapse

Ch 1: Azure ETL, DWH Introduction

Data Warehouse (DWH)
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 DB
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
Data Orchestration with IR
Integration Runtime Engine
Linked Services, Datasets
Pipelines: Copy Data Activity
Data Flow Activity with IR

Ch 5: Azure SQL DB Loads

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

Ch 6: BLOB Data Loads

Azure Storage Account
Azure BLOB Containers
BLOB Storage in ADF
Synapse Analytics with IR
ADF Pipeline Edits
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 Transformations
Spark Clusters for Debugging
Optimized Clusters, Preview
Conditional Split, SELECT
Sort, Union Transformations
Pipelines with Data Flow

Ch 11: 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 12: ADF Data Flow – 3

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

Ch 13: ADF Data Flow – 4

Lookup Transformation
Cache Lookup
Inline Datasets
Data Validations
Lookup Versus Joins
Broadcast Options

Ch 14: ADF Metrics, Alerts

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

Ch 15: ADF Parameters, Security

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

Ch 16: Parameters, SCD & ETL

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

Ch 17: Synapse Analytics

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

Ch 18: CI CD with GitHub

Creating Github Account
GIT: Main, Branches
Connecting with ADF
Version Changes
Builds and Deployments
CI-CD Integrations

Section 2: ADLS, loT, Migrations

Ch 1: Azure Storage Security, ADF

Access Keys & Admin Access
SAS Keys Generation, Ips
Azure AD Users, Groups
IAM & RBAC with Entra Users
ACLs and ADLS Security
ADF with Azure Storage Security

Ch 2: 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 3: Azure Tables & ADF

Azure Tables
Entities and Properties
Storage Service Operations
OData Queries & Filters
Data Loads with ADF

Ch 4: 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 Streams

Ch 5: Azure Key Vaults

Azure Encryptions at REST
SMK & CMK Encryptions
Azure Key Vaults & Keys
Key Access Policies
Rest, Transit Encryptions
Realtime Considerations

Ch 6: Azure Logic Apps

Azure Logic Apps
Consumption Logic
Standard Logic
Logic App Connectors
Triggers & Parallel Branches
Schedules & Automations

Section 3: Python, Spark, PySpark, 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
UI : Limitations

Ch 4: Unity Catalog Operations, Spark SQL – 1

Spark SQL Notebooks
Creating Catalog
Creating Schemas, Tables
Spark Data Types
Data Partitioning
Managed Tables
SQL Queries with the PySpark API
Union, Views in Spark
Dropping Objects
External Tables, External Volumes
Spark SQL Notebooks: Exports, Clone

Ch 5: Spark SQL Notebooks – 2

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 6: Python Concepts – 1

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

Ch 7: Python Concepts – 2

Python Data Types
Integer / Int Data Types
Float, String Data Types
Arithmetic, Assignment Ops
Comparison Operators
Operator Precedence
If … Else Statement
Short Hand If, OR, AND
ELIF and ELSE IF Statements

Ch 8: Python Concepts – 3

Python Lists
List Items, Indexes
Python Dictionaries
Tables Versus Dictionaries
Python Modules & Pandas
import pandas.DataFrame
Pandas Series, arrays
Indexes, Indexed Lists

Ch 9: PySpark – 1

Dataframes with SQL DB
Pandas Dataframes
Dataframe()
List Values, Mixed Values
spark.read.csv()
spark.read.format()
Filtering DataFrames
Grouping your DataFrame
Pivot your DataFrame

Ch 10: PySpark – 2

DataFrameReader
DataFrameWriter Methods
CSV Data into a DataFrame
Reading Single Files
Reading Multiple Files
Schema with an SQL String
Schema Programmatically

Ch 11: PySpark – 3

Writing DataFrames to CSV
Working with JSON
Working with ORC
Working with Parquet
Working with Delta Lake
Rendering your DataFrame
Creating DataFrames from Python Data Structures

Ch 12: PySpark Transformations – 1

Data Preparation
Selecting Columns
Column Transformations
Renaming Columns
Changing Data Types
select() and selectExpr()
Column Transformation
withColumn()

Ch 13: PySpark Transformations – 2

Basic Arithmetic and Math Functions
String Functions
Datetime Conversions
Date and Time Functions
Joining DataFrames
Unioning DataFrames
Joining DataFrames

Ch 14: PySpark Transformations – 3

Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Filtering Null Values
Grouping and Aggregating
Pivoting and Unpivoting
Conditional Expressions

Ch 15: Medallion Architecture

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

Ch 16: Delta Lake – 1

Storage Layer
Delta Table API
Deleting Records
Updating Records
Merging Records
History and Time Travel

Ch 17: Delta Lake – 2 (SCD)

Schema Evolution
Delta Lake Data Files
Deleting and Updating Records
Merge Into
Table Utility Commands
Exploratory Data Analysis
Incremental Loads
Old History Retention
Delta Transaction Log

Ch 18: Widgets

Text Widgets
User Parameters
Manual Executions
Lake Bridge
Databricks BridgeOne

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: Databricks Tuning

How Spark Optimizes your Code
Lazy Evaluation
Explain Plan
Inspecting Query Performance
Caching, Data Shuffling
Broadcast Joins
When to Partition
Data Skipping
Z Ordering
Liquid Clustering
Spark Configurations

Ch 21: Version Control & GitHub

Local Development
Runtime Compatibility
Git and GitHub Pre-requisites
Git and GitHub Basics
Linking to GitHub & Databricks
Databricks Git Folders
Project Code to GitHub
Adding Modules to the Project Code
Databricks Job Updates, Runs

Ch 22: Spark Structured Streaming

Streaming Simulator Notebook
Micro-batch Size
Schema Inference and Evolution
Time Based Aggregations, Watermarking
Writing Streams
Trigger Intervals
Delta Table Streaming Reads, Writes

Ch 23: Auto Loader

Reading Streams with Auto Loader
Reading a Data Stream
Manually Cancel your Data Streams
Writing to a Data Stream
Workspace Modules

Ch 24: Lake Flow Declarative Pipelines

Delta LIVE Tables
Data Generator Notebook
Pipeline Clusters
Databricks CLI
Data Quality Checks
Streaming Dataset “Simulator”
Streaming Live Tables

Ch 25: Security: ACLs

Overview of ACLs
Adding a New User to our Workspace
Workspace Access Control
Cluster Access Control
Groups & LakeBridge

Ch 26: Realtime Project 2 @ Ecommerce / Banking / Sales

👉 Detailed Project Requirements
👉 Project Solutions
👉 Project FAQs
👉 Project Flow
👉 Interview Questions & Answers
👉 Resume Guidance (1:1)

Module 3: Snowflake (Cloud ETL, DWH)

Ch 1: Introduction to Snowflake

Database, DWH Introduction
Cloud Data Warehouse
Cloud DWH Implementations
Snowflake Cloud Intro
Snowflake: SaaS Platform

Ch 2: Snowflake Concepts

Snowflake Account (Cloud)
Snowflake Components
Snowflake Editions, Credits
Snowflake Editions
Virtual Private Edition (VPS)
Snowflake Pricing

Ch 3: Architecture, Warehouse

Compute Architecture
Shared Disk Architecture
CPU & Memory in Clusters
Database Query & Data Cycle
ColumnStore, Virtual Warehouse
Classic UI with Snowflake
Massively Parallel Processing

Ch 4: Snowflake DB & Tables

DB Objects and Hierarchy
DB Creation with Snowflake
Snowflake Tables and Usage
Retention Time, Connections
Permanent, Transient Types
CREATE TABLE AS SELECT

Ch 5: Time Travel, Recovery

Time Travel in Snowflake
Invoking Time Travel Feature
Timestamp, Offset, Query ID
Data Recovery, TIMESTAMP
Fail Safe and UNDROP, OFFSET
Transient Tables, Real-time

Ch 6: Schemas and Session Context

Schema Creation Usage
Permanent, Transient Schemas
Managed Schemas in Snowflake
Invoking Schemas & Cloning
Session Context & Schema
Data Loading with GUI

Ch 7: Constraints & Data Types

Constraints & Validations
NULL, NOT NULL Properties
Keys & Constraints, Usage
Inline, Out Of LineConstraints
ENFORCED Constraints
Snowflake Data Types

Ch 8: Snowflake Cloning

Cloning with Snowflake
Zero Copy, Schema Cloning
Snapshot, Metadata
Accessing, Clone
Storage & Metadata Layer
Real-time Considerations

Ch 9: Snowflake Procedures

Procedures and Functions
SQL and JavaScript & CALL
Transactions & Injection
sqlText:command
Cursoring Data and Operations
Dynamic DML with SPs
RETURN, RETURNS Statements

Ch 10: Security Management

Security with Snowflake
Users & Roles in Snowflake
Privileges and Groups
Organization, Account, Users
Creating, Using Roles, Users
System Defined Roles Usage
Role Hierarchy, Dependency
RBAC & DAC in Real-time

Ch 11: Snowflake Transactions

Transaction ACID Properties
Implicit, Explicit and Auto
Durability and Data Storage
current transaction() Usage
to_timestamp_ltz and Usage
Failed Transactions with SPs
Transactions and SPs
Scoped & INNER Transactions

Ch 12: Snowflake Streams & Audits

Snowflake Streams & Usage
Streams and DML Auditing
Snapshot Creation, Offset
METADATA Options & Streams
Auditing DML Operations
Data Flow & Snowflake Streams
Streams on Transient Tables
Time Travel with Stream Tables

Ch 13: Snowflake Tasks, Partitions

Tasks, Serverless Compute
Tasks Tree: Root and DAG
Tasks Schedules and RESUME
User & Snowflake Managed
CRON Syntax with Tasks
Virtual Warehouse Concepts
Multi Cluster Warehouse

Ch 14: SnowSQL and Variables

SnowSQL Configurations
DDL, DML & SELECT
SnowSQL Command Line
Variables and Batch Process
DECLARE, LET, BEGIN & END
EXECUTE IMMEDIATE, FOR
Creating Virtual Warehouse
Writing Output to Files

Ch 15: Snowflake Partitions, Stages

Snowflake Partitions, Use
Micro Partition with DML, CDC
Cluster Key, Depth and Overlap
Internal Partition Types & Usage
List, Range and Hash Partitions
Snowflake Stages, Types
Internal and External Stages
COPY Command, Bulk Loads

Ch 16: Azure & External Stages

Azure Storage Account, BLOB
SAS: Shared Access Signature
Using SAS Key and FILE PATH
Azure Storage with BLOB
COPY INTO Command Usage
Snowflake Patterns & RegEx
File Formats: Creation, Usage

Ch 17: Snow Pipes & Incr Loads

SnowPipe Incremental Loads
Azure Queues & Integrations
Azure Active Directory
External Stage, Enterprise AD
Snow Pipes and Data Loads
Incremental Data Loads
File Format with Reg Expr

Module 4: Power BI

Ch 1: Power BI Intro, Installation

Power BI & Data Analysis
5 Design Tools, 3 Techniques
2 Hosting Solutions
Power BI with Co-Pilot & AI
Power BI Installation

Ch 2: Report Design Concepts

Basic Report Design (PBIX)
Get Data, Canvas (Design)
Data View, Data Models
Data Points, Spotlight
Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

Visual Interactions & Edits
Limitations with Visual Edits
Creating Power BI Templates
CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

Creating Groups : Lists
Creating Groups: Bins
List Items & Group Edits
Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

Slicer Visual in Power BI
Slicer: Format Options
Single Select, Multi Select
Slicer: Select All On / Off
Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

Hierarchies: Creation, Use
Hierarchies: Advantages
Drill Up, Drill Down
Conditional Drill Down
Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

Power BI Filters
Basic, Top & Advanced
Visual Filters, Page Filters
Report Level Filters, Clear Filter
Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

Power BI Bookmarks
Images: Actions, Bookmarks
Buttons: Actions, Bookmarks
Page to Page Navigations
Score Cards, Master Pages

Ch 9: SQL DB Access & Big Data

SQL DB Access, Queries
Storage Modes: Direct Query
Formatting & Date Time
Storage Modes in Power BI
Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

Charts, Bars, Lines, Area
TreeMaps & HeatMaps
Funnel, Card, Multrow Card
PieCharts & Waterfall
Scatter Chart, Play Axis
Infographics, Classifications

Ch 11: Power Query Introduction

Power Query (Mashup)
ETL Transformations in PBI
Power Query Expressions
Table Combine Options
Merge, Union All Options
Close, Apply & Visualize

Ch 12: Power Query : Table Tfns

Table Duplicate, Header Promotion
Group By Transformation
Aggregate, Pivot Operation
Reverse Rows, Count Rows
Advanced Power Query Mode

Ch 13: Power Query: Column Tfn

Any Column Transformations
Data Type Detection, Change
Rename, Replace, Move
Fill Up, Fil Down
Step Edits & Rollbacks

Ch 14: Power Query: Text, Date

String / Text Transformations
Split, Merge, Extract, Format
Numeric and Date Time
Add Column & Expressions
Expressions and New Columns
Column From Examples

Ch 15: Power Query: Parameters

Parameters in Power Query
Static Parameters, Defaults
Dynamic Dropdowns, Lists
Linking with Table Queries
Step Edits, Type Conversions

Ch 16: Power BI Cloud: Publish

Power BI Cloud Concepts
Workspace Creation, Usage
Report Publish Cloud
Report Edits in Cloud
Semantic Models & Usage

Ch 17: Power BI Cloud Dashboards

Power BI Dashboards
Dashboard Creation, Usage
Pin Visuals, Pin LIVE Pages
Add Image, Video Tiles
Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

Report Shares, Alerts
Subscriptions, Exploration
Downloads & Edits
Report Cloning in Cloud
QR Codes, Web Publish
Lineage & Metrics

Ch 19: Power BI Cloud Gateways

Data Gateways, Data Refresh
Install, Configure Gateways
Data Sources Configurations
Data Refresh & Scheduling
Gateway Optimizations

Ch 20: Power BI Cloud Apps

Power BI Apps: Creation
App Sections & Content
Audience Options
App Security & Sharing
App Updates, Favourites
App URL, End User Access

Ch 21: Power BI Report Server

SQL Server 2025 (Mandatory Installations)
Power BI Report Server
Report Server Vs Cloud
Installation, Configuration
RS Config Tool Options
Report Database, TempDB
Web Service & Server URL

Ch 22: Paginated Reports

Report Builder Tool
Paginated Report (RDL)
Report Expressions (RDL)
Tablix, Chart Wizards
Fields & Drill-Down
RDL Report Publish

Ch 23: DAX Concepts (Basics)

DAX Concepts: Intro & Realtime Need
DAX Columns: Creation, Use
DAX Measures: Creation, Use
DAX Functions: IIF, ISBLANK
SUM, CALCULATE Functions
DAX Cheat Sheet

Ch 24: DAX Quick Measures

Quick Measures in Power BI
Average & Filters
Running Totals
Star Rating Calculations
DAX Measures in Data View
DAX in Visuals
DAX in Cloud Reports

Ch 25: Data Modelling, DAX

Dimensions Tables
Fact Tables & DAX Measures
Data Models & Relations
DAX Expressions
Star & Snowflake Schemas
DAX Joins & Expressions

Ch 26: DAX Joins, Variables

CALCULATEX & Variables
COUNT, COUNTA, etc..
SUM, SUMX, etc..
SELECTED MEMEBER
Filter Context, RETRUN
Dynamic Report with DAX

Ch 27: DAX Time Intelligence

Need for Time Intelligence
Date Table Generation
Time Intelligence with DAX
PARALLELPERIOD, DATE
CALENDAR, Total Functions
YTD, QTD, MTD with DAX

Ch 28: DAX – Row Level Security

RLS: Row Level Security
Data Modelling & Roles
Verify Roles (Testing)
Add Cloud Users to Roles
Dynamic Row Level Security
Testing RLS in Power BI

Ch 29: Analytical Reports

Analytical Report Concepts
Excel Data Analytics
Excel with Power BI Cloud
SQL, AVRO, JSON Sources
Analyse in Excel (Cloud)
Excel Reports to Cloud

Ch 30: Power BI AI, CoPilot

AI Components in Power BI
CoPilot Practical Uses
CoPilot with Desktop
CoPilot with Cloud
Need for AI Analytics (Fabric)
PL 300 Exam (Microsoft Certified Data Analyst) Guidance
PL 300 Exam Mocks

Module 5: ERWIN

Ch 1: Introduction

Data Modeling Concepts
Data Modeling Methods
Typographical Conventions
ERWIN Tool Installations

Ch 2: Information Systems, Data Modeling

Data Modeling Concepts
Data Modeling Sessions
Session Roles
IDEFX1 Modelling Methodology

Ch 3: Modeling Architecture

Modeling Architecture
Logical Modeling
Physical Modeling
Entity Relationship Diagrams
Key Based Models
Transformation Models

Ch 4: Logical Modeling

Local Models Construction
ER Diagrams
Entities & Attributes
Many to Many Relationships
Design Validations
Data Models in Realtime

Ch 5: Key Based Data Models

Key Types
Entity, Non Key Areas
Primary Key Selection
Alternate Key Attributes
Inversion Entry Attributes

Ch 6: Relationships in ER Model

Relationship, Foreign Key Attributes
Dependant, Independent Attributes
Identifying Relations
Non Identifying Relations
Role Names

Ch 7: Naming Conventions

Entity Names
Attribute Names
Synonyms
Hamonyms
Aliases
Entity Definitions
Definition References
Circularity
Business Glossary Construction
Attribute Definitions
Validation Rules
Role Names
Definition and Business Rules

Ch 8: Relationships

Relationship Cardinality
Referential Integrity (RI)
Additional Relationship Types
Many to Many Relationships
N-ary Relationships
Recursive Relationships
Subtype Relationships
Inclusive Relationships
Exclusive Relationships
IDEF1X, IE SubType Notation

Ch 9: Normalization Issues, Solutions

Normal Forms Concepts
Common Design Problems (CDP)
Repeating Data Groups
Repeating Attributes
Repeating Facts
Conflicting Facts
Derived Attributes
Missing Information
Unification
Normal Forms Support Levels

Ch 10: Physical Data Models

Physical Data Models Concepts
Physical Data Models Components
Logical versus Physical Data Models
Dependant Entities Classifications
DBMS Models
DWH Models
SQL DB Configurations

Module 6: Fabric Data Engineer

Ch 1: Fabric Introduction

Need for Fabric, Big Data
Fabric Data Engineering Model
Fabric Components (Items)
Microsoft Fabric: Advantages
Cloud Warehouse Uses
Benefits of Fabric Over Azure
Azure Versus Fabric DWH

Ch 2: Fabric Account, Workspace

Need for Fabric Workspace
Workspace Creation Process
Pins and New Items
Item Categorization
ETL, Storage, Analytical
Streaming, Monitoring
Compute & Separation

Ch 3: Fabric 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

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

Ch 6: SSMS Connections

Warehouse SQL Connection
Database Engine Server
Multi Factor Authentication
Warehouse Artifacts
Executing .SQL Scripts
Testing Fabric Artifacts

Ch 7: 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 8: Fabric Statistics

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

Ch 9: 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 10: Aggregated Data Store

Options for Data Aggregations
Save As table, Save As View
Single Table Aggregations
Multi Table Aggregations
Dynamic Conditions
Parameterized Aggregations

Ch 11: 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 12: Fabric Security

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

Ch 13: Fabric Data Factory

ETL Implementation Options
Need for Fabric Data Factory
ETL Operations in FDF
Data Sources, Transformations
Data Destinations (Sinks)
Creating Pipelines

Ch 14: Fabric Pipelines

Activities and Connections
Gateways & OnPrem Access
Data Sets & Activity Sets
Data Activator & Alerts
Run ID & Monitoring
Pipeline Creation, Verification

Ch 15: Fabric Pipelines Design

Creation Options for Pipelines
Azure SQL DB Data Loads
Creating Data Sets
RRR Transformations
Copy Command Usage
Internal Staging (Workspace)

Ch 16: 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 17: ETL Staging

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

Ch 18: OnPrem Gateways

Need for On_Premi Gateway
Installing & Configuring
Authentication, Usage
OnPremises Connections
Pipelines for Data Loads
Warehouse Data Storage
Data Refresh with Gateways

Ch 19: Fabric Lakehouse

Need for Fabric Lakehouse
Files and Tables Storage
Data Sources: Parquet Files
Transformation Options
Direct Lake Concepts
Lakehouse Consumption
Lakehouse Real time Use

Ch 20: Lakehouse File Loads

Creating Lakehouse
Copy Data Wizard
Azure SQL Database Source
File Data Loads in Lakehouse
Concurrency & Batch Count
Pipeline Execution Tests
Pipeline Monitor Check

Ch 21: Aggregated Data Loads

Aggregated Data Store
Plan & Design Aggregations
Testing Aggregations
Pipelines for Data Compute
Data Copy Options
Pipeline Optimizations
Data Loads and Verification

Ch 22: MultiTable Loads in LH

Table Loads Connections
Data Load in Lakehouse
Using Copy Data Wizard
Data Store in Lakehouse
View Run History, Executions
SQL End Points & Access
Lakehouse Schemas

Ch 23: Lakehouse Visual Queries

Visual Query Interface
Visual Editor & Tables / Views
Merge, Remove, Sort Tfns
Data Preview, Save As Table
Save As View : Advantages
Using Schemas, Identifiers
TDS Packets & Transfer Units

Ch 24: File Explorer

Installing One Lake Explorer
Autocreation of Folders
Workspace Directories
Warehouse Directories, Logs
Lakehouse Folders, Files
Lakehouse Uploads
Explorer Tool Limitations

Ch 25: Power Query Level 1

Power Query Concept
Need for Power Query
Data Flow Gen 1
Data Flow Gen 2
Power Query Items
Differences with Copy Activity
ETL, ELT Process

Ch 26: Power Query Level 2

Data Flow Gen2 Operations
PQ Online Editor
Working with Binary Content
Detailed Data Options
Data Cleansing Options
Step Names, Aggregations
Warehouse Data Loads

Ch 27: Power Query Level 3

Binding Power Query Steps
Edit / Delete Steps
Optimizing Power Query
ETL & ELT with Power Query
Advanced Editor
M Language Expressions
Duplicate / Reference Queries

Ch 28: Fabric Notebooks

Need for Notebooks
Fabric Notebook Types
Get / Prep / Analyze
Sessions, Markdown Folding
Standard, High Concurrency
Magic Command
Freeze Cells

Ch 29: Spark SQL Notebooks

Creating Environment
Creating Spark Clusters
Spark Cluster Compute
SQL Analytics in Notebooks
Visual Query Vs SQL
Cell Execution Options
Magic Command Usage

Ch30: PySpark Notebooks

Creating / Using Environment
PySpark Notebook Sessions
Reading Source Data
Data Prep & Aggregations
Data Loads, Analytics
Cell Execution Options
Markdown Cells

Ch 31: StreamHouse, KQL

Need for Stream House
Auto creation of KQL
Manual KQL Databases
Verification & Usage
Differences with Warehouse
Differences with Lakehouse

Ch 32: KQL Query Sets

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

Ch 33: Fabric Data Activator

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

Ch 34: Model Layouts

Need for Layouts
Creating Model Layouts
Adding Refences, Keys
Power BI Semantic Models
Creating Report Items
Using Power BI Desktop

Module 7: AWS Data Engineer

Ch 1: LINUX Introduction

Client-Server Architecture
GUI vs CLI
Navigating through CLI
Basic commands
File System Hierarchy
Help commands

Ch 2: File Hierarchy System

Relative Path Concepts
Absolute Path Concepts
Common File Types
Regular files
Directories, Links
Realtime Usage

Ch 3: File Management

Create Files, Directories
touch and mkdir
Directory Operations
Commands & Usage
File Editing Options
Text Editors (vim)

Ch 4: Basic User Management

User Login Activity
Viewing login records
Local User Authentication
/etc/passwd, /etc/shadow
useradd, usermod, userdel
Custom config & Profiles

Ch 5: Cloud Computing

Cloud Architecture & Use
Cloud Computing Concepts
Cloud Implementation Models
Public, Private, and Hybrid
AWS Cloud: Properties
AWS Cloud: Advantages
AWS Cloud: Usage Scope

Ch 6: AWS Concepts

AWS Free Tier Account
Account setup
AWS Initial Configuration
AWS Global Infrastructure
Overview of Regions
Availability Zones, Edges

Ch 7: Compute

Creating EC2 Instances
Instance types, AMIs
Instance Launch Options
Security Groups, Ports
SSH Overview, Key Pairs
Key pair creation and SSH
Private vs Public vs Elastic IP

Ch 8: Security & IAM

IAM Introduction
Core IAM Architecture
Managing Users & Groups
Creating and managing IAM
Group Policies, Inline Policies
Difference and use cases
AWS Cloud Shell, IAM

Ch 9: EC2 Instance Storage

EBS : Elastic Block Store
Managing EBS Volumes
Volume Usage Options
EBS Snapshots & Usage
Cross-AZ, Replication
EBS Encryption
Amazon Machine Images

Ch 10: S3 Storage Service

S3 Buckets and Objects
S3 Usage Management
S3 Versioning, Policies
Access Control
Static Website Hosting
S3 Storage Classes
Automation,EFS Concepts

Ch 11: Cloud Network & VPC

Introduction to Networking
CIDR : Notation, Usage
Public, Private Subnets
Subnet Creation Options
Public and Private VPCs
VPC setup & Configuration

Ch 12: Cost Management

AWS Budgets Overview
Budget Management
Cost Management Tools
AWS Cost Explorer
Cost / Pricing Reports
Real-time Strategies

Ch 13: CloudWatch

Metrics
Dashboards
Alarms
Logs
Events (basics)

Ch 14: AWS Kinesis – 1

Amazon Kinesis
Realtime Data Streaming
Amazon Kinesis Data streams
Creating Data Stream
Enhanced Fan-Out
Lambda function & Kinesis

Ch 15: AWS Kinesis – 2

Kinesis Firehose
Data Firehose Stream
Firehose – Transformations
Firehose with Lambda
ETL Implementations
Data Streaming

Ch 16: RDS DB Database – 1

Database on EC2 instance
Introduction to RDS
RDS Networking and Subnet
Create a VPC for RDS
RDS Subnet Group
Create an RDS Instance
View an RDS Instance

Ch 17: RDS DB Database – 2

RDS Usage in OLTP
RDS Backups and Snapshots
Restore RDS from Backup
Share RDS Snapshots
RDS Encryption in Transit
Delete an RDS Instance

Ch 18: RDS DB Database – 3

Authenticating to RDS
Credentials, IAM
Secrets Manager
RDS Parameter Groups
RDS Proxy, Multi-AZ RDS
RDS Read Replicas

Ch 19: Amazon Redshift – 1

Redshift overview
Redshift Serverless
Provisioned Cluster
Architecture Overview
Clusters & Nodes
Create Redshift Cluster
Access Redshift Cluster
Query Editor, Node Types

Ch 20: Amazon Redshift – 2

Storage, Resizing Methods
Snapshots & Sharing
Resizing Snapshots
Redshift – VACCUM
Load Data From S3
Unload Data
Federated Queries
Redshift Spectrum

Ch 21: Amazon Redshift – 3

AWS RedShift Security
AWS RedShift Connections
Authentication Types
Optimization Options
Data Load Operations
Data Load Requirements
Transformations with ELT

Ch 22: Amazon Redshift – 4

Need for AWS Lamda
Need for AWS Glue
Need for AWS Athena
AWS Redshift Tuning
AWS RedShift Connections

Ch 23: Lambda Introduction

What is serverless
AWS Lambda Introduction
AWS Lambda for Python
AWS Lambda Python code
Packages and Deployments
AWS Lambda configuration
AWS Lamda Settings

Ch 24: Lambda Implementation

AWS Lambda Layers
Python with Lamda
Java with Lambda
AWS Lambda – S3
Event Notifications in AWS
API Gateway, Aliases
AWS Lambda – Snapstart

Ch 25: AWS Athena

Athena overview
Query data using Athena
Federated Queries
Performance and cost
Workgroups
Workgroups (Hands-on)
Querying with Athena

Ch 26: AWS Glue – 1

AWS Glue overview
Need for AWS Glue
AWS Glue Usage Scope
Setting up Crawlers
AWS Glue Costs
AWS Budgets

Ch 27: AWS Glue – 2

Stateful vs Stateless
Stateless Data Ingesting
Glue Transformations (ETL)
Glue Data Quality
Glue workflows
Scheduling Crawlers & ETL

Ch 28: AWS Glue – 3

Default Classifiers
Custom Classifiers
Glue Triggers
Run the pipelines using CloudFormation

Ch 29: EMR & Spark

EMR concepts
EMR in AWS Big Data
Spark Concepts
PySpark & ETL
PySpark & DWH
End to End Integrations

Module 8: End to End Project

👉 Project Requirement (Ecommerce / Banking)

👉 Project Solution (Detailed)

👉 Project FAQs, Resume Guidance

SQL SCHOOL vs Other Institutes

Training Modes

LIVE Online Training

Instructor Led

Self Paced Videos

On-Demand

Corporate Training

With 100% Hands-On

Why Choose 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

A man smiling and giving a thumbs up while holding a notebook.

Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support
Realtime Project Solution
MS Certification Guidance