#Microsoft Cloud Engineer

Microsoft Cloud Engineer is a stable and high-demand job role responsible for designing, deploying, and managing scalable cloud-based data and application platforms using Microsoft Azure. This promising career stream involves integrating data and services from multiple sources, building secure cloud architectures, and managing modern data environments.

✅ Module 1: MSSQL & TSQL, Tuning
✅ Module 2: Azure Data Engineer
✅ Module 3: Fabric Data Engineer
✅ Module 4: Realtime Projects, Cerifications
⏳ Duration: 3.5 – 4 Months

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Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: Data Engineer Job Roles

Introduction to Data
Data Engineer Job Roles
Data Engineer Challenges
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 – Level 2

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

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
Parameterized Queries
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

Ch 24: Key Take-Aways from Module 1

Case Study 1: Medicare Scenario
Case Study 2: Ecommerce Scenario

Module 2: Azure Data Engineer (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

Module 3: Azure Migrations, IoT & Key Vaults

Ch 1: Azure Storage Security, IAM

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

Module 4: Databricks with Auto Loader, DLT, PySpark

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 Transformations
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 GitHub and 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 and Watermarking
Writing Streams
Trigger Intervals
Delta Table Streaming Reads and 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 5: Fabric Concepts, DWH, Data Factory

Ch 1: Fabric Introduction

Need for Fabric, Big Data
Fabric Data Engineering Model
Fabric Components (Items)
Microsoft Fabric: Advantages
Cloud Warehouse & AI
AI with CoPilot
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

Fabric Lakehouse, AI
Files and Tables Storage
Data Sources: Parquet Files
Transformation Options
Direct Lake & AI
Lakehouse with AI
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: Data Factory Pipeline Tuning

Intelligent Throughput
DOCP & Optimizations
Staging & In-Memory
Reliable Logging
Spark Compute Options
Concurrent Connections
ETL Partitions in Real-world

Module 6: Data Flow, KQL, Models

Ch 1: 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 2: Power Query Level 1

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

Ch 3: Power Query Level 2

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

Ch 4: 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 5: Stream House, KQL

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

Ch 6: KQL Query Sets

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

Ch 7: Fabric Data Activator

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

Ch 8: Model Layouts

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

Module 7: Python, Spark, PySpark, DWH

Ch 1: Fabric Notebooks

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

Ch 2: 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

Ch 3: Spark SQL – 1

Spark SQL Notebooks
Creating Schemas, Tables
Spark Data Types
Data Partitioning
SQL Queries with the PySpark API
Union, Views in Spark
Dropping Objects

Ch 4: Spark SQL – 2

Spark Joins
Aggregations
Math, Sort Functions
String, DateTime Functions
Conditional Statements
SQL Expressions with expr()
Spark SQL Aggregations

Ch 5: Spark SQL – 3

Spark Time Travel
Data Recovery & Undo
Version Number
Describe
Describe Extended
Timestamp As Of Concept

Ch 6: Python Dataframes

Pandas Module (Python)
Dataframes from Lists
Dataframe from Dict
Pandas Dataframes
Dataframe print, display
Dataframe from Files
spark.read.csv()
spark.read.format()

Ch 7: Medallion Architecture

Medallion Concepts
Bronze, Gold and Silver
Raw Data
Data Preparation (Prepping)
Temporary Views
Aggregated Data Flow
Big Data Analytics

Ch 8: PySpark: Medallion Loads – 1

Reading from Volumes
Dataframes, Temp Views
Data Prep (Silver)
Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Spark SQL Dataframes
Gold Layer Implementation
Testing Aggregated Loads

Ch 9: PySpark: Medallion Loads – 2

Azure SQL DB Connections
SQL Queries in PySpark
Data Prep (Silver)
Filtering Null Values
Grouping and Aggregating
Spark SQL Dataframe
Gold Layer Implementation
Testing Aggregated Loads

Ch 10: PySpark: SCD

Slowly Changing Dimension
Parquet Versus Delta
Deleting and Updating Records
Table Utility Commands
Merge Into Statement
Incremental Loads, Temp Views
Merge with OLTP Data Sources

Ch 11: PySpark: Widgets

Text Widgets
User Parameters
Manual Executions
Parameters & JSON

Module 8: Realtime Project with AI, CoPilot

👉 Azure Synapse Migrations

👉 DP 700 Exam Guidance

👉 Databricks Data Engineer Associate Exam Guidance

👉 End to End Realtime Project with Power BI Integrations

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Cloud Engineer Training FAQ's

What is Cloud Engineer Job Role?

A Cloud Engineer is responsible for designing, deploying, managing, and supporting cloud infrastructure and services across platforms like Azure, AWS, GCP, and hybrid clouds. The role involves working on cloud architecture, security, automation, monitoring, migration, and optimization. Cloud Engineers ensure that organizations’ applications and data operate securely, reliably, and cost-effectively in the cloud.

What are the Job Roles of a Cloud Engineer?

💼 Top Job Roles:

1️⃣ Design and deploy scalable cloud infrastructure and solutions
2️⃣ Manage and automate cloud resources using tools like Terraform, ARM, CloudFormation
3️⃣ Configure security controls, identity management, and network settings
4️⃣ Monitor, troubleshoot, and optimize cloud workloads
5️⃣ Migrate on-premises systems to the cloud
6️⃣ Ensure cloud compliance, backup, and disaster recovery and more..!

What does our Cloud Engineer Training course contains?

The course is carefully curated with below module:
👉🏻Module 1: Azure Data Engineer
👉🏻Module 2: Fabric Data Engineer
👉🏻Module 3: AWS Data Engineer
👉🏻Module 4: Snowflake Data Engineer