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#Azure Data Engineer

Azure Data Engineer is a stable job role responsible for design of Data Warehouses (DWH). This ever promising job stream involves Extraction (E) of data from various sources, perform data mashup and Transformations (T) and Loading the data (L) into Warehouse and Lakehouse platforms.

✅ 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
✅ Python ETL, PySpark with Optimizations
✅ CI/CD Pipelines, Medallion Architecture
✅ Delta LIVE Tables, Serverless Pools
✅ End to End Real-time Project
✅ 1:1 Mentorship, Resume Guidance

Azure Data Engineer
Training Course Contents:

Module 1 : Microsoft SQL (TSQL)

Ch 1: Database Intro & Job Roles

  •  Database Introduction
  •  Database Types: OLTP, DWH
  •  DBMS & Realtime Use
  •  DBMS Software & Purpose
  •  SQL : Purpose & Use
  •  SQL Server Versions, Editions
  • Job Roles & Responsibilities

Ch 2: SQL Server Installations

  • SQL Server 2022 Installations
  • SQL Server 2019 Installations
  • SSMS Tool Installation
  • Server Connections, Properties
  • Instance & Instance Types
  • Authentication Types
  • System Databases & Purpose

Ch 3: SQL Basics V1 (Commands)

  • Database, Tables & Columns
  • SQL Basics: Purpose
  • DDL Statements
  • DML Statements
  • DQL Statements
  • Verifications @ GUI
  • Basic SELECT Queries

Ch 4: SQL Basics V2 (Operators)

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

Ch 5: Excel Data Imports

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

Ch 6: Schemas & Security

  • Schemas: Creation, Usage
  • Schemas & Table Grouping
  • Using Default Schema
  • Real-world Banking Database
  • Table Migrations @ Schemas
  • 2 Part, 3 Part & 4 Part Naming
  • Verifying Schemas in UI

Ch 7: Constraints & Keys Basics

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

Ch 8: Indexes Basics, Tuning

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

Realtime Case Study 1

Ch 9: Joins Basics

  • Joins: Table Comaparisons
  • Inner Joins & Matching Data
  • Outer Joins: LEFT, RIGHT
  • Full Outer Joins & Audits
  • Cross Joins & Table Combinations
  • Joining more than 2 tables
  • Joining Tables with Aliases

Ch 10: Views & RLS

  • Views: Realtime Usage
  • Storing SELECT in Views
  • DML, SELECT with Views
  • RLS: Row Level Security
  • WITH CHECK OPTION
  • Database Audits & Metadata
  • Important System Views

Ch 11: Stored Procedures

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

Ch 12: User Defined Functions

  • Using Functions in MSSQL
  • Scalar Value Functions
  • Inline & Multiline Functions
  • Parameterized Queries
  •  Date & Time Functions
  • String Functions & Queries
  • Aggregated Functions & Usage

Ch 13: Triggers & Automations

  • Need for Triggers
  • DDL & DML Triggers
  • For / After Triggers
  • Instead Of Triggers
  • Memory Tables with Triggers
  • Data Replication, Automation
  • Disabling DMLs & Triggers

Ch 14: Transactions & ACID

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

Ch 15: Cursors & Fetch

  • Cursors: Realtime Usage
  • Cursor Declaration Types
  • Open Cursor, Close Cursor
  • Local & Global Cursors
  • Scroll & Forward Only Cursors
  • Static & Dynamic Cursors
  • Fetch, Absolute Cursors

Ch 16: CTEs & Tuning

  • CTE: Common Table Expression
  • Creating and Using CTEs
  • CTEs and In-Memory Processing
  • Using CTEs for DML Operations
  • Using CTEs for Data Retrieval
  • Using CTEs for Tuning
  • CTEs For Duplicate Row Deletion

Realtime Case Study 2

Ch 17: Relations, Normal Forms

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

Ch 18: Self Joins, EXISTS

  • Joining same table
  • Correlated Queries
  • Joining Tables, Queries
  • Self Joins with WHERE
  • Self Joins with UNION
  • Self Joins with Order By
  • Self Joins with Views

Ch 19: Remote Joins

  • Working with Multiple Servers
  • Multi Server Access from SSMS
  • Linked Servers Creation, Tests
  • 4 Part Naming Convention
  • Remote Data Access
  • RPC & RPC OUT
  • Remote Joins & Data Analysis

Ch 20: Sub Queries

  • Sub Queries Concept
  • Sub Queries & Aggregations
  • Joins with Sub Queries
  • Sub Queries with Aliases
  • Sub Queries with OrderBy
  • Sub Queries with WHERE
  • Sub Queries, Joins, Where

Ch 21: 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 22: Joins with Group By

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

Ch 23: Data Types & Conversions

  • Integer Data Types
  • Character, MAX Data Types
  • Decimal & Money Data Types
  • Boolean & Binary Data Types
  • Date and Time Data Types
  • Table, SQL_Variant Types
  • Cast( ) and Convert( ) Functions

Ch 24: Window Functions, CASE

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

Realtime Case Study 3

Module 2: Azure Data Engineer

Part 1: ADF & Synapse

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

Part 2: Storage, ADLS & IoT

Ch 1: 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 2: 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

Ch 3: 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 4: 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 5: 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 6: Azure Key Vaults

  • Azure Encryptions @ REST
  • SMK & CMK Encryptions
  • Azure Key Vaults & Key
  • Access Policies
  • Automated Encryptions
  • Realtime Considerations

Ch 7: Azure Metrics & Alerts

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

Ch 8: 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 9: 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

Part 3: Databricks (ETL, DWH)

Ch 1: Databricks Intro

  • Azure Introduction
  • Azure Account & Subscription
  • Open Source ETL : Spark
  • Azure Databricks Resource
  • Databricks Workspace
  • Creating Spark Cluster

Ch 2: Spark Architecture

  • Spark Clusters: Types, Policies
  •  Driver Node: Purpose, Compute
  • Worker Node: Purpose, Compute
  • Cluster Manager, Executions
  • Resilent Distributed Datasets
  • DAG: Directed Acyclic Graph

Ch 3: DBFS Operations

  • DBFS Concepts: File Store, Tables
  • DBFS File Uploads, Infer Schema
  • Header Row Promotion
  • Create Table using UI
  • HIVE Metastore Catalog
  • Spark Database & Tables

Ch 4: Notebooks Intro

  • ETL & ELT Process
  • Workspace Options: Notebooks
  • Notebooks: SQL, Python, Scala
  • When to use which Notebooks?
  • Notebook Exports, Imports
  • Cloning and Markdown Cells

Ch 5: Unity Catalog

  • Unity Catalog & Big Data Storage
  • Unity Catalog Connectors
  • Catalog Explorer, HIVE Metastore
  • Ubuntu VM : Azure Resource
  • Cluster Size & VM Size Options
  • Default Spark Database, Usage

Ch 6: Spark SQL Notebooks

  • Creating Spark Databases
  • Connecting to Spark Databases
  • Creating Spark tables
  • Data Inserts & DML Operations
  • DDL Operations on Spark Tables
  • SQL Notebook: Limitations

Ch 7: Python Introduction

  • Python Introduction
  • Python Usage in ETL, DDL, DML
  • Dataframes : Purpose
  • Dataframes as Spreadsheets
  • Spark Environment for Python
  • PySpark: Python inside Spark

Ch 8: Python Notebooks

  • DBFS File Source & DataFrames
  • Creating Temp View
  • Dataframe Loads to TempView
  • Data Filters in Temp View
  • Data Aggregations in Temp View
  • Creating Parquet Tables

Ch 9: Azure SQL Reads

  • Azure SQL DB Connections
  • Azure SQL Server & DB Names
  • Connection String & URL Format
  • Dataframes @ spark.read.jdbc()
  • Aggregated / Incremental Tfns
  • Data Loads into Spark Database

Ch 10: Azure SQL Writes

  • DBFS FileStore into Dataframes
  • SQL Database Connections
  • Filters, Aggregations with DBFS
  • Azure SQL DB Connections
  • Dataframes To write.jdbc()
  • Dataframes to Azure SQL DB

Ch 11: Medallion Architecture

  • Medallion Architecture: Scaling
  • Raw Data with Medallion
  • Transformations (ETL)
  • Bronze Layer : Raw Data
  • Silver Layer with Temp Views
  • Gold Layer with Spark Tables

Ch 12: PySpark Transformations

  • Custom DataFrames
  • Single List, Mixed List Options
  • Concat Function & Index Options
  • Removing Empty Rows
  • Replacing Null Values
  • Merge, Joins, Join Kind

Ch 13: Delta Tables (PySpark)

  • Delta Tables : Upsert Activity
  • Creating Delta Tables
  • DML Operations in Delta Tables
  • Upsert: Incremental Loads
  • Delta Tables in HIVE Metastore
  • MERGE INTO Statement (Spark)

Ch 14: Python Widgets (PySpark)

  • Widgets: Notebook Parameters
  • dbutils.widgets.text()
  • dbuitls.widget.get()
  • Reading Widgets into Variables
  • Using Variables in Notebook
  • Aggregated Loads with Widgets

Ch 15: Workflows (PySpark)

  • Python Notebook Schedules
  • Adding Tasks to Jobs
  • Job Clusters & Cluster Sizes
  • High Performance Cluster
  • Unlimited Clusters
  • Job Notifications, Verifications

Ch 16: Security

  • IAM : Creating AD Users, Groups
  • RBAC Concepts: IAM Roles
  • Databricks Resource Security
  • Databricks Workspace Security
  • Notebook Job Level Security
  • Job Level Security, Sharing

Ch 17: Spark Data Analytics

  • Access Tokens & API Access
  • JDBC Connections: Server Host
  • HTTP Path & Port: Server URL
  • Power BI Desktop : Get Data
  • Spark Cluster Connections
  • Data Access & Test Connection

Ch 18: Databricks Tuning

  • Databricks Tuning: Caching
  • Job Clusters & Cloud Computing
  • Photon Acceleration
  • Spot Instance & Unity Catalog
  • Auto Scaling & Cluster Nodes
  • Performance Optimizations

Ch 19: Scala Notebooks – V1

  • Scala Notebooks: Realtime Use
  • JVM and Scala Notebooks
  • Creating Data Frames in Scala
  • Creating Temp Tables in Scala
  • Medallion Architecture
  • Parquet Tables & Delta Tables

Ch 20: Scala Notebooks – V2

  • Working with Widgets in Scala
  • Variables and Parameters
  • Dynamic Connections
  • Format String (F String) Options
  • Using SQL DB Connections in Scala
  • Python Versus Scala in Realtime?

Databricks Certification Exam
Fabric Cloud Concepts
Fabric Cloud Migrations

Module 3: Power BI

Ch 1: Power BI Intro, Installation

  • Power BI Eco System
  • Report Types & Usage
  • Power BI Tools, Cloud
  • Power BI Components
  • Power Query (M), DAX
  • Power BI: Co-Pilot & AI
  • Power BI Installations

Ch 2: Report Design Concepts

  • Basic Report Design (PBIX)
  • Get Data, Canvas (Design)
  • Data View, Data Models
  • Data Points, Aggregations
  • Focus Mode, Spotlight
  • PDF Exports From Power BI
  • ToolTip, PBIX Reports

Ch 3: Visual Interactions, PBIT

  • Data View Concepts
  • Visual Interactions & Edits
  • Limitations with Visual Edits
  • Creating Power BI Templates
  • CSV Exports & PBIT Imports
  • Optimizing Power BI : Caching
  • PBIX Versus PBIT

Ch 4: Grouping, Hierarchies

  • Power BI : Field Values
  • Field Value Groups
  • 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
  • Integer, Character Slicers
  • 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 of Data Points

Ch 7: Filters & Drill Thru

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

Ch 8: Bookmarks, Buttons

  • Power BI Bookmarks
  • Bookmarks Creation, Use
  • 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
  • Storage Modes & Formatting
  • Azure (Big Data) Access

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, Reference
  • Group By Transformation
  • Aggregate, Pivot Operation
  • First Row as Header
  • Reverse Rows, Count Rows
  • Advanced Power Query Mode

 Ch 13: Power Query: Column Tfn

  • Any Column Transformations
  • Change Data Type
  • Detect Data Type
  • 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
  • Column From Examples
  • Step Edits, Type Conversions

Ch 16: Power BI Cloud: Publish

  • Power BI Cloud Concepts
  • Workspace Creation, Usage
  • Workspace Items
  • 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
  • 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
  • Dataset 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, Favorites
  • App URL, End User Access

Ch 21: Power BI Report Server

  • 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)
  • SQL Database Access
  • SQL Queries For RDL
  • Tablix, Chart Wizards
  • Fields & Drill-Down
  • RDL Report Publish

Ch 23: DAX Concepts (Basics)

  • DAX Concepts (Introduction)
  • DAX : Realtime Use
  • 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
  • Analyze in Excel (Cloud)
  • Excel Reports to Cloud

Ch 30: Introduction to CoPilot

  • AI Components in Power BI
  • Need for CoPilot
  • CoPilot Practical Uses
  • CoPilot with Desktop
  • CoPilot with Cloud
  • Need for AI Analytics (Fabric)

Ch 31: Realtime Project – Phase 1

  • Customer Requirement
  • Requirement Analysis
  • Project Planning
  • Creating Data Sheets
  • Creating Data Models
  • Scope of the Project
  • Data Sheets, Project Planning

Ch 32: Realtime Project – Phase 2

  • Report Design & Modelling
  • Power Query Implementation
  • DAX & Data Analytics
  • Power BI Cloud (Service)
  • Power BI Report Server
  • End User Take Aways
  • Implementation Phases

Ch 33: PL 300 Exam Guidance

  • PL 300 Exam Benefits
  • Data Analyst Exam Pattern
  • Type of Questions
  • Sample Questions, Answers
  • Mock Certification
  • Resume Guidance
  • Mock Interviews

SQL SCHOOL

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

Azure Data Engineer Training FAQ's

What is Azure Data Engineer Job Role?

Azure Data Engineers are responsible for designing and implementing secure, scalable, and efficient data solutions on Microsoft Azure. They manage data ingestion, storage, transformation, integration, and security using services like Azure Data Factory, Azure Synapse Analytics, Data Lake Storage, Azure SQL, and Databricks. Azure Data Engineers ensure that enterprise data pipelines are optimized, reliable, and compliant with governance policies.

What are the Job Roles of an Azure Data Engineer?

💼 Top Job Roles:

1️⃣ Design and build modern data pipelines using Azure Data Factory, Synapse, and Data Lake
2️⃣ Implement ETL and ELT processes for structured and unstructured data
3️⃣ Ensure data security, privacy, and compliance across Azure data solutions
4️⃣ Develop and optimize big data processing solutions with Spark and Databricks
5️⃣ Set up monitoring, troubleshooting, and performance tuning for data flows
6️⃣ Collaborate with business teams to deliver end-to-end data solutions and more..!

What does our Azure Data Engineer Training course contain?

The course is carefully curated with below module:
👉🏻Module 1: MSSQL & TSQL Queries
👉🏻Module 2: Azure Data Engineer
👉🏻Module 3: Power BI

Who can join this course?

  • Freshers aspiring to build a career in cloud data engineering

  • ETL developers wanting to move to Azure platforms

  • SQL DBAs and Data Analysts aiming to transition to cloud data engineering

  • IT professionals interested in modern data architecture and big data pipelines

  • Anyone looking to upskill for cloud-based data solutions

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1:        LIVE Online Training  (100% Interactive, step by step, assignments)

Option 2:        Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for Azure Data Engineering training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice.  Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

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