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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.

Azure Data Engineer SQL School Modules

Training Schedules

S NoTime (IST, Mon - Fri)Start Date
16 AM - 7 AMSep 2nd
210 AM - 11 AMAug 26th
311 AM - 12 PMAug 12th
48 PM -9 PMAug 18th
Fabric Data Engineer

Azure Data Engineer
Training Course Contents:

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: Power BI

Ch 1 : Power BI Introduction

  • Reporting Basics & Types
  • Interactive,Analytical Reports
  • Paginated Reports (RDL)
  • Power BI Eco System
  • Power BI Tools,Service,Server
  • Need for Power Query (M)
  • Need for DAX & Cloud

Ch 2: Power BI Basic Reports

  • Power BI Desktop Installation
  • Basic Report Design (PBIX)
  • Data View, Data Models
  • Data Points, Aggregations
  • Focus Mode, Spotlight, Exports
  • ToolTip, PBIX and PBIT
  • Visual Interactions & Edits

Ch 3 : Grouping, Hierarchies

  • Creating Groups in Power BI
  • Groups : Creation & Usage
  • Group Edits Options
  • Bins & Bin Size, Bin Count
  • Hierarchies: Creation, Use
  • Drill Down, Drill Up
  • Conditional Drill Down

Ch 4 : Visual Sync, Filters

  • Slicer & Single Select
  • Multi Select Options
  • Integer, Character Slicers
  • Visual Sync with Slicers
  • Filters: Visual, Page, Report
  • Drill Thru Filters & Usage
  • Basic, Top & Advanced
  • Clear Filter Options, Resets

Ch 5 : Bookmarks, Big Data

  • Bookmarks Creation & Usage
  • Visual Interactions, Bookmarks
  • Images : Actions, Bookmarks
  • Big Data Access with Power BI
  • Storage Modes: Direct Query
  • Import & Performance Impact
  • Formatting & Data Refresh
  • Summary, Date Time Formats

Ch 6 : Power BI Visualizations

  • Chart and Bar Visuals
  • Line and Area Charts
  • Maps, TreeMaps, HeatMaps
  • Funnel, Card, Multrow Card
  • PieCharts & Settings
  • Waterfall, Sentiment Colors
  • Scatter Chart, Play Axis
  • Infographics, Classifications

Ch 7 : Power Query Level 1

  • Power Query (Mashup)
  • ETL Transformations in PBI
  • Power Query Expressions
  • Table Combine Options
  • Merge, Union All Options
  • Table Transformations

Ch 8 : POWER QUERY LEVEL 2

  • Any Column Transformations
  • String / Text Transformations
  • Numeric Analytics & Mashup
  • Date Time Transformations
  • Add Column Transformations
  • Expressions and New Columns

Ch 9 : POWER QUERY LEVEL 3

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

Ch 10 : Power BI Cloud – 1

  • Power BI Cloud Concepts
  • Workspace Creation, Usag
  • Report Publish & Edits
  • Semantic Models in Realtime
  • Dashboard Creation, Usage
  • Clone, Share, Subscribe
  • Q&A, Lineage, Settings

Ch 11 : Power BI Cloud – 2

  • Data Gateways, Data Refresh
  • Data Source Configurations
  • Data Refresh & Scheduling
  • Gateway Optimizations
  • Semantic Model Optimizations
  • Report Optimizations
  • Dashboard Optimizations

Ch 12 : Power BI Cloud – 3

  • Power BI Apps, Shares
  • App Sections & Options
  • App Updates, Security
  • Excel Analytics
  • Data Explorer Option
  • Sharing, Subscriptions
  • Alerts, Metrics, Insights

Ch 13 : Report Server & DAX

  • Power BI Report Server
  • Report Database, TempDB
  • Web Service & Server URL
  • Paginated Reports (RDL)
  • Report Builder Tool Usage
  • DAX : Purpose, Realtime Use

Ch 14: DAX Level 2

  • DAX Measures Creation, Use
  • DAX Functions: IIF, ISBLANK
  • SUM, CALCULATE Functions
  • DAX Cheat Sheet : Examples
  • Quick Measures in Power BI
  • Running Totals, Filters

Ch 15 : DAX Level 3

  • Star Rating Calculations
  • Data Models & DAX
  • Star & Snowflake Schemas
  • Dimensions, Fact Tables
  • DAX Expressions & Joins
  • DAX Variables, Usage

Ch 16 : DAX Level 4

  • Dynamic Report with DAX
  • SELECTED MEMEBER
  • Time Intelligence with DAX
  • PARALLELPERIOD, DATE
  • DAX with Big Data
  • Big Data Analytics

Ch 17 : Realtime Project Phase 1

  • Project Requirement Spec
  • Understanding Data, Formats
  • Report Pattern Design
  • Report Design & Modelling
  • Power Query, DAX, Insights
  • Analytical Reports in Cloud

Ch 18 : Realtime Project Phase 2

  • Complete Project Solution
  • Project FAQs, Key Roles
  • Real-world Considerations
  • Power BI Admin Concepts
  • Resume Points, FAQs
  • PL 300 Exam Guidance

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