Azure Data Engineering Training & Certification

What is an Application?

Types of Applications

Web Application Fundamentals

Web Technologies:

• Frontend: HTML, CSS, JavaScript, React
• Backend: Python, Java, Node.js
• Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB)

Software Development Life Cycle (SDLC)

• Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

Application Development Methodologies

• Agile: Core principles, Scrum, Kanban
• Waterfall

What is Data?

Types of Data

Data Storage

Data Analysis

Data Engineering

Data Science

The Importance of Computing Power

Key Computing Technologies:

• CPU (Central Processing Unit)
• GPU (Graphics Processing Unit)

Cloud Computing:

• What is the Cloud?
• Cloud Service Models:
  • IaaS (Infrastructure as a Service)
  • PaaS (Platform as a Service)
  • SaaS (Software as a Service)

What is Artificial Intelligence (AI)?

How AI Works?

Key Concepts:

• Machine Learning (ML)
• Deep Learning (DL)

Generative AI:

• What is Generative AI?
• Examples: Large Language Models (LLMs), image generation models.

AI in Everyday Learning

Customer Relationship Management (CRM)

Human Resource Management Systems (HRMS)

Retail & E-Commerce

Healthcare

Why Python — domains and applications

Installation, environment setup, and running scripts

Syntax, keywords, and identifiers

Variables, data types, and operators

Input/output, comments, and indentation rules

Conditional statements and loops

Strings — indexing, slicing, methods

Lists, tuples, sets, and dictionaries

Mutability vs immutability

List comprehensions and advanced collection operations

Functions — definition, parameters, return values

Anonymous (lambda) functions and scope

File handling — open/read/write/append, with statement

Working with CSV and Excel files

Importing and creating modules, using packages

Python Standard Library highlights (os, sys, datetime, json)

Virtual environments and dependency management

Classes, objects, constructors, and destructors

Instance, class, and static methods

Inheritance, polymorphism, and encapsulation

Exception handling (try, except, finally, custom exceptions)

Regular expressions for pattern matching

Functional tools — map, filter, reduce

Practical mini-project (e.g., text analyzer or inventory tracker)

Database and RDBMS concepts (overview only)

Essential SQL queries — SELECT, WHERE, JOIN, GROUP BY, HAVING

DML basics (INSERT, UPDATE, DELETE)

Using SQLite or PostgreSQL with Python (sqlite3, psycopg2)

Fetching and manipulating data from databases via Python

End-to-end project combining Python logic and SQL data

Understanding Business Intelligence and data-driven decision making

Power BI ecosystem: Desktop, Service, and Mobile

Navigating the Power BI interface

Connecting to multiple data sources (Excel, CSV, SQL, Web, APIs)

Power Query overview — loading and transforming your first dataset

Building your first interactive report

Data cleaning, shaping, and transformation in Power Query

Handling nulls, duplicates, and inconsistent formats

Merging, appending, pivoting, and unpivoting data

Creating calculated columns, custom tables, and hierarchies

Star vs Snowflake schema design — Fact & Dimension tables

Building relationships and optimizing data models for performance

Core visualization principles and report design best practices

Building charts, maps, tables, cards, slicers, and filters

Creating interactive dashboards with bookmarks, drill-downs, and drill-throughs

Using field parameters for dynamic visuals

Custom visuals from the Power BI marketplace

Designing executive dashboards and storytelling with data

Introduction to DAX: syntax, context, and evaluation flow

Calculated columns vs measures

Common DAX functions: SUMX, CALCULATE, FILTER, IF, RELATED

Time intelligence: YTD, MTD, QoQ, YoY, rolling averages

Advanced DAX concepts — variables, iterators, and dynamic measures

Debugging and performance tuning DAX queries

Creating KPIs and analytical insights

Power BI and Power Automate — workflow automation

AI visuals: Key Influencers, Decomposition Tree, Q&A

Integrating with Azure ML and Cognitive Services

Publishing and sharing reports via Power BI Service

Managing workspaces, dataflows, and refresh schedules

Row-Level and Object-Level Security (RLS/OLS)

Deployment pipelines, governance, and best practices for enterprise BI

What is Data Engineering

Role of a Data Engineer in the modern data stack

Core components of a data pipeline

Data Engineering vs Data Science vs Data Analytics

Understanding Data Lifecycle (Ingestion → Storage → Processing → Visualization)

OLTP vs OLAP systems

Data Warehouse vs Data Lake vs Lakehouse

ETL vs ELT

Streaming vs Batch Processing

Real-world architecture patterns (Lambda, Kappa)

File formats: CSV, JSON, Avro, Parquet, Delta

Compression techniques and partitioning

Data ingestion methods and best practices

APIs, message queues, and connectors

Data validation and quality frameworks

Apache Spark, Hadoop, Kafka overview

Databricks introduction

Orchestration tools: Airflow, ADF

Version control for data pipelines (Git, CI/CD)

Testing & Monitoring data workflows

Building scalable data pipelines

Handling schema evolution and metadata

Data Governance and Security

Data Lineage tracking

Observability and alerting in data systems

What is Cloud Computing and its importance

Overview of Azure Architecture and Global Infrastructure

Understanding Service Models: IaaS, PaaS, and SaaS

Azure Subscription, Resource Groups, and Role-Based Access Control (RBAC)

Navigating the Azure Portal, CLI, and ARM Templates

Introduction to Azure Storage types: Blob, Table, Queue, and File Storage

Understanding Storage Tiers: Hot, Cool, and Archive

Data Redundancy Models: LRS, GRS, ZRS

Implementing Lifecycle Management Policies

Securing access with SAS Tokens and Managed Identities

Overview: Azure SQL Database, Managed Instance, and Synapse Analytics

Steps to Deploy and Manage Azure SQL Databases

Understanding DTUs vs vCores performance models

Configuring Firewall Rules and Authentication Options

Backup Strategies and High Availability concepts

Introduction to Azure Data Factory (ADF)

Understanding Linked Services, Datasets, and Pipelines

Working with Copy Activity and Data Flows

Using Integration Runtime for data movement

Scheduling, Triggering, and Monitoring ADF Pipelines

Overview of Virtual Machines, App Services, Containers, and Functions

Fundamentals of Azure Networking: VNet, Subnet, Peering

Using Private Endpoints and integrating with data services

Managing Identity & Access Control in Azure

Hybrid Connectivity and On-Premises Integration Options

Overview and architecture

Dedicated SQL pools vs Serverless SQL pools

Data ingestion and transformation in Synapse

Synapse pipelines and integration with ADF

Query optimization and workload management

Workspace and cluster setup

Notebooks and jobs

Delta Lake concepts

Data ingestion and transformation with PySpark

Integration with ADF and Synapse

Real-time streaming pipelines

Input and output configurations

Query language for Stream Analytics

Event Hubs and IoT Hub integration

Use cases: fraud detection, IoT telemetry

Power BI overview

Dataflows, Datasets, and Gateways

Connecting Power BI with Synapse, Databricks, and Azure SQL

Data refresh scheduling

Building and publishing reports

Scheduling and triggering with Logic Apps

Monitoring pipelines via Azure Monitor

Alerts and diagnostics

Automation Runbooks

Cost optimization strategies

What is data governance

Azure Purview / Microsoft Fabric Data Catalog

Metadata scanning and classification

Lineage visualization

Data stewardship and glossary management

Azure Security Center overview

Role-based access control (RBAC)

Data encryption (at rest & in transit)

Managed Identities and Key Vault integration

Compliance standards (GDPR, HIPAA)

Query tuning in Synapse and SQL

Partitioning and indexing strategies

Storage optimization and caching

Monitoring query performance

Scaling compute dynamically

Azure Monitor, Log Analytics, and Application Insights

Debugging Data Factory and Synapse pipelines

Error handling and retry policies

Cost management dashboards

Alerts and health checks

Design an end-to-end Azure Data Pipeline

Include ingestion, storage, transformation, and visualization

Apply governance and security controls

Performance tuning and documentation

DP-600 Certification tips and mock Q&A