Azure Data Engineering Training & Certification

1. What is an Application?

2. Types of Applications

3. Web Application Fundamentals

4. Web Technologies: (List key technologies and their roles)

Frontend: HTML, CSS, JavaScript, React

Backend: Python, Java, Node.js

Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

5. Software Development Life Cycle (SDLC)

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

6. Application Development Methodologies

Agile: Core principles, Scrum, Kanban

Waterfall

1. What is Data

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

1. The Importance of Computing Power

2. Key Computing Technologies:

CPU (Central Processing Unit)

GPU (Graphics Processing Unit)

3. Cloud Computing:

What is the Cloud?

Cloud Service Models:

IaaS (Infrastructure as a Service)

PaaS (Platform as a Service)

SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)?

2. How AI Works?

3. Key Concepts:

Machine Learning (ML)

Deep Learning (DL)

4. Generative AI:

What is Generative AI?

Examples: Large Language Models (LLMs), image generation models.

5. AI in Everyday Learning

1. Customer Relationship Management (CRM)

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

Topics

What is Data Engineering

Data Engineer Roles & Responsibilities

Difference Between ETL Developer & Data Engineer

Types of Data

Steaming Vs Batch Data

Topics

Cloud Introduction and Azure Basics

Azure Implementation Models: IaaS, PaaS, SaaS

Overview of Azure Data Engineer Role

Understanding Azure Storage Components

Introduction to Azure ETL & Streaming Components

Topics

Azure SQL Server and Database Deployment

DTU vs. DWU: Understanding Performance Levels

Managing Firewall Rules and Secure SSMS Connections

Azure Account and Subscription Management

Topics

Azure Resources and Resource Types

Introduction to Azure Data Factory (ADF) and Azure Synapse Analytics

Basic Concepts of Data Movement and Processing

Topics

Synapse SQL Pools (Data Warehousing) and Massively Parallel Processing (MPP)

Data Movement with DMS and SQL Pool Management

Table Creations, Distributions, and Indexing for Performance

Topics

Azure Data Factory Pipeline Architecture and Integration Runtime

Constructing ETL Pipelines with DIU Considerations

Data Flow Activities and Monitoring

Topics

Incremental Data Loading and Handling On-Premise Data Sources

Advanced ADF Features: Data Flows, ETL Logging, and Performance Tuning

Implementing CDC with ADF for Real-Time Data Capture

Topics

Integrating Spark with Synapse Analytics for Big Data Processing

Utilizing Python Notebooks and Spark Pools for Data Analysis

Performance Optimization and Data Transformation Techniques

Topics

Security Measures with Azure Active Directory and Role-Based Access Control

Managing Parameters and Security in Synapse and ADF Pipelines

Utilizing Azure OpenDatasets and Parquet Files for Advanced Analytics

Azure Storage Essentials: Files, Tables, and Queues

Introduction to Azure Data Lake Storage Gen2 (ADLS Gen2)

Configuring and Managing Storage Accounts

Hierarchical Namespace (HNS) and its Advantages

Managing BLOB Storage: Binary Large Objects Explained

Utilizing Azure Storage Explorer for Efficient Storage Management

Directory and File Operations in Azure Data Lake

Best Practices for Organizing Data in ADLS Gen2

Implementing Security Measures in Azure Data Lake Storage

Access Control with Shared Access Signatures (SAS) and Access Control Lists (ACLs)

Role-Based Access Control (RBAC) in Azure Storage

Encryption, Authentication, and Compliance Features

Strategies for SQL Database Migrations to Azure

Integrating Azure SQL with Data Lake Storage

Utilizing Azure Data Factory for Data Movement and Transformation

Data Migration Tools and Techniques

Advanced Concepts in Azure Table Storage

Data Replication and Geo-Redundancy Options

Optimizing Storage Costs and Performance

Leveraging Data Lake for Big Data Analytics

Fundamentals of Azure Stream Analytics

Developing Stream Analytics Jobs for Real-Time Insights

Integrating IoT Devices with Azure for Data Streaming

Processing and Analyzing Streaming Data

Understanding Azure Event Hubs for Large-Scale Event Processing

Configuring Event Hubs and Event Hub Namespaces

Connecting Event Hubs with Azure Stream Analytics

Patterns for Real-Time and Event-Driven Data Processing

Monitoring Azure Storage and Stream Analytics Resources

Performance Tuning for Azure Data Services

Implementing Disaster Recovery Strategies

Using Azure Monitor and Key Vaults for Operational Excellence

Azure Cloud Overview: Understanding SaaS, PaaS, IaaS

Introduction to Azure Databricks: Configuration, Compute Resources, and Workspace Usage

Spark Clusters in Azure Databricks: Configurations, Types, and Resource Management

Databricks File System (DBFS): Utilizing Files and Tables with Spark

Integrating Python with Spark: PySpark Basics

Data Loading Techniques: Using PySpark for Data Ingestion and Processing

Utilizing SQL in Databricks: Creating and Managing Spark Databases and Tables

Advanced Data Transformation: Working with DataFrames and Spark SQL for Data Analytics

Configuring Azure Data Lake Storage (ADLS) for use with Databricks

Data Management: Reading and Writing Data to ADLS using PySpark and Scala

Secure Data Access: Managing Access and Security between Databricks and ADLS

Understanding Databricks Architecture: Driver and Worker Nodes, RDDs, and DAGs

Building and Monitoring Databricks Jobs: Scheduling, Task Management, and Optimization

Implementing Delta Lake for Reliable Data Lakes: ACID Transactions and Performance Tuning

Machine Learning Fundamentals in Databricks: Using MLlib for Predictive Modeling

Data Exploration and Visualization: Leveraging Notebooks for Insights

Advanced Analytic Techniques: Utilizing Scala and Python for Complex Data Analysis

Databricks Security: Integrating with Azure Active Directory (AD)

Managing Permissions: Workspace, Notebooks, and Data Security

Compliance and Data Governance: Best Practices in Databricks Environments

Streaming Data with Databricks: Concepts and Practical Applications

Integrating Azure Event Hubs with Databricks for Real-Time Analytics

Processing Live Data Streams: Building and Deploying Stream Analytics Solutions

Automating Workflows with Azure Logic Apps and Databricks

CI/CD for Databricks: Automation and Version Control Integration

Deployment Strategies: Best Practices for Production Deployments in Azure

Introduction to Generative AI

1. What is Generative AI?

2. Key Applications:

Text (ChatGPT, Claude, LLaMA)

Images (DALL·E, MidJourney, Stable Diffusion)

Audio (Music Generation, Voice Cloning)

Code (GitHub Copilot, Cursor)

3. Evolution of GenAI:

Rule-Based → Deep Learning → Transformers

GANs vs. VAEs vs. LLMs

1. Effective Prompt Design

Instruction-Based, Few-Shot, Zero-Shot

2. Advanced Techniques:

Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Hands-on:

Transformer Architecture

1. Why Transformers? (Limitations of RNNs/LSTMs)

2. Key Components:

Self-Attention & Multi-Head Attention

Encoder-Decoder (BERT vs. GPT)

3. Evolution: BERT → GPT → T5 → Mixture of Experts

4. Large Language Models (LLMs)

5. Pre-training vs. Fine-tuning

6. Popular Architectures:

GPT-4, Claude, Gemini, LLaMA 3

BERT (Encoder-based) vs. T5 (Text-to-Text

Introduction to AI Agents

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

CrewAI + n8n: Automating Business Workflows

Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

Case Studies:

AI Customer Support Agents