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Azure Data Engineer

Azure Data Engineer Training: Data engineering is about designing and maintaining systems that collect, store, and process…

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Azure Data Engineer Training:

Data engineering is about designing and maintaining systems that collect, store, and process large amounts of data. It helps businesses organize and analyze data efficiently. This involves working with different types of data, creating automated workflows (ETL pipelines), and ensuring data security.

An Azure Data Engineer is responsible for designing, building, and managing data solutions on Microsoft Azure. They specialize in data storage, integration, transformation, and security, enabling businesses to derive valuable insights from their data. Azure Data Engineers work with structured and unstructured data, leveraging Azure services like Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, and Azure SQL Database to build scalable, high-performance data pipelines.

This Azure Data Engineer Training is designed to help you with the expertise needed to design, implement, and manage data solutions on Microsoft Azure. This Azure Data Engineer Course covers data storage, integration, processing, security, and governance, enabling you to build scalable, high-performance, and cost-efficient data solutions for modern enterprises. Whether you’re looking for Azure Data Engineer Online Training or a structured learning path, this course helps you with the necessary skills to excel in Azure data engineering.

What Will You Learn?

1. Introduction to Azure Data Engineering: Overview of Azure Data Services – Understand the key data services available in Azure, including data storage, processing, and analytics tools. Roles & Responsibilities of an Azure Data Engineer – Learn what an Azure Data Engineer does, including data pipeline development, data transformation, and cloud-based analytics. Data Pipelines & Big Data Architectures – Explore different big data architectures and how Azure enables scalable, high-performance data solutions.
2.Data Storage in Azure: Azure Data Lake Storage (ADLS Gen2) – Learn how to store, manage, and organize big data efficiently using hierarchical namespace and security features. Azure SQL Database & Synapse Analytics – Discover data warehousing solutions, optimizing performance for large-scale analytical workloads. Azure Cosmos DB – Explore NoSQL databases, globally distributed data models, and how to work with multi-model data storage.
3. Data Ingestion and Integration: Azure Data Factory (ADF) – Master ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines, automating data movement across Azure services. Azure Event Hubs & IoT Hub – Learn to stream real-time data from IoT devices, applications, and logs for real-time analytics and monitoring. Azure Stream Analytics – Process and analyze streaming data in real time for insights and decision-making.
4. Data Processing & Transformation: Azure Databricks (Apache Spark) – Learn to process big data at scale with Apache Spark, integrating machine learning and analytics workflows. Azure Synapse Analytics – Work with serverless and dedicated SQL pools for high-performance querying and big data analytics. Mapping Data Flow in ADF – Build no-code data transformation workflows to cleanse and enrich data within Azure Data Factory.
5. Security, Governance & Compliance: Role-Based Access Control (RBAC) & Data Encryption – Implement access control policies and ensure data security with encryption techniques. Azure Purview – Use data governance tools to track data lineage, classify datasets, and enforce compliance with industry regulations. Monitoring & Logging – Leverage Azure Monitor, Log Analytics, and Application Insights to track performance, detect issues, and enhance system reliability.
6. Performance Optimization & Cost Management: Query Optimization Techniques – Improve Azure Synapse and SQL query performance using indexing, partitioning, and caching strategies. Cost-Effective Storage & Compute Management – Learn to manage cloud costs effectively by choosing the right pricing model and storage tiering options. Scaling Resources Efficiently – Implement auto-scaling, load balancing, and compute optimization to handle high data workloads cost-effectively.
7. Real-World Use Cases & Hands-on Projects: Building ETL Pipelines with Azure Data Factory – Create data pipelines to extract, transform, and load data efficiently. Real-Time Data Processing using Azure Databricks – Process real-time event streams and perform analytics on big data. Data Warehousing & BI with Synapse Analytics – Design and optimize data warehouses for business intelligence and reporting with Power BI integration.

Course Curriculum

Module 1: Introduction to Data Engineering and Azure

1.1 Fundamentals of Data Engineering
:: Overview of Data Engineering Roles and Responsibilities
:: Understanding Data Pipelines: Batch vs. Real-time Processing
:: Key Data Engineering Concepts: ETL, Data Lakes, Data Warehouses, and Data Analytics
1.2 Introduction to Microsoft Azure
:: Overview of Microsoft Azure and Its Ecosystem
:: The Role of Azure in Modern Data Engineering
:: Azure Global Infrastructure: Regions, Availability Zones, Resource Groups, and VNETs
1.3 Setting Up Your Azure Environment
:: Creating and Configuring an Azure Account
:: Navigating the Azure Portal and Using Azure CLI
:: Understanding Azure Active Directory (Azure AD) and Role-Based Access Control (RBAC)
:: Managing Costs and Billing in Azure

Module 2: Azure Storage Solutions

2.1 Azure Blob Storage: The Foundation of Azure Storage
:: Introduction to Azure Blob Storage
:: Blob Types: Block, Append, and Page Blobs
:: Data Lifecycle Management and Archiving with Blob Storage
2.2 Azure Data Lake Storage (ADLS)
:: Introduction to Azure Data Lake Storage Gen2
:: Hierarchical Namespace, Security, and Performance Features of ADLS
:: Organizing Data in ADLS for Analytics
:: Best Practices for Data Management and Cost Optimization
2.3 Azure Files and Azure Managed Disks
:: Understanding Azure Files: SMB and NFS File Shares
:: Configuring and Managing Azure Managed Disks
:: Choosing Between Azure Blob Storage, ADLS, and Azure Files for Different Use Cases
2.4 Data Migration and Integration in Azure
:: Data Migration Strategies: Azure Data Box, Azure Migrate
:: Using Azure Storage Explorer for Data Management
:: Hybrid Storage Solutions with Azure File Sync and Azure Data Box Gateway
:: Real-time Scenario: Setting Up a Secure Data Lake in ADLS

Module 3: Databases and Data Warehousing in Azure

3.1 Introduction to Azure SQL Database
:: Overview of Azure SQL Database and SQL Managed Instance
:: Deploying and Managing Azure SQL Databases
:: High Availability, Backup, and Disaster Recovery in Azure SQL
:: Security and Performance Tuning in Azure SQL Database
3.2 Azure Cosmos DB: Globally Distributed NoSQL Database
:: Introduction to Azure Cosmos DB and Its Multi-Model Capabilities
:: Partitioning, Consistency Levels, and Global Distribution
:: Working with Cosmos DB APIs: SQL, MongoDB, Cassandra, Gremlin, Table
:: Optimizing Performance and Costs in Cosmos DB
3.3 Azure Synapse Analytics: Data Warehousing and Big Data
:: Introduction to Azure Synapse Analytics (formerly SQL Data Warehouse)
:: Setting Up and Configuring a Synapse Workspace
:: Integrating Synapse with ADLS, Power BI, and Azure Machine Learning
:: Performance Optimization: Distribution, Partitioning, and Caching in Synapse
3.4 Real-time Scenario: Designing a Data Warehouse in Azure Synapse
:: Architecting a Data Warehouse Solution with Azure Synapse
:: Implementing ETL Pipelines with Azure Synapse and Azure Data Factory
:: Query Performance Tuning and Cost Management in Azure Synapse

Module 4: Data Ingestion and ETL Pipelines

4.1 Azure Data Factory (ADF): Orchestration and ETL
:: Introduction to Azure Data Factory and Its Components
:: Creating and Managing ADF Pipelines, Datasets, and Activities
:: Data Movement and Transformation with Copy Data, Mapping Data Flows, and Wrangling Data Flows
:: Monitoring, Debugging, and Optimizing ADF Pipelines
4.2 Real-time Data Processing with Azure Stream Analytics
:: Introduction to Azure Stream Analytics for Real-time Data Ingestion
:: Configuring Input, Output, and Query in Stream Analytics Jobs
:: Integrating Stream Analytics with Event Hubs, IoT Hub, and Blob Storage
:: Real-time Data Processing and Analytics with Stream Analytics and Power BI
4.3 Azure Databricks: Unified Data Analytics Platform
:: Introduction to Azure Databricks and Apache Spark
:: Setting Up and Configuring Databricks Workspaces and Clusters
:: Data Engineering with Databricks: ETL, Data Integration, and Batch Processing
:: Real-time Scenario: Building a Data Pipeline with ADF and Databricks
4.4 Serverless Data Processing with Azure Functions
:: Introduction to Azure Functions and Event-driven Architectures
:: Triggering Functions from Blob Storage, Cosmos DB, and Event Hubs
Building Serverless ETL Pipelines with Azure Functions
:: Best Practices for Function App Performance and Cost Optimization

Module 5: Data Analytics and Machine Learning

5.1 Data Exploration and Analytics with Azure Synapse Studio
:: Writing and Running SQL Queries and Spark Jobs in Synapse Studio
:: Visualizing Data with Power BI Integration in Synapse
:: Optimizing Queries and Managing Costs in Synapse Studio
5.2 Azure Machine Learning: End-to-End ML Lifecycle
:: Introduction to Azure Machine Learning and ML Studio
:: Data Preparation and Feature Engineering with Azure ML
:: Training, Tuning, and Deploying Models in Azure ML
:: Monitoring and Managing ML Models in Production
5.3 Big Data Processing with Azure HDInsight
:: Introduction to Azure HDInsight: Apache Hadoop, Spark, Hive, and Kafka
:: Setting Up and Managing HDInsight Clusters
:: Data Processing and Analytics with Spark and Hive on HDInsight
:: Real-time Scenario: Implementing a Big Data Pipeline with HDInsight and Synapse
5.4 Real-time Scenario: End-to-End Data Analytics Pipeline
:: Building a Data Analytics Pipeline from ADLS to Synapse and Power BI
:: Implementing a Machine Learning Workflow with Azure ML and Synapse
:: Automating Data Processing and Model Training with ADF and Azure Functions

Module 6: Data Security and Governance

6.1 Data Security in Azure
:: Understanding the Azure Security Model
:: Implementing Network Security: VNETs, NSGs, and Firewalls
:: Data Encryption: At Rest and In Transit with Azure Key Vault
:: Securing Data Access with Managed Identities, RBAC, and Conditional Access
6.2 Data Governance with Azure Purview
:: Introduction to Azure Purview: Data Governance and Cataloging
:: Setting Up Purview Accounts, Scanning Data Sources, and Building Data Catalogs
:: Managing Data Lineage, Classifications, and Policies with Purview
:: Integration of Purview with ADF, Synapse, and Power BI for Data Governance
6.3 Compliance and Regulatory Requirements
:: Understanding Compliance Frameworks: GDPR, HIPAA, etc.
:: Implementing Compliance Controls with Azure Policy and Blueprints
:: Real-time Scenario: Securing and Governing a Data Pipeline in Azure

Module 7: Advanced Data Engineering with Azure

7.1 Building and Managing Data Lakes with Azure Data Lake
:: Architecting Data Lakes on Azure: ADLS Gen2 and Synapse
:: Data Lake Best Practices: Security, Performance, and Cost Management
:: Implementing Data Lakehouse Architectures with Synapse and Databricks
:: Real-time Scenario: Building a Scalable Data Lake on Azure
7.2 Data Migration Strategies in Azure
:: Migrating On-premises Data to Azure: Azure Migrate, Data Box, and ADF
:: Designing Hybrid Cloud Architectures: Integrating On-premises and Azure Data
:: Data Replication and Synchronization with ADF, SQL Data Sync, and Event Grid
:: Real-time Scenario: Migrating a Large-scale Data Warehouse to Azure
7.3 Advanced Data Pipeline Architectures
:: Designing Fault-tolerant and Scalable Data Pipelines in Azure
:: Implementing Event-driven Architectures with Azure Event Hubs, Service Bus, and Logic Apps
:: Building Complex Data Workflows with Azure Durable Functions and Logic Apps
:: Managing Workflow State, Retries, and Error Handling in Azure Pipelines
7.4 Real-time Scenario: Implementing a Scalable Data Architecture
:: Architecting and Implementing a Data Lakehouse with Synapse and Databricks
:: Integrating Real-time and Batch Processing Pipelines in Azure
:: Optimizing Data Storage, Query Performance, and Costs in a Large-scale Data Solution

Module 8: Monitoring, Optimization, and Cost Management

8.1 Monitoring and Logging in Azure
:: Introduction to Azure Monitor, Log Analytics, and Application Insights
:: Setting Up Alerts, Metrics, and Dashboards for Data Pipelines
:: Centralized Logging with Azure Monitor and Storage Accounts
:: Real-time Scenario: Implementing Comprehensive Monitoring for a Data Pipeline
8.2 Performance Optimization Techniques
:: Optimizing Data Storage and Retrieval in ADLS, Synapse, and Cosmos DB
:: Improving Query Performance in Azure Synapse and Databricks
:: Efficient Scaling and Auto-scaling for Data Pipelines
:: Real-time Scenario: Tuning Pipeline Performance for High-volume Data Processing
8.3 Cost Management and Optimization
:: Azure Cost Management and Billing Tools: Cost Explorer, Budgets, and Reservations
:: Identifying and Reducing Azure Costs with Azure Advisor
:: Cost Optimization Strategies for Data Engineering Workloads
:: Leveraging Azure Reserved Instances and Spot VMs for Cost Savings
:: Real-time Scenario: Managing Costs and Optimizing Resources in a Data Pipeline

Module 9: Final Project and Certification Preparation

9.1 Project: End-to-End Data Engineering Solution on Azure
:: Designing and Implementing a Complete Data Pipeline in Azure
:: Integrating Azure Services: ADLS, ADF, Synapse, Databricks, and Power BI
:: Ensuring Security, Compliance, and Governance in the Data Solution
:: Optimizing Performance, Scalability, and Costs for the Final Project
9.2 Azure Data Engineer Certification Preparation
:: Overview of Microsoft Azure Data Engineer Associate (DP-203) Certification
:: Exam Objectives and Key Topics Review
:: Practice Questions and Mock Exams
:: Tips and Strategies for Passing the Certification Exam

Module 10: Career Development and Real-world Applications

10.1 Real-world Applications of Azure Data Engineering
:: Case Studies of Data Engineering Solutions in Various Industries
:: Emerging Trends in Data Engineering and Cloud Computing
:: Azure AI and IoT Integration with Data Engineering Pipelines
:: Networking, Community Engagement, and Continuing Education
10.2 Career Development and Job Search Strategies
:: Building a Data Engineering Portfolio on GitHub and Azure
:: Crafting a Resume and Preparing for Data Engineering Interviews
:: Understanding Industry Demand and Market Trends for Data Engineers
:: Leveraging LinkedIn and Networking for Career Opportunities

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