The Azure Data Engineer Online Training is designed to equip learners with the skills and knowledge required to design, implement, and manage data solutions on Microsoft Azure. This course is ideal for data engineers, data architects, and IT professionals who want to specialize in Azure data services, big data processing, and real-time analytics. Achieving this certification enhances career opportunities in cloud data engineering and analytics.

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Instructor led live online Classes

Azure Data Engineer Online Training

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Azure Data Engineer Online Training?

Master Azure Data Engineering: Learn Azure Data Lake, Data Factory, Synapse Analytics, and Databricks.
Career Growth: Gain a globally recognized certification in cloud-based data engineering.
Hands-on Labs: Work on real-world projects using Azure data services.
In-Demand Skills: Learn data ingestion, transformation, and storage solutions on Azure.
Globally Recognized Certification: Showcase your expertise in Azure data engineering to employers worldwide.

Course Description

The Azure Data Engineer Online Training validates your ability to design, implement, and optimize data solutions using Microsoft Azure.

1. Data Engineers working on cloud-based data pipelines and ETL workflows. 2. Data Architects designing scalable data solutions. 3. IT Professionals implementing Azure data services. 4. Beginners looking to start a career in Azure data engineering.

1. Comprehensive curriculum covering Azure data services and best practices. 2. Hands-on practice with real-world cloud data scenarios. 3. 24/7 expert support for technical guidance.

What you'll learn

Introduction to Azure Data Engineering: Understanding Azure data architecture and services.

Data Storage and Processing: Working with Azure Data Lake, Blob Storage, and Synapse Analytics.

Data Integration and ETL with Data Factory: Designing data ingestion and transformation pipelines.

Big Data Processing with Azure Databricks: Implementing Apache Spark-based analytics.

Real-Time Analytics with Stream Analytics: Handling real-time data streaming.

Requirements

Basic SQL and Python Knowledge: Familiarity with database concepts and scripting.
Understanding of Cloud Computing: Knowledge of Azure fundamentals is beneficial.

Curriculum Designed by Experts

Azure Subscriptions
Azure Resources
Azure Free Tier Account
Azure Resource Manager
Azure Resource Manager Template
Azure Storage
Types of Azure Storage

Create a free tier Azure account
Create a web app service using Azure Portal
Create and Deploy ARM templates
Manage Azure Storage account
Manage Azure Cost and Billing

Understand the evolving world of data
Data abundance
Understanding the Data Engineering Problem
Understand job responsibilities
Understanding Data Engineering Processing - Extract Transform and Load
Overview of Azure Data Engineering Services
Understand data storage in Azure Storage
Understand data storage in Azure Data Lake Storage
Understand Azure Cosmos DB
Understand Azure SQL Database
Understand Azure Synapse Analytics
Understand Azure Stream Analytics
Understand Azure HDInsight
Understand other Azure data services

Navigating through Azure data engineering services

How to choose an Azure Storage Service in Azure
Create an Azure Storage Account
Connect an app to Azure Storage API
Connect to your Azure storage account
Explore Azure Storage security features
Understand storage account keys
Understand shared access signatures
Control network access to your storage account
Understand Advanced Threat Protection for Azure Storage
Explore Azure Data Lake Storage security features
Introduction to Blob storage
What are blobs?
Design a storage organization strategy

Add the storage client library to your app
Add Azure Storage configuration to your app
Connect your application to your Azure Storage account
Create Azure storage resources
Configure and initialize the client library
Get blob references
Blob uploads and downloads

Integrate data with Azure Data Factory or Azure Synapse Pipeline
Understand Azure Data Factory
Describe data integration patterns
Explain the data factory process
Understand Azure Data Factory components
Azure Data Factory security
Set-up Azure Data Factory
Create linked services
Create datasets
Create data factory activities and pipelines
Manage integration runtimes
Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipeline
List the data factory ingestion methods
Describe data factory connectors
Understand data ingestion security considerations

Use the data factory copy activity
Manage the self hosted integration runtime
Setup the Azure integration runtime

Explain Data Factory transformation methods
Describe Data Factory transformation types
Debug mapping data flow
Describe slowly changing dimensions
Choose between slowly changing dimension types
Understand data factory control flow
Work with data factory pipelines
Debug data factory pipelines
Add parameters to data factory components
Execute data factory packages
Describe SQL Server Integration Services
Understand the Azure-SIS integration runtime
Set-up Azure-SIS integration runtime
Run SSIS packages in Azure Data Factory
Migrate SSIS packages to Azure Data Factory
Configure a git repository with a development factory
Create and merge a feature branch
Deploy a release pipeline
Visually monitor pipeline runs
Integrate with Azure Monitor
Set up alerts
Rerun pipeline runs

Author an Azure Data Factory mapping data flow
Use Data Factory wrangling data
Use compute transformations within Data Factory
Integrate SQL Server Integration Services packages within Data Factory
Design and implement a Type 1 slowly changing dimension with mapping data flows
Integrate a Notebook within Azure Synapse Pipelines

What is Azure Synapse Analytics
How Azure Synapse Analytics works
When to use Azure Synapse Analytics
Create Azure Synapse Analytics workspace
Describe Azure Synapse Analytics SQL
Explain Apache Spark in Azure Synapse Analytics
Orchestrate data integration with Azure Synapse pipelines
Visualize your analytics with Power BI
Understand hybrid transactional analytical processing with Azure Synapse Link
Use Azure Synapse Studio

Explore Azure Synapse Analytics
Create pools in Azure Synapse Analytics
Identifying Azure Synapse pipeline components

Understand the Azure Synapse Analytical processes
Explore the Data hub
Explore the Develop hub
Explore the Integrate hub
Explore the Monitor hub
Explore the Manage hub

Create a new data flow or dataset, and manage the file
Use the Monitor hub to view pipeline and trigger runs, view the status of the various integration runtimes

Describe a modern data warehouse
Define a modern data warehouse architecture
Exercise - Identify modern data warehouse architecture components
Design ingestion patterns for a modern data warehouse
Understand data storage for a modern data warehouse
Understand file formats and structure for a modern data warehouse
Prepare and transform data with Azure Synapse Analytics

Serve data for analysis with Azure Synapse Analytics
Design a data warehouse schema
Create data warehouse tables
Load data warehouse tables
Query a data warehouse

Understand data load design goals
Explain load methods into Azure Synapse Analytics
Manage source data files
Manage singleton updates
Set-up dedicated data load accounts
Implement workload management
Simplify ingestion with the Copy Activity
Understand performance issues related to tables

Data Loading in Azure Synapse Analytics
Data Ingestion using Copy Activity
Implement workload management
Understand performance issues related to tables

Understand table distribution design
Use indexes to improve query performance
Understand query plans
Create statistics to improve query performance
Improve query performance with materialized views
Use read committed snapshot for data consistency
How does statistics affect a query plan?
Describe the integration methods between SQL and spark pools in Azure Synapse Analytics
Understand the use-cases for SQL and spark pools integration
Exercise: Integrate SQL and spark pools in Azure Synapse Analytics
Externalize the use of spark pools within Azure Synapse Workspace
Transfer data outside the synapse workspace using the PySpark connector
Explore the development tools for Azure Synapse Analytics
Understand transact-SQL language capabilities for Azure Synapse Analytics

Use table distribution and indexes to improve performance
Optimize common queries with result-set caching
Work with windowing functions
Work with approximate execution
Work with JSON data in SQL pools
Encapsulate transact-SQL logic with stored procedures
Authenticate in Azure Synapse Analytics
Transfer data between SQL and spark pool in Azure Synapse Analytics
Authenticate between spark and SQL pool in Azure Synapse Analytics

Scale compute resources in Azure Synapse Analytics
Pause compute in Azure Synapse Analytics
Manage workloads in Azure Synapse Analytics
Use Azure Advisor to review recommendations
Use dynamic management views to identify and troubleshoot query performance
Understand skewed data and space usage
Understand network security options for Azure Synapse Analytics
Configure Conditional Access
Configure authentication
Manage authorization through column and row level security
Manage sensitive data with Dynamic Data Masking
Implement encryption in Azure Synapse Analytics

Manage authorization through column and row level security

Get started with Azure Databricks
Identify Azure Databricks workloads
Understand key concepts
Use Apache Spark in Azure Databricks
Create a Spark cluster
Use Spark in notebooks
Use Spark to work with data files
Visualize data
Get Started with Delta Lake
Create Delta Lake tables
Create and query catalog tables
Use Delta Lake for streaming data
Get started with SQL Warehouses
Create databases and tables
Create queries and dashboards
Understand Azure Databricks notebooks and pipelines
Create a linked service for Azure Databricks
Use a Notebook activity in a pipeline
Use parameters in a notebook

Explore Azure Databricks
Run an Azure Databricks Notebook with Azure Data Factory
Use a SQL Warehouse in Azure Databricks
Use Delta Lake in Azure Databricks
Use Spark in Azure Databricks

In this project, you will design a secure, analytical, data ingestion, scalable, build and deploy data pipeline solution which you have learned as a part of this certification course
Documentation and presentation: Develop detailed documentation outlining the design and implementation of the Azure data solution, and effectively present the solution and its outcomes to business
Design and implement an Azure Data Solution

Integrating Azure Data Solutions with Azure Synapse Analytics
Integrating Azure Data Solutions with Power BI
Integrating Azure Data Solutions with Azure Logic Apps
Best Practices for Integrating Azure Data Solutions with Other Services

Working with the Azure Data Solutions with Azure Synapse Analytics
Build and deploy Azure Data Solutions with Power BI

Overview of Azure Security Features
Implementing Security for Azure Data Storage Solutions
Implementing Security for Azure Data Processing Solutions
Best Practices for Securing Data Solutions in Azure

To demonstrate the practical implementation of security and compliance measures in Azure data engineering to safeguard the organization's data
To demonstrate the practical implementation of compliance measures in Azure data engineering to ensure adherence to regulatory requirements

Monitoring and Alerting Strategies for Azure Data Solutions
Optimizing Data Processing Solutions for Performance and Cost
Using Azure Monitor to Monitor Data Solutions
Best Practices for Monitoring and Optimizing Azure Data Solutions

To demonstrate the practical implementation of monitoring measures in Azure data engineering solutions to ensure efficient system operation
Implementation of optimization measures in Azure data engineering solutions to ensure better system performance, reduce costs, and increase productivity

Case Studies on Implementing Azure Data Solutions
Emerging Trends and Technologies in Azure Data Engineering
Best Practices for Keeping Up with Industry Trends in Azure Data Engineering

Interview preparation material to assist you in getting ready for the upcoming interviews

Whitepapers
Reference Documents

Additional Resources (Part 1)
Additional Resources (Part 2)

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Azure Data Engineer Online Training

Read Review

Instructor led live online Classes

Azure Data Engineer Online Training