Azure Data Engineering: The Driving Force of Modern Data Analytics
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The Azure Data Engineering program equips you with the skills to design and manage data pipelines using Microsoft Azure and big data technologies. This all-inclusive training covers vital concepts such as ETL processes, data transformation, automation, and cloud-native data solutions. You’ll gain hands-on experience with tools like Azure Data Factory, Databricks, and Synapse Analytics, while mastering best practices for data modeling, pipeline optimization, and large-scale data processing. With real-world projects and practical experience, this program prepares you for a successful career in Azure Data Engineering.
Course Overview
The Azure Data Engineering Training Program empowers professionals to excel in the field of data engineering using Microsoft Azure. Through hands-on projects and expert-led instruction, participants gain proficiency in data integration, ETL processes, and cloud-based data solutions. This program prepares participants for the job market with practical labs, real-world scenarios, and interview coaching, equipping them for success in Azure data engineering roles.
Distinctive Advantages
Training Program Outline : Empowering Today's Data Solutions: Python Data Engineering
Module 1: Introduction to Azure Data Engineering
- Overview of Azure services
- Role and responsibilities of a data engineer
- Key concepts in cloud computing
- Introduction to Azure portal, subscriptions, and resources
Module 2: Data Storage Options in Azure
- Azure Storage: Blob, Table, Queue, and Files
- Azure Data Lake Storage (ADLS): Features and use cases
- Azure SQL Database: Key features and deployment
- Cosmos DB: NoSQL database options
- Storage security best practices
Module 3: Azure Data Factory (ADF)
- Introduction to ADF and data orchestration
- Creating and scheduling pipelines
- Data movement and transformation activities
- Integration with on-premise and cloud sources
- Monitoring and optimizing ADF pipelines
Module 4: Data Ingestion & Transformation
- Azure Databricks: Fundamentals and workspace setup
- Data ingestion with Apache Spark on Databricks
- Transformations using PySpark and Databricks SQL
- Managing and optimizing big data pipelines
Module 5: Working with Azure Synapse Analytics
- Introduction to Synapse Analytics and key features
- Synapse Studio: Managing data, notebooks, and pipelines
- Data integration with Synapse Pipelines
- Big data and machine learning integration
- Monitoring performance and costs
Module 6: Data Security & Compliance
- Encryption, Authentication, and Authorization
- Managing security in data storage services (Blob, SQL DB, etc.)
- Implementing Azure RBAC (Role-Based Access Control)
- Managing compliance in data storage and movement
Module 7: Real-time Data Processing with Stream Analytics
- Introduction to real-time data streaming
- Configuring Azure Stream Analytics jobs
- Integration with Event Hubs, IoT Hub, and Kafka
- Querying streaming data with Query language
- Real-time dashboards and Alerting
Module 8: : Data Governance and Monitoring
- Introduction to Azure Purview: Data cataloging and lineage
- Data Monitoring and Logging
- Troubleshooting performance issues
- Monitoring data quality and governance standards
Module 9: Advanced Analytics with Power BI & Azure
- Connecting Power BI to Azure data sources
- Building and optimizing reports and dashboards
- Real-time data visualization
- Embedding Power BI reports in Azure applications
Module 10: Capstone Project
- End-to-end data pipeline implementation
- Real-time data streaming solutions
- Integrating multiple Azure services in a single project
- Data security and compliance in a live scenario
- Performance optimization and monitoring tasks
Module 11: Soft Skills Training, Interview Preparation, & Professional Resume Building
Soft Skills Training
- Communication skills for Data Engineers
- Collaboration in cross-functional teams
- Problem-solving and critical thinking
Interview Preparation
- Common interview questions for Data Engineering roles
- Behavioural interview techniques
- Mock interviews and feedback sessions
Professional Resume Building
- Crafting a resume tailored to Data Engineering roles
- Highlighting technical skills and project experience
- LinkedIn profile optimization and online presence