Azure Data Engineer Associate Certification Preparation (DP-203)
Mastering Data Engineering on Microsoft Azure
Duration
5 Days (8 hours per day)
Level
Intermediate to Advanced Level
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This course is designed to prepare participants for the Microsoft DP-203 certification by equipping them with practical skills and theoretical knowledge in data storage, data processing, data security, and monitoring solutions on Azure. Participants will gain hands-on experience in Azure Synapse Analytics, Azure Data Lake, Azure Data Factory, Azure Databricks, and real-time data processing solutions.
After completing this course you will be able to:
- Design robust Azure data architectures using modern storage solutions.
- Build and manage end-to-end ETL pipelines with Azure Data Factory.
- Leverage Azure Synapse Analytics and Databricks for scalable data processing.
- Implement strong security measures including RBAC, encryption, and data masking.
- Monitor and optimize data solutions using Azure Monitor and Log Analytics.
- Apply hands-on skills and best practices in real-world Azure data engineering scenarios.
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- Aspiring Data Engineers looking to specialize in Azure-based solutions
- Data Analysts & Data Scientists transitioning into data engineering roles
- BI Developers & Solution Architects who design and implement data solutions
- IT Professionals with experience in databases, ETL, or cloud computing
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- Overview of the Role of a Data Engineer
- Understanding Big Data & Modern Data Architecture
- Azure Data Engineering Services: Overview & Use Cases
- Data Processing in Azure: Batch vs. Streaming vs. Real-time
- Data Storage Options in Azure: Structured, Semi-structured, Unstructured
- Navigating the Azure Portal & Resource Management
- Setting up an Azure Subscription & Resource Groups
- Introduction to Azure Storage Services & Types
- Azure Blob Storage vs. Azure Data Lake Storage (ADLS) Gen2
- Hierarchical Namespace & Security in ADLS
- Access Control Mechanisms: Role-Based Access Control (RBAC) & ACLs
- Managing Storage Accounts, Containers & Access Keys
- Configuring Data Lifecycle Management & Tiering
- Hands-On Lab: Setting Up Azure Storage & ADLS Gen2
- Introduction to Azure Synapse Analytics & Key Features
- Understanding Dedicated SQL Pools vs. Serverless SQL Pools
- Partitioning Strategies & Data Distribution Concepts
- Indexing, Performance Tuning & Caching in Synapse
- Loading Data into Synapse using COPY & PolyBase
- Integrating Synapse with Power BI & Reporting
- Hands-On Lab: Creating a Dedicated SQL Pool in Synapse
- Understanding ETL vs. ELT & Data Ingestion Strategies
- Creating Data Pipelines in Azure Data Factory
- Working with Linked Services, Datasets & Pipelines
- Using Data Flow for Data Transformation in ADF
- Parameterization & Expressions in Data Pipelines
- Monitoring & Debugging Data Pipelines
- Hands-On Lab: Implementing an ETL Pipeline with ADF
- Introduction to Streaming Data & Real-time Processing
- Using Azure Event Hubs & IoT Hub for Streaming Data
- Implementing Azure Stream Analytics (ASA) Queries
- Integrating ASA with Power BI & SQL Database
- Scaling & Optimizing Streaming Pipelines
- Apache Kafka on Azure: When to Use It?
- Hands-On Lab: Ingesting Real-time Data with Event Hubs & ASA
- Introduction to Apache Spark & Databricks Concepts
- Databricks Clusters: Standard, High Concurrency & GPU
- Using Notebooks & Writing PySpark Code
- ETL with Databricks: Connecting to ADLS & Synapse
- Optimizing DataFrames & Managing Jobs in Databricks
- Delta Lake: Advantages & Implementing Change Data Capture (CDC)
- Hands-On Lab: Writing ETL Jobs with Azure Databricks
- Understanding Batch Processing & its Challenges
- Implementing Data Aggregation & Cleansing Strategies
- Using T-SQL for Data Processing in Synapse Analytics
- Working with PolyBase & External Tables
- Automating Batch Pipelines with Azure Data Factory
- Performance Optimization Techniques
- Hands-On Lab: Implementing a Batch Processing Pipeline
- Understanding Structured Streaming in Databricks
- Writing Windowed Aggregation Queries
- Handling Late Arriving Data & Watermarking
- Processing Data Streams using Delta Lake
- Fault Tolerance & Checkpointing in Streaming
- Hands-On Lab: Implementing Streaming ETL with Databricks
- Azure Security Controls: Role-Based Access Control (RBAC)
- Implementing Data Encryption: At-Rest & In-Transit
- Data Masking & Row-Level Security
- Key Vault for Secure Credential Management
- Firewall & Virtual Network Integration for Data Security
- Hands-On Lab: Configuring Security in Synapse & ADLS
- Introduction to Azure Monitor & Log Analytics
- Setting up Alerts & Metrics for Data Pipelines
- Monitoring Query Performance in Synapse
- Profiling Data in Databricks
- Using Application Insights for Logging
- Hands-On Lab: Monitoring Data Pipelines with Azure Monitor
- Cost Optimization Strategies for Data Pipelines
- Indexing & Query Optimization in Synapse
- Scaling Databricks Clusters Efficiently
- Optimizing Data Ingestion with ADF
- Performance Tuning in Stream Analytics
- Hands-On Lab: Implementing Performance Best Practices
- Understanding DP-203 Exam Structure & Domains
- Key Exam Tips & Common Pitfalls
- Practicing with Exam-style Questions & Case Studies
- Hands-On Exam Simulation & Performance Review
- Final Q&A & Certification Readiness Checklist
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- Basic understanding of cloud computing and Azure services (recommended: AZ-900)
- Fundamental knowledge of SQL, Python, or Spark
- Experience working with databases and data structures
