Azure DE

Section 1: Introduction to Azure and Data Engineering

Objective:Understand the fundamentals of data engineering and Azure cloud services.

Duration:4 hours

Topics:

• Overview of data engineering: Roles, responsibilities, and tools
• Introduction to Azure: Subscriptions, resource groups, and Azure Active Directory
• Azure data engineering ecosystem: ADF, Databricks, ADLS, Synapse, Azure SQL
• Setting up an Azure account and Azure CLI/PowerShell
• Best practices for security (RBAC, Key Vault) and cost management

Hands-On Lab:

• Create an Azure account, configure a resource group, and set up Azure Active Directory roles.
• Install Azure CLI and authenticate to manage resources programmatically.

Section 2: Azure Data Lake Storage (ADLS) for Data Lakes

Objective:Learn to use ADLS Gen2 as the foundation for data lakes.

Duration:6 hours

Topics:

• ADLS Gen2 fundamentals: Containers, directories, and hierarchical namespaces
• Security: Azure AD integration, access control lists (ACLs), and encryption
• Data organization: Partitioning, folder structures, and lifecycle management
• ADLS integration with ADF, Databricks, and Synapse
• Performance optimization: Concurrent writes, blob storage tuning, and caching

Hands-On Lab:

• Create an ADLS Gen2 account, upload sample datasets (e.g., CSV, Parquet), and configure lifecycle policies.
• Set up RBAC and ACLs to restrict access to specific folders.
• Mount ADLS in Databricks using service principal authentication.

Project:

• Design an ADLS-based data lake for a financial dataset, partitioned by year/quarter.

Section 3: Azure Data Factory (ADF) for ETL Orchestration

Objective:Build and orchestrate ETL pipelines using ADF.

Duration:8 hours

Topics:

• ADF overview: Pipelines, activities, and linked services
• Data ingestion: Copy Data activity, source/sink configurations, and partition discovery
• Data transformation: Data Flows, Mapping Data Flows, and Assert Transformations
• Orchestration: Triggers, parameters, and pipeline dependencies
• Monitoring and debugging: ADF Monitor, alerts, and Azure Monitor integration
• Performance optimization: Parallel processing, staging, and incremental loads

Hands-On Lab:

• Create an ADF pipeline to ingest data from ADLS to Azure SQL Database using Copy Data activity.
• Build a Mapping Data Flow to clean and transform a customer dataset, applying Assert Transformations for data quality.
• Schedule a pipeline with a tumbling window trigger and monitor execution.

Project:

• Develop an ADF pipeline to ingest and transform a sales dataset, storing curated data in ADLS.

Section 4: Azure Databricks for Big Data Processing

Objective:Process and analyze large-scale data with Databricks and PySpark.

Duration:8 hours

Topics:

• Databricks overview: Workspaces, clusters, and Delta Lake
• PySpark fundamentals: DataFrames, transformations, and actions
• Delta Lake: ACID transactions, versioning, and time travel
• Performance optimization: Caching, partitioning, adaptive query execution, and salting for skew
• Integration with ADLS, ADF, and Synapse
• Collaborative notebooks: Data exploration, visualization, and MLflow

Hands-On Lab:

• Set up a Databricks workspace and create a cluster with auto-scaling.
• Write a PySpark job to process a dataset from ADLS, applying salting (floor(rand() * salt_factor).cast(“string”)) to mitigate skew.
• Create a Delta table and perform upserts, leveraging time travel for auditing.

Project:

• Build a Databricks pipeline to process streaming data from ADLS, storing results in Delta Lake.

Section 5: Azure Synapse Analytics for Data Warehousing

Objective:Design and query data warehouses with Synapse Analytics.

Duration:6 hours

Topics:

• Synapse Analytics overview: Dedicated SQL pools, serverless SQL, and Spark pools
• Data loading: PolyBase, COPY INTO, and integration with ADLS
• Table design: Distribution (ROUND_ROBIN, HASH, REPLICATED), partitioning, and indexing
• Query optimization: Materialized views, result-set caching, and workload management
• Synapse serverless: Querying ADLS data with OPENROWSET
• Integration with Power BI for visualization

Hands-On Lab:

• Create a Synapse workspace and load data from ADLS into a dedicated SQL pool.
• Design a star schema for a retail dataset, applying HASH distribution and partitioning.
• Query ADLS Parquet files using serverless SQL and visualize results in Power BI.

Project:

• Build a Synapse data warehouse for a marketing dataset, creating dashboards with sample SQL queries.

Section 6: Azure SQL Database for Relational Data

Objective:Manage relational data with Azure SQL Database for analytics.

Duration:6 hours

Topics:

• Azure SQL Database fundamentals: Deployment models (Single, Elastic Pool, Managed Instance)
• Data ingestion: Bulk loading, BACPAC imports, and ADF integration
• Schema design: Indexes, partitioning, and normalization
• Query optimization: Query Store, execution plans, and indexing strategies
• Security: Transparent Data Encryption, dynamic data masking, and firewall rules
• High availability: Geo-replication and auto-failover groups

Hands-On Lab:

• Deploy an Azure SQL Database and load data from ADLS using ADF.
• Create indexed tables for a customer dataset and optimize query performance.
• Configure dynamic data masking and test access controls.

Project:

• Design an Azure SQL Database for an inventory dataset, integrating with Synapse for analytics.

Section 7: End-to-End Data Pipeline and Architecture

Objective:Integrate Azure services to build a scalable data pipeline.

Duration:6 hours

Topics:

• Medallion architecture: Bronze, Silver, Gold layers
• Orchestrating pipelines: ADF pipelines, Logic Apps, and Event Grid
• Real-time vs. batch processing: Azure Stream Analytics and Delta Live Tables
• Monitoring and logging: Azure Monitor, Log Analytics, and Databricks alerts
• Cost optimization: Auto-scaling, serverless options, and storage tiers
• Best practices: Data Vault modeling, static pruning, and version control with Azure DevOps

Hands-On Lab:

• Build a pipeline using ADF to ingest data, Databricks to transform it, and Synapse to store curated data.
• Implement static pruning in Databricks to skip irrelevant partitions (e.g., by FiscalYear).
• Set up Azure Monitor alerts for pipeline failures and optimize costs.

Project:

• Architect an end-to-end pipeline for a customer analytics use case, from raw ADLS data to a Synapse/Power BI dashboard.

Section 8: Capstone Project and Career Preparation

Objective:Apply skills to a real-world project and prepare for data engineering roles.

Duration:6 hours

Topics:

• Review of key concepts: ADF, Databricks, ADLS, Synapse, and Azure SQL
• Building a portfolio-worthy project with medallion architecture
• Career preparation: Resume building with Apache Spark expertise, LinkedIn optimization, and interview tips
• Common interview questions: PySpark coding, ADF pipelines, and Azure architecture
• Certification paths: Microsoft Certified: Azure Data Engineer Associate, Databricks Data Engineer Associate

Capstone Project:

• Design and implement a complete data pipeline for a fictional e-commerce company:
  • Ingest raw data into ADLS using ADF.
  • Transform data in Databricks using PySpark and Delta Lake, applying optimizations like salting.
  • Store curated data in Synapse and Azure SQL Database.
  • Query with Synapse serverless and visualize insights in Power BI.
  • Document the architecture with Data Vault principles and present findings.

Career Prep:

• Mock interview with PySpark coding (e.g., DataFrame operations, skew mitigation) and Azure architecture questions.
• Create a GitHub repository with project code, ADF pipelines, and Databricks notebooks, versioned using Azure DevOps.

Additional Resources

• Tools Covered: Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure SQL Database, PySpark, Delta Lake, Azure CLI, Azure Monitor
• Sample Datasets: Retail sales, customer data, and streaming logs
• Supplementary Materials: Cheat sheets for PySpark, SQL, ADF Data Flows, and Azure services; access to recorded sessions
• Community Support: Slack/Discord channel for peer collaboration and instructor Q&A

Assessment and Certification

• Quizzes: Weekly quizzes to reinforce concepts
• Labs and Projects: Graded hands-on assignments
• Capstone Evaluation: Assessed on pipeline functionality, optimization, and presentation
• Certificate: Awarded upon successful completion of the course and capstone project