Azure Data Engineering Training

Azure Data Engineering

This comprehensive course is designed to equip data professionals with the skills required to design, implement, and manage enterprise-grade data solutions using the full spectrum of Azure data services. Participants will gain hands-on experience across a wide range of technologies, from foundational cloud architecture to advanced analytics and governance.

Key Areas of Focus:

Azure Architecture:

Learn how to design scalable, secure, and resilient cloud-based data solutions. Understand the principles of cloud architecture, resource organization, and cost optimization in the Azure ecosystem.

• Programming and Scripting – SQL, Python, Spark, and PySpark:

Build a strong foundation in SQL for querying and managing data, while also mastering Python for data manipulation, analysis, and automation. Learn to integrate these skills with Spark and PySpark to execute scalable data transformations and machine learning tasks.

Storage Solutions (Blob Storage & ADLS):

Explore Azure Blob Storage for unstructured data and Azure Data Lake Storage (ADLS) for big data workloads. Gain insights into data storage strategies, performance tuning, and lifecycle management to support diverse data requirements.

• Relational and Analytical Databases (Azure SQL & Azure Synapse Analytics):

Delve into Azure SQL for robust relational database management and Azure Synapse Analytics for integrated big data and data warehousing. Learn how to architect data models, optimize queries, and implement data transformations to drive business insights.

• Data Orchestration and Pipeline Management (Azure Data Factory):

Build and manage scalable data pipelines with Azure Data Factory. Learn to automate data ingestion, transformation, and integration processes across various data sources and sinks.

• Advanced Data Processing (Azure Databricks & Azure Stream Analytics):

Harness the power of Azure Databricks for big data processing and machine learning, and explore Azure Stream Analytics for real-time data ingestion and analysis. Develop skills to manage both batch and streaming data workflows effectively.

• Data Governance and Cataloging (Azure Purview):

Understand the importance of data governance, lineage, and compliance using Azure Purview. Learn how to catalog, monitor, and secure your data assets to ensure they meet organizational and regulatory standards.

Throughout the course, you will engage in practical labs, real-world projects, and case studies that bridge the gap between theory and practice. By the end of the program, you will be well-equipped to leverage Azure’s powerful data services to create robust, scalable, and secure data engineering solutions that drive business value in a modern, cloud-first environment.

Course Modules

Azure Data Engineer Fundamentals

Module 1: Azure Cloud Fundamentals

Overview of Azure Architecture

• Azure regions, subscriptions, and resource groups
• Azure service categories (compute, storage, networking, etc.)
• Designing scalable and secure cloud solutions

Blob Storage

• Understanding Blob Storage concepts (containers, blobs, tiers)
• Data redundancy options and lifecycle management

Entra ID (Azure Active Directory)

• Overview of identity and access management in Azure
• Role-Based Access Control (RBAC) and authentication flows

Virtual Network (VNet)

• Fundamentals of virtual networking in Azure
• Subnets, IP addressing, and network security groups

Azure Functions

•  Introduction to serverless computing and event-driven architectures
•  Creating, deploying, and scaling Azure Functions

Azure SQL

• Overview of Azure SQL Database and managed instances
• Performance, scalability, and security features

Logic Apps

• Building and orchestrating workflows without code
• Integrating various Azure and external services

Azure Key Vault Services

• Secure storage for keys, secrets, and certificates
• Integrating Key Vault with other Azure services for secure access

Module 2: Azure Data Lake Storage (ADLS)

Introduction to Azure Data Lake Storage

• Differences between ADLS Gen1 and Gen2
• Use cases for data lakes in modern architectures

Provisioning and Configuring ADLS

• Creating an ADLS account
• Setting up hierarchical namespaces and access control lists (ACLs)

Data Security and Access Management

• Managing access with role-based permissions and ACLs
• Best practices for data encryption and compliance

Performance Optimization and Best Practices

•  Partitioning strategies and file organization
• Cost management and monitoring data access patterns

Module 3: SQL

Introduction to Relational Databases

• Database concepts, tables, relationships, and normalization
•  Overview of popular SQL database systems

SQL Fundamentals

•  Basic SELECT statements: FROM, WHERE, ORDER BY
• Filtering and sorting data

Data Manipulation Language (DML)

• Inserting, updating, and deleting data
• Hands-on examples with practical datasets

Aggregations and Grouping

• Using GROUP BY, HAVING, and aggregate functions
• Data summarization techniques

Joins and Subqueries

• INNER, LEFT, RIGHT, and FULL OUTER JOINS
• Using subqueries for complex data retrieval

Advanced Query Techniques (Introduction)

• Views, indexes, and transactions
• Best practices for query optimization

Module 4: Python & PySpark

Introduction to Python for Data Engineering:

Environment Setup:

• Installing Python (via Anaconda or a virtual environment)
• Setting up development tools (Jupiter Notebook, VS Code)

Basic Syntax and Data Types:

• Variables, strings, numbers, Booleans, and type conversions
• Data structures: lists, tuples, dictionaries, and sets

Control Structures and Functions:

• Conditionals, loops, and error handling
• Writing functions, understanding scope, and modular programming

Working with Python Libraries for Data Processing:

Data Manipulation with Pandas and NumPy:

• Introduction to Pandas Data Frames and NumPy arrays
• Basic data cleaning, transformation, and aggregation tasks

Introduction to PySpark: Overview of PySpark and Apache Spark:

• The role of PySpark in big data analytics
• Understanding Spark’s architecture and how PySpark fits in

Setting Up PySpark:

• Installing and configuring PySpark locally or within a managed environment (e.g., Databricks)
• Initiating a SparkSession and understanding its importance

Working with PySpark Data Structures:

PySpark DataFrames and RDDs:

• Creating DataFrames and understanding schema inference
• Introduction to Resilient Distributed Datasets (RDDs) and their transformation operations:

Basic Data Transformations

• Filtering, mapping, and aggregating data using PySpark DataFrame API
• Applying simple User-Defined Functions (UDFs) to extend functionality

Azure Data Factory

Module 1: Introduction to Azure Data Factory

Overview of ADF:

• Understanding the role of Azure Data Factory within the Azure ecosystem.
• Key components: pipelines, activities, datasets, linked services, and triggers.

Use Cases and Scenarios:

• Batch data processing.
• Data ingestion and migration.
• ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes.
• Data orchestration and integration with other Azure services

Module 2: Core Concepts and Architecture

Data Integration and Orchestration:

• How ADF facilitates data movement and transformation.
• Differences between code-free (visual) data flows and code-based transformations.

ADF Pipeline Structure:

• Components of a pipeline and how they work together.
• Understanding activities: Copy Activity, Data Flow Activity, Stored Procedure Activity, etc.

Linked Services and Datasets:

• Defining connections to data sources and sinks.
• Configuring datasets to represent data structures in source/target systems.

Module 3: Data Ingestion and Movement

• Copy Activity:
  • Configuring and optimizing the Copy Activity for high-volume data transfers.
  • Handling different data formats: structured, semi-structured, and unstructured data.
• Integration Runtime (IR):
  • Types of Integration Runtime: Azure IR, Self-hosted IR, and Azure-SSIS IR.
  • When and how to use each type to support data movement across various environments.
• Connecting to Diverse Data Sources:
  • Ingesting data from on-premises databases, cloud storage, SaaS applications, and more.
  • Authentication methods and connectivity options (e.g., managed identities, service principals).

Module 4: Data Transformation Techniques

• Mapping Data Flows:
  • Designing and executing data transformation logic visually.
  • Understanding transformation components: joins, aggregations, lookups, conditional splits, etc.
  • Performance considerations and tuning in data flows.
• Custom Transformations:
  • Leveraging Azure Databricks or Azure Synapse for more complex transformations..
  • Incorporating custom activities (e.g., Azure Functions or custom) within pipelines.

Module 5: Monitoring, Logging, and Debugging

• ADF Monitoring Tools:
  • Using the ADF monitoring dashboard to track pipeline runs, activity progress, and performance.
  • Setting up alerts and notifications for failures or performance bottlenecks.
• Logging and Diagnostics:
  • Configuring diagnostic settings to capture detailed logs.
  • Integrating with Azure Monitor and Log Analytics for centralized logging and alerting.

Module 6: Performance Tuning and Optimization

• Optimizing Data Movement:
  • Tuning copy activities: parallelism, batch sizes, and performance settings.
  • Best practices for scaling Integration Runtimes.
• Optimizing Data Flows:
  • Performance considerations in mapping data flows (e.g., partitioning, caching strategies).
  • Monitoring resource usage and adjusting pipeline design to minimize latency and maximize throughput.

Azure Databricks

Module 1: Databricks Platform

• Overview:
  • Introduction to the Databricks environment and its core capabilities
  • Understanding the collaborative workspace and development tools.
• Cluster Management:
  • Creating, configuring, and managing clusters
  • Autoscaling, cluster sizing, and cost optimization best practices.
• Jobs and Workflows:
  • Building and scheduling jobs
  • Orchestrating workflows with notebooks, jobs, and pipelines

Module 2: Apache Spark Concepts

• Spark Architecture:
  • Understanding the Spark execution model (driver, executors, tasks, and partitions)
  • Overview of distributed computing fundamentals
• Spark SQL & DataFrames:

• • Working with Spark SQL for querying data
  • Transformations and actions using DataFrames and Datasets
• Spark Data Processing:

• • Techniques for batch processing of large-scale datasets
  • Best practices for optimizing Spark applications

Module 3: Delta Lake and the Data Lakehouse

• Introduction to Delta Lake:
  • Fundamentals of Delta Lake and its role in modern data architectures
  • Benefits such as ACID transactions and schema enforcement
• Implementing a Lakehouse Architecture:

• • Combining the strengths of data lakes and data warehouses
  • Designing robust ETL pipelines using Delta Lake
• Data Versioning and Governance:

• • Managing data updates and ensuring data quality
  • Techniques for schema evolution and time travel

Module 4: Data Ingestion and Integration

• Ingestion Techniques:
  • Best practices for ingesting batch data from various sources
  • Handling structured, semi-structured, and unstructured data
• Streaming Data:
• • Introduction to real-time data ingestion with Spark Structured Streaming
  • Concepts such as event time, watermarks, and windowing
• Integrating with External Systems:
• • Connecting Databricks with external storage systems (e.g., AWS S3, Azure Blob, GCS)
  • Integrating with messaging systems (e.g., Kafka, Kinesis) for real-time data streams

Module 5: ETL Pipelines and Data Transformation

• Designing ETL/ELT Workflows:
  • Strategies for designing efficient extraction, transformation, and load processes
  • Modular pipeline design for reusability and scalability
• Data Cleaning and Enrichment:
• • Techniques to clean and enrich raw data
  • Utilizing Spark functions and User-Defined Functions (UDFs) for custom transformations

Module 6: Performance Tuning and Optimization

• Optimizing Spark Jobs:
  • Methods to tune Spark configurations for enhanced performance
  • Managing memory, partitioning, and resource allocation
• Query Optimization:
• • Best practices for writing and optimizing Spark SQL queries
  • Strategies such as caching, broadcast joins, and query plan analysis< /li>

Module 7: Data Security, Governance, and Compliance

• Secret Scope:
  • Managing sensitive information and credentials securely using secret scopes
  • Integrating with external secret management systems (e.g., Azure Key Vault)
• Unity Catalog:
• • Overview of Unity Catalog for centralized metadata and data governance
  • Implementing data lineage and access control policies< /li>
• Regulatory Compliance (GDPR, HIPAA, etc.)::
• • Ensuring data processing and storage comply with industry regulations
  • Best practices for auditability, data privacy, and security in Databricks environments< /li>