Azure Databricks for Data Engineering Training Course

 Azure Databricks for Data Engineering Training Course 

Introduction 

Azure Databricks for Data Engineering Training Course is designed to equip participants with practical skills in modern data engineering, cloud data management, big data processing, and enterprise data analytics using Microsoft Azure and Apache Spark. The course provides comprehensive knowledge of the Azure Databricks Lakehouse Platform, Delta Lake architecture, Apache Spark, PySpark, SQL, data pipelines, and distributed computing. Participants will explore advanced data ingestion, data transformation, data orchestration, and real-time data streaming techniques to develop scalable, secure, and high-performance data engineering solutions. Emphasis is placed on cloud-native data engineering, data lakehouse architecture, automated workflows, data governance, and enterprise data integration. 

Through practical exercises and global industry case studies, participants will learn to build robust Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines, optimize large-scale datasets, and implement reliable data processing workflows. The training covers integration with Azure Data Factory, Azure Data Lake Storage, Microsoft Power BI, and other enterprise technologies. Participants will gain hands-on experience in developing production-ready data pipelines, implementing data quality frameworks, managing metadata, and applying DevOps practices. By the end of the course, learners will be equipped to support digital transformation, artificial intelligence, machine learning, and data-driven decision-making through modern Azure Databricks data engineering solutions. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand Azure Databricks architecture and cloud data engineering principles.
2. Develop scalable data pipelines using Apache Spark and PySpark.
3. Implement modern data lakehouse and Delta Lake architectures.
4. Design efficient ETL and ELT workflows.
5. Perform advanced data transformation using Spark SQL.
6. Integrate Azure Databricks with Microsoft Azure services.
7. Implement real-time data ingestion and streaming analytics.
8. Optimize distributed computing and Spark workloads.
9. Apply data governance, security, and access control.
10. Implement automated data orchestration and workflow management.
11. Develop reliable data quality and validation frameworks.
12. Apply CI/CD and DevOps practices to data engineering.
13. Build enterprise-ready data engineering solutions.

Organizational Benefits
 

1. Improved enterprise data processing and operational efficiency.
2. Reduced infrastructure and data management costs.
3. Enhanced data quality and reliability.
4. Faster business intelligence and reporting.
5. Scalable cloud-based data engineering infrastructure.
6. Improved regulatory compliance and data governance.
7. Streamlined enterprise data integration.
8. Better support for artificial intelligence and machine learning.
9. Increased productivity through automated data pipelines.
10. Stronger data-driven strategic decision-making.

Target Audiences
 

1. Data Engineers and Big Data Engineers.
2. Cloud Solutions Architects.
3. Database Administrators.
4. Business Intelligence Developers.
5. Data Scientists and Data Analysts.
6. ETL Developers and Data Integration Specialists.
7. IT Managers and Enterprise Architects.
8. Software Developers and Cloud Computing Professionals.

Course Duration: 10 days

Course Modules

Module 1: Introduction to Azure Databricks
 

• Azure Databricks architecture and workspace configuration.
• Databricks clusters, notebooks, and compute resources.
• Apache Spark fundamentals and distributed computing.
• Lakehouse architecture and modern data engineering.
• Workspace administration and environment management.
• Case study: Enterprise cloud analytics adoption at Shell.

Module 2: Apache Spark and PySpark Fundamentals
 

• Spark architecture, RDDs, DataFrames, and datasets.
• PySpark programming and DataFrame operations.
• Transformations, actions, and lazy evaluation.
• Partitioning and distributed data processing.
• Spark SQL integration and query execution.
• Case study: Large-scale data processing at Netflix.

Module 3: Data Ingestion and Integration
 

• Batch data ingestion techniques.
• Connecting external databases and cloud storage.
• Data ingestion using Auto Loader.
• Structured and semi-structured data processing.
• Incremental data loading strategies.
• Case study: Enterprise data integration at Walmart.

Module 4: Azure Data Lake Storage Integration
 

• Azure Data Lake Storage Gen2 architecture.
• Storage containers, access controls, and permissions.
• Connecting Databricks to cloud storage.
• Managing Parquet, JSON, CSV, and Avro files.
• Secure storage access and identity management.
• Case study: Cloud data lake implementation at Siemens.

Module 5: Delta Lake and Lakehouse Architecture
 

• Delta Lake fundamentals and architecture.
• ACID transactions and schema enforcement.
• Time travel and version management.
• Delta tables and data reliability.
• Medallion architecture: Bronze, Silver, and Gold.
• Case study: Lakehouse implementation for enterprise analytics at Shell.

Module 6: Advanced Data Transformation
 

• Advanced PySpark transformations and functions.
• Data cleansing and normalization.
• Joins, aggregations, and window functions.
• Handling missing values and duplicates.
• Complex business transformation workflows.
• Case study: Customer data transformation at Amazon.

Module 7: Spark SQL and Query Optimization
 

• SQL queries and Databricks SQL.
• Query execution plans and performance analysis.
• Indexing alternatives and data skipping.
• Caching, partitioning, and file optimization.
• Advanced analytical queries.
• Case study: High-performance analytics at Microsoft.

Module 8: ETL and ELT Pipeline Development
 

• Designing scalable ETL and ELT pipelines.
• Building reusable data processing workflows.
• Pipeline error handling and recovery.
• Incremental processing and change data capture.
• Data validation and pipeline monitoring.
• Case study: Enterprise data pipelines at Unilever.

Module 9: Azure Data Factory Integration
 

• Azure Data Factory architecture and components.
• Integrating Databricks notebooks with pipelines.
• Pipeline triggers and scheduling.
• Parameterization and dynamic workflows.
• Monitoring and troubleshooting integrations.
• Case study: Automated enterprise integration at Accenture.

Module 10: Real-Time Data Streaming
 

• Structured Streaming fundamentals.
• Processing real-time data streams.
• Kafka and event-driven data integration.
• Watermarking and window-based processing.
• Streaming reliability and fault tolerance.
• Case study: Real-time transaction analytics in global banking.

Module 11: Data Quality and Governance
 

• Data quality frameworks and validation.
• Unity Catalog and centralized governance.
• Metadata management and data lineage.
• Role-based access control and auditing.
• Regulatory compliance and secure data sharing.
• Case study: Enterprise data governance in financial services.

Module 12: Workflow Orchestration and Automation
 

• Databricks Workflows and job scheduling.
• Task dependencies and pipeline orchestration.
• Parameterized notebook execution.
• Automated monitoring and notifications.
• Workflow troubleshooting and recovery.
• Case study: Automated data operations at multinational retailers.

Module 13: Performance Optimization and Cost Management
 

• Spark cluster sizing and configuration.
• Adaptive Query Execution.
• File compaction and data optimization.
• Photon acceleration and workload tuning.
• Compute utilization and cloud cost management.
• Case study: Large-scale cloud workload optimization at Adobe.

Module 14: DevOps and CI/CD for Data Engineering
 

• Version control using Git and GitHub.
• Databricks Asset Bundles.
• Automated testing and deployment pipelines.
• Development, staging, and production environments.
• Infrastructure automation and release management.
• Case study: Continuous data pipeline deployment at Microsoft.

Module 15: Advanced Data Engineering Capstone Project
 

• Designing an enterprise Azure Databricks solution.
• Developing end-to-end batch and streaming pipelines.
• Implementing Delta Lake and Medallion architecture.
• Integrating governance, security, and monitoring.
• Testing, optimizing, and deploying production workflows.
• Case study: Building an enterprise lakehouse for global retail analytics.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Microsoft Azure Databricks.
• Hands-on PySpark, Spark SQL, and Delta Lake exercises.
• Real-world enterprise projects and international case studies.
• Group assignments and collaborative pipeline development.
• Capstone projects, assessments, and practical evaluations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.