Implement a Data Analytics Solution with Azure Databricks Training

The Implement Data Engineering Solutions using Azure Databricks course provides a practical introduction to designing, building, and operating modern data engineering solutions using Azure Databricks and the lakehouse architecture.

Throughout the programme, participants learn how to create scalable data pipelines that support enterprise analytics and machine learning workloads. Key areas include Azure Databricks workspace configuration, compute management, data governance with Unity Catalog, data ingestion and transformation with SQL and Python, reliable storage with Delta Lake, and production-grade pipeline deployment.

The course also addresses the wider operational requirements of enterprise data engineering, including security, lineage, monitoring, performance optimisation, governance, and cost efficiency.

Instructor-led learning is supported by practical labs aligned with Microsoft Learn and realistic organisational scenarios. This enables participants to apply Azure Databricks data engineering techniques directly and understand how they can be transferred into enterprise environments.

Participants should have:

• Experience writing SQL queries
• Practical knowledge of Python
• Familiarity with notebook-based development
• Understanding of data engineering concepts
• Knowledge of data warehousing fundamentals
• Familiarity with Azure services
• Basic knowledge of cloud security principles
• Basic version control experience using Git

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

• Configure and manage Azure Databricks workspaces
• Create and manage compute resources for different workloads
• Implement centralised data governance with Unity Catalog
• Ingest data using SQL and Python
• Work with batch and streaming data
• Use Delta Lake to create reliable and scalable data layers
• Apply data cleansing, validation, and quality checks
• Design and orchestrate production data pipelines
• Use Databricks Jobs and Workflows
• Apply CI/CD practices to Databricks workloads
• Monitor and troubleshoot production pipelines
• Apply performance optimisation techniques
• Improve cost efficiency through appropriate compute and processing strategies
• Deploy and maintain enterprise-scale data engineering solutions

Setting Up and Configuring an Azure Databricks Environment

This section introduces the Azure Databricks platform and the steps required to establish a development environment.

Topics include:

• Azure Databricks architecture
• Core platform components
• Workspace creation and configuration
• Collaborative development environments
• Clusters
• Compute resources
• Selecting compute for different workloads
• Development environment integration
• Git integration
• Version control
• Microsoft Entra ID
• Identity and access management
• Foundational workspace governance

Participants learn how to structure Databricks environments for both development and production use.

Securing and Governing Data with Unity Catalog

This module focuses on establishing centralised governance across Databricks environments.

Topics include:

• Unity Catalog architecture
• Metastores
• Catalogs
• Schemas
• Tables
• Centralised data access control
• Multi-workspace governance
• Role-Based Access Control
• Secure data operations
• Data lineage
• Auditing
• Compliance requirements
• Governance best practices

Participants learn how to control access to data consistently across teams and workspaces.

Preparing and Processing Data with Azure Databricks

This section focuses on ingesting, preparing, and transforming data.

Topics include:

• Data ingestion strategies
• Batch ingestion
• Streaming ingestion
• Data transformation with SQL
• Data transformation with Python
• Dataset preparation
• Delta Lake
• Reliable data storage
• Scalable storage
• Data quality checks
• Data cleansing
• Data validation
• Optimising transformations
• Performance considerations

Participants apply practical techniques for preparing raw data for analytics and machine learning workloads.

Working with Delta Lake

This section explores the role of Delta Lake within the lakehouse architecture.

Topics include:

• Delta Lake fundamentals
• Transactional data storage
• Schema management
• Reliable data pipelines
• Scalable data processing
• Data consistency
• Production data management
• Preparing data for analytics and ML workloads

Participants learn how the lakehouse approach combines the flexibility of a data lake with stronger reliability and management capabilities.

Designing and Orchestrating Data Pipelines

This module focuses on production-ready pipeline architecture.

Topics include:

• Data pipeline design
• Databricks Jobs
• Databricks Workflows
• Task dependencies
• Scheduling
• Pipeline orchestration
• Batch workloads
• Streaming workloads
• Failure handling
• Retry strategies
• Production pipeline design

Participants learn how to build reliable and manageable data pipelines for different types of workloads.

CI/CD and Deployment

This section examines how Databricks solutions can move from development into production.

Topics include:

• Continuous Integration
• Continuous Deployment
• Git-based development workflows
• Source control
• Deployment automation
• Environment management
• Development, test, and production separation
• Release processes
• Enterprise deployment practices

Participants learn how to create repeatable and controlled deployment processes for data engineering workloads.

Monitoring and Troubleshooting

This module focuses on the operational management of data pipelines.

Topics include:

• Pipeline monitoring
• Job monitoring
• Failure analysis
• Logging
• Error investigation
• Runtime diagnostics
• Performance monitoring
• Production troubleshooting

Participants learn how to identify operational issues and improve pipeline reliability.

Performance and Cost Optimisation

This section focuses on running Databricks workloads efficiently.

Topics include:

• Compute optimisation
• Cluster sizing
• Workload tuning
• Transformation performance
• Resource utilisation
• Pipeline efficiency
• Cost monitoring
• Cost reduction strategies
• Scalable architecture decisions

Participants evaluate the trade-offs between performance, scalability, and cost.

Enterprise-Scale Data Engineering

The final section considers how Databricks-based data engineering solutions can be operated at enterprise scale.

Topics include:

• Multi-workspace environments
• Governance at scale
• Production workload management
• Standardisation
• Operational reliability
• Security and compliance
• Scalable lakehouse architecture
• Enterprise data engineering best practices