Implement Data Engineering Solutions using Azure Databricks Training in Kazakhstan

This course provides a comprehensive, hands-on introduction to building modern data engineering solutions using Azure Databricks and the lakehouse architecture. Learners will explore how to design, develop, and optimise scalable data pipelines that support enterprise analytics and machine learning workloads.

Key focus areas include workspace configuration, data governance with Unity Catalog, data ingestion and transformation using SQL and Python, and deploying production-grade pipelines. Through instructor-led sessions and practical labs aligned to Microsoft Learn, learners will gain the confidence to apply data engineering techniques in real-world organisational contexts. We believe applied, hands-on learning is critical to transforming skills into measurable business outcomes.

Participants should have:

• Experience writing queries in SQL and working with Python, including notebooks
• Understanding of data engineering and data warehousing concepts
• Familiarity with Azure services and foundational security principles
• Basic knowledge of version control using Git

Target audience

This course is designed for:

• Data engineers responsible for building and maintaining data pipelines
• Analytics professionals transitioning into data engineering roles
• Cloud professionals working with data platforms in Azure
• Organisations seeking to develop scalable lakehouse solutions using Databricks

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

• Configure and manage Azure Databricks workspaces and compute resources
• Implement secure and scalable data governance using Unity Catalog
• Ingest, prepare, and transform data using SQL, Python, and Delta Lake
• Design and orchestrate reliable data pipelines for production workloads
• Apply monitoring and optimisation techniques to improve performance and cost efficiency
• Deploy and maintain enterprise-grade data engineering solutions in Azure

Setting up and configuring an Azure Databricks environment

• Understand Azure Databricks architecture and core components
• Configure workspaces for collaborative data engineering development
• Create and manage clusters and compute resources for different workloads
• Integrate development environments with Git for version control
• Apply identity and access management controls using Microsoft Entra ID
• Establish foundational governance practices across workspaces

Securing and governing data with Unity Catalog

• Explore Unity Catalog architecture, including metastores, catalogs, schemas, and tables
• Implement centralised data access control across workspaces
• Apply role-based access control for secure data operations
• Understand data lineage tracking and auditing capabilities
• Apply governance best practices to support compliance and organisational standards

Preparing and processing data with Azure Databricks

• Design data ingestion strategies for batch and streaming data
• Use SQL and Python to transform and prepare datasets for analysis
• Work with Delta Lake to enable reliable and scalable data storage
• Implement data quality checks, cleansing, and validation processes
• Optimise data transformations for performance and scalability

Deploying and maintaining data pipelines and workloads

• Build and orchestrate data pipelines using jobs and workflows
• Implement continuous integration and deployment for Databricks workloads
• Monitor pipeline performance and troubleshoot issues in production
• Apply optimisation techniques to improve efficiency and reduce costs
• Manage production workloads at scale across enterprise environments

Exams and assessments

This course does not include a formal exam. Learners will complete knowledge checks, instructor-led discussions, and practical lab exercises throughout the course to reinforce key concepts and validate their understanding.

Hands-on learning

This course includes:

• Guided labs aligned to Microsoft Learn modules
• Scenario-based exercises reflecting real-world data engineering challenges
• Instructor-led demonstrations of Azure Databricks capabilities
• Practical implementation of data pipelines, governance, and optimisation techniques