Implement Data Engineering Solutions using Azure Databricks is a comprehensive, hands-on course focused on developing modern data engineering solutions with Azure Databricks and the lakehouse architecture. The program explores how scalable data pipelines can be designed, developed, managed and optimised to support enterprise analytics and machine learning workloads.
Learners progress through an end-to-end data engineering journey, beginning with Azure Databricks environment configuration and continuing through data governance, ingestion, transformation, pipeline orchestration and production workload management.
A major area of the course is establishing centralised and secure data governance with Unity Catalog. Learners examine metastores, catalogs, schemas and tables while exploring how access can be managed across workspaces, how appropriate permissions can be applied, and how data lineage and auditing capabilities support wider governance requirements.
For data processing, the course covers the ingestion, preparation and transformation of batch and streaming data using SQL and Python. Delta Lake is introduced as a foundation for reliable and scalable data storage, alongside practical approaches to data quality, cleansing, validation and transformation optimisation.
Later sections focus on building dependable data pipelines using Databricks jobs and workflows, introducing CI/CD practices, monitoring production workloads, troubleshooting issues and improving both performance and cost efficiency.
Through instructor-led sessions and practical labs aligned with Microsoft Learn, learners develop the confidence to apply these data engineering techniques within realistic enterprise environments.
