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Home > Technical Courses > > Implement Data Engineering Solutions Using Azure Databricks (DP-750)
You will build secure, scalable lakehouse solutions with Azure Databricks and Unity Catalog.
Master end-to-end data engineering with Azure Databricks and Unity Catalog. This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.
Category
ID
Duration
Level
Price
Azure
14114
4 Day(s)
Intermediate
$2,595.00
Objectives
In this course, you will learn to: • Create and manage scalable Azure Databricks workspaces. • Configure and optimize compute resources, including clusters and SQL warehouses. • Integrate workspaces with Azure Data Lake Storage (ADLS Gen2) and external Microsoft services. • Implement central governance using the Unity Catalog three-level namespace. • Manage identity, credentials, and access control policies for data assets. • Apply fine-grained security rules, such as row-level filtering and column masking. • Track data lineage and configure audit logging for security compliance. • Design secure data sharing across environments using Delta Sharing. • Design efficient data models optimized for a lakehouse architecture. • Ingest batch and streaming data using tools like Auto Loader and COPY INTO. • Cleanse and transform data through structured data pipelines. • Apply data quality expectations and handle schema drift or enforcement. • Implement Delta Lake features, including Time Travel, ACID transactions, and MERGE operations. • Orchestrate automated data pipelines using Databricks Lakeflow Jobs. • Implement development lifecycles using Git integrations and Databricks Asset Bundles. • Monitor pipeline execution and configure alerts for processing errors. • Troubleshoot failed workloads and debug notebooks or cluster issues. • Tune query performance using OPTIMIZE, VACUUM, and Liquid Clustering.
Questions?
Microsoft Certified: Azure Databricks Data Engineer Associate
The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. You should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, you should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.
Implement Data Engineering Solutions Using Azure Databricks (DP-750)
Class Schedule
Live Online
Course Overview
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