Azure Databricks Data Engineer Training Course (DP-750)
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Description of the training: Azure Databricks Data Engineer Training Course (DP-750)
This hands-on course equips you to design, deploy and operate data engineering solutions on Azure Databricks. Aligned with the Microsoft DP-750 exam outline (Azure Data Engineer Associate path), it covers the full chain: environment setup, governance with Unity Catalog, data ingestion and transformation, Lakeflow pipelines and Spark workload optimisation. It builds naturally on the fundamentals of the Microsoft Azure DP-900 course and on an Azure foundation you can consolidate with the Microsoft Azure AZ-104 course.
Over 4 days with a strong hands-on focus, you build a modern data pipeline step by step: creating catalogs and Delta tables, streaming ingestion with Auto Loader, transformation and quality control, orchestration via Lakeflow Jobs, then monitoring and optimisation. Every concept is applied immediately in a lab on a Databricks workspace. To extend toward analytics and BI, the Microsoft Fabric course is an ideal complement. Sessions run in a small group of 1 to 3 participants.
Good to know
Our inter-company sessions are guaranteed from a single registration (no risk of postponement except in cases of force majeure). A 30-day follow-up support period comes with the course and a certificate of completion is issued. Explore our full catalogue in the Cloud Computing training courses category.
Objectives of the training: Azure Databricks Data Engineer Training Course (DP-750)
By the end of the course, you will be able to:
- Set up an Azure Databricks workspace (compute, Photon, runtime) and organise Unity Catalog objects.
- Secure and govern data: privileges, table/column/row-level access control, lineage and audit.
- Ingest data in batch and streaming with Lakeflow Connect, Auto Loader and Spark Structured Streaming.
- Cleanse, transform and validate data with quality constraints and schema drift management.
- Design, schedule and deploy Lakeflow pipelines (Jobs, declarative pipelines, Asset Bundles, Git CI/CD).
- Monitor, troubleshoot and optimise Spark workloads and Delta tables (OPTIMIZE, VACUUM, Azure Monitor).
Prerequisites of the training: Azure Databricks Data Engineer Training Course (DP-750)
Because every participant is unique, a preliminary interview tailors the content. Basic comfort with SQL and Python plus notions of Git and Data Lake are recommended. Some Azure experience is a plus but not required.
Detailed programme of the training: Azure Databricks Data Engineer Training Course (DP-750)
Module 1 — Set up and configure an Azure Databricks environment
- Choose and configure compute (job, serverless, SQL warehouse, classic, shared): autoscaling, Photon, runtime, pooling.
- Install libraries and manage compute access permissions.
- Create and organise Unity Catalog objects: catalogs, schemas, volumes, tables, views and materialized views.
- Apply naming conventions and implement foreign catalogs.
Module 2 — Secure and govern Unity Catalog
- Grant privileges to principals (users, groups, service principals).
- Implement table-, column- and row-level access control.
- Access Azure Key Vault secrets and authenticate access using managed identities.
- Govern: ABAC via tags, column masks, retention, lineage, audit logging and Delta Sharing.
Module 3 — Model and ingest data
- Design ingestion logic and choose the tools (Lakeflow Connect, notebooks, Azure Data Factory).
- Choose table formats (Delta, Parquet, Iceberg) and partitioning/clustering strategies (liquid clustering, Z-ordering).
- Ingest in batch and streaming: Auto Loader, Spark Structured Streaming, Azure Event Hubs, CDC, COPY INTO.
- Handle Slowly Changing Dimensions (SCD) and temporal tables.
Module 4 — Cleanse, transform and validate data
- Profile data, choose column data types, resolve duplicate and missing values.
- Transform: filtering, aggregation, joins, unions, pivots and denormalisation.
- Load using merge, insert and append operations.
- Quality constraints: validation checks, schema enforcement, schema drift, pipeline expectations.
Module 5 — Design and deploy pipelines
- Design the order of operations and error handling for pipelines.
- Build pipelines with notebooks and Lakeflow Spark Declarative Pipelines.
- Implement Lakeflow Jobs: triggers, scheduling, alerts, automatic restarts.
- Lifecycle: Git best practices, testing, Databricks Asset Bundles, deployment via CLI and REST APIs.
Module 6 — Monitor, troubleshoot and optimise
- Monitor cluster consumption for performance and cost.
- Troubleshoot Lakeflow Jobs and Spark jobs (bottlenecks, restart, tuning).
- Resolve caching, skew, spilling and shuffle issues via the Spark UI and query profile.
- Optimise Delta tables (OPTIMIZE, VACUUM) and monitoring via Azure Monitor / Log Analytics.
Module 7 — Course highlights
- A case-driven approach: every skill is anchored in a concrete Databricks lab.
- Hands-on use of Unity Catalog, Lakeflow and Spark, preparing you for the DP-750 certification.
- A small group (1 to 3 participants) and 30 days of post-course support.
FAQ
What is the Azure Databricks Data Engineer training (DP-750)?
A data engineering course on Azure Databricks, aligned with the Microsoft DP-750 exam outline (Azure Data Engineer Associate). It covers Unity Catalog, ingestion, transformation, Lakeflow pipelines and workload optimisation.
Who is this course for?
Data engineers, data developers and architects, analysts who want to industrialise their processing, and candidates for the DP-750 certification.
What are the prerequisites?
Basic SQL and Python and notions of Git are recommended. Some Azure experience is a plus. A preliminary interview tailors the content.
How long is it and in what format?
4 days (24 hours), remotely, in a group of 1 to 3 participants, with around 60 % hands-on labs and 30 days of follow-up support.