Start with the right foundation.
- Data analysts moving into engineering
- SQL or Python developers
- ETL developers modernizing batch pipelines
- Cloud engineers supporting analytics platforms
AZURE + DATABRICKS PRACTICAL PATH
Azure Data Engineering with Databricks
Design reliable lakehouse pipelines with Azure storage, Azure Data Factory, Apache Spark, Delta Lake, Databricks Workflows and Unity Catalog.
Learn how a production data platform moves from source discovery to governed, observable datasets. The program focuses on architecture decisions, incremental processing, data quality, security and operational recovery—not isolated notebook commands.
Recommended prerequisites.
- Comfort with SQL joins and aggregations
- Basic Python or PySpark reading ability
- Foundational cloud storage and identity concepts
ROLE-BASED OUTCOMES
Learn to make and defend real implementation decisions.
The goal is not to memorize product menus. Each outcome connects architecture, implementation and operational evidence.
Choose ingestion patterns for files, databases and event sources
Build bronze, silver and gold Delta pipelines with testable quality rules
Secure data products with managed identities, secrets and Unity Catalog
Orchestrate, monitor and recover pipelines with useful operational evidence
Tools and platformsAzure Data Lake StorageAzure Data FactoryAzure DatabricksApache SparkDelta LakeUnity CatalogDatabricks WorkflowsAzure Monitor
DETAILED SYLLABUS
One connected path from concepts to production evidence.
Every module includes a practical deliverable so you can explain what you built, why you chose it and how you verified it.
Architecture and source assessment
Translate volume, velocity, latency, schema and compliance requirements into a practical Azure data architecture.
- Lakehouse and medallion patterns
- Batch versus streaming
- Landing-zone design
- Identity boundaries
Hands-on lab
Create a source-to-serving architecture decision record for sales, customer and event data.
Secure ingestion and orchestration
Build repeatable movement from files and relational sources without embedding credentials.
- ADF linked services
- Managed identity
- Parameterized pipelines
- Incremental watermarks
Hands-on lab
Implement a metadata-driven ingestion pipeline with restart-safe watermarks and failure routing.
Spark transformations that scale
Use Spark execution concepts to prevent avoidable shuffles, skew and driver-memory failures.
- DataFrames and Spark SQL
- Partitions and joins
- Adaptive execution
- Debugging query plans
Hands-on lab
Diagnose and improve a skewed join, then document the before-and-after execution evidence.
Reliable Delta Lake tables
Design tables that support schema evolution, merges, history and dependable downstream consumption.
- ACID transactions
- MERGE and CDC
- Schema enforcement
- Optimization and maintenance
Hands-on lab
Build an idempotent customer-change pipeline and prove that a rerun does not duplicate records.
Governance with Unity Catalog
Control who can discover and use data while preserving lineage and least privilege.
- Catalog hierarchy
- Grants and ownership
- External locations
- Lineage and auditability
Hands-on lab
Create analyst, engineer and service-principal access paths and test both allowed and denied actions.
Production workflows and operations
Turn notebooks into scheduled, observable jobs with clear recovery procedures.
- Task dependencies
- Retries and repair runs
- Data-quality checks
- Cost and performance signals
Hands-on lab
Deploy a multi-task workflow with quality gates, alerts and a runbook for partial failure.
CAPSTONE WORK
Projects designed for explanation, review and improvement.
A useful portfolio shows decisions and evidence, not screenshots alone.
Retail lakehouse
Ingest orders and customer changes, build curated Delta tables and publish a sales mart.
Evidence to retain- Architecture decision record
- Restart-safe pipeline
- Quality report
- Operations runbook
Near-real-time event pipeline
Process application events into queryable aggregates with late-data handling.
Evidence to retain- Event schema
- Checkpoint strategy
- Latency dashboard
- Failure replay test
Governed data product
Publish a reusable customer dataset with ownership, access controls and lineage.
Evidence to retain- Access matrix
- Unity Catalog grants
- Data contract
- Consumer guide
CONTINUE WITH PRIMARY SOURCES
Use current official documentation alongside guided practice.
Technology changes. These primary references help you verify current features and continue learning after the course. ITCertPath is an independent training brand and is not endorsed by the listed vendors.
COURSE QUESTIONS
What to confirm before you enrol.
Ask for a written syllabus, delivery plan, trainer profile, lab arrangement, fee breakdown and cancellation terms.
Is this an Azure certification course?
It is practical role training aligned to common Azure and Databricks data-engineering work. It does not itself award a Microsoft or Databricks credential. Use the official study guide for the exact certification objectives you plan to pursue.
Will I build pipelines or only watch demonstrations?
The outline is lab-led. Each module produces reviewable evidence such as a pipeline, quality check, access test, architecture decision or recovery runbook. Confirm the exact lab environment and delivery format before enrolling.
Do I need advanced Python?
No, but you should be able to read basic Python and work with SQL. The focus is data-engineering decisions and reliable processing rather than software-engineering theory.
PLAN YOUR NEXT SKILL
Discuss Azure Data Engineering training with an advisor.
Tell us your experience, target role, time zone and preferred learning format. We will share the current options without promising a batch that has not been confirmed.