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Sample curriculum

Azure Data Factory

Orchestrate repeatable data movement and transformation workflows across Azure data services.

  • Intermediate
  • Live online and in person
Explore the curriculum Discuss your team’s training

At a glance

Who it’s for

Engineers with the listed foundations who want to deepen this skill or apply it in a different stack.

Starting point: Intermediate

Bring these foundations

  • SQL, data pipeline concepts, and familiarity with Azure.
Foundation sessions can be included when needed.

Technologies & tools

  • Azure Data Factory
  • Azure storage
  • Azure Databricks
  • SQL
Setup and versions agreed for your environment.

A practical destination

Skills to practise.

The sample path works towards these capabilities. Final goals reflect your team’s starting point and agreed scope.

  1. 01

    Connect datasets, linked services and pipelines.

  2. 02

    Parameterise data movement and orchestration.

  3. 03

    Operate a pipeline with useful monitoring and delivery controls.

Sample curriculum

Follow the learning route.

Each module connects the concepts to practical work. Open a module to explore both.

01Module 1

Data Factory foundations

Concepts to understand

  • Linked services and datasets
  • Pipelines and activities
  • Connections and access configuration

Practical work

Create a pipeline around a source and destination. Explain the roles of datasets, linked services and integration runtime.

Show the result. Explain one decision. Identify what you would improve.
02Module 2

Data movement

Concepts to understand

  • Copy activity and source-to-target mapping
  • Parameters, variables, and expressions
  • Incremental ingestion patterns

Practical work

Move a sample dataset and parameterise the source or target. Validate the transferred records and handle a missing input.

Show the result. Explain one decision. Identify what you would improve.
03Module 3

Orchestration

Concepts to understand

  • Triggers and scheduling
  • Dependencies, branching, and iteration
  • Connecting Databricks transformations

Practical work

Connect activities with dependencies and triggers. Show how parameters and failure branches change the workflow.

Show the result. Explain one decision. Identify what you would improve.
04Module 4

Operations & delivery

Concepts to understand

  • Monitoring and failure handling
  • Configuration across environments
  • Version control and deployment workflow

Practical work

Inspect run history, diagnose a failed activity and review environment-specific configuration before preparing delivery.

Show the result. Explain one decision. Identify what you would improve.

A sample project

Bring the skills together.

Build a parameterized ingestion pipeline with scheduling, monitoring, and a Databricks transformation step.

Shorter engagements can focus on selected exercises. A larger, learner-owned capstone can be scoped for a longer programme.

Review the work & reasoning

Four useful questions.

  1. 01

    Does the work meet the agreed requirements?

  2. 02

    Can the learner explain the key decisions and trade-offs?

  3. 03

    What happens with invalid input, failure or an unexpected condition?

  4. 04

    What would they change or explore next?

Designed & delivered by Ragav Kumar V.

Training since 2016 · 100+ batches · 3,000+ professionals trained

Live demonstrations, guided exercises, pair work and project reviews connect the concepts to implementation. We agree the goals, starting knowledge, cohort size, delivery and schedule before shaping the final curriculum.

Shape this for your team

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