Data Engineering Fundamentals
Develop the skills to build a validated source-to-report data pipeline through a learning path centred on Data Engineering Fundamentals. Explore concepts, guided practice and an applied project.
Build practical Data Engineering Fundamentals skills.
Skills and knowledge developed in this course.
- Data architecture
- Ingestion design
- Transformation practice
- Pipeline operations
- Applied project: Build a validated source-to-report data pipeline
- Data Engineering Fundamentals — validation and review
About this course
This course develops practical capability in Data Engineering Fundamentals. Its course-specific content covers Data architecture, Ingestion design, Transformation practice, Pipeline operations, followed by an applied project focused on build a validated source-to-report data pipeline. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Learners starting in data engineering & analytics and professionals who want an organized foundation before more advanced work.
Before you start
Basic computer literacy and comfort using the relevant operating system. No prior production experience is assumed; any lab accounts or software are confirmed before training.
Build a validated source-to-report data pipeline
Deliver build a validated source-to-report data pipeline. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Data architecture
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- Operational versus analytical workloads
- Batch, streaming and storage layers
Practical exerciseApply data architecture to the build a validated source-to-report data pipeline and capture the validation evidence.
02Ingestion design
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- Files, APIs and change capture
- Schemas, metadata and incremental loads
Practical exerciseApply ingestion design to the build a validated source-to-report data pipeline and capture the validation evidence.
03Transformation practice
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- Cleaning, joins and deduplication
- Data quality checks and lineage
Practical exerciseApply transformation practice to the build a validated source-to-report data pipeline and capture the validation evidence.
04Pipeline operations
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- Orchestration, retries and idempotency
- Access controls, monitoring and recovery
Practical exerciseApply pipeline operations to the build a validated source-to-report data pipeline and capture the validation evidence.
05Applied project: Build a validated source-to-report data pipeline
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- Define inputs, assumptions and acceptance criteria for: Build a validated source-to-report data pipeline.
- Implement the project using data architecture, ingestion design, transformation practice, pipeline operations.
Practical exerciseBuild a validated source-to-report data pipeline.
06Data Engineering Fundamentals — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Data architecture · Ingestion design
Establish the Data Engineering Fundamentals scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Transformation practice · Pipeline operations
Apply the core Data Engineering Fundamentals techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for build a validated source-to-report data pipeline. - 04Learning milestone 4 In progress
Deliver the applied project
Complete build a validated source-to-report data pipeline, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| Data Engineer | ₹8–15 LPA | ₹17–28 LPA | ₹29–50 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗