ETL and ELT
Develop the skills to implement and compare two data-loading strategies through a learning path centred on ETL and ELT. Explore concepts, guided practice and an applied project.
Build practical ETL and ELT skills.
Skills and knowledge developed in this course.
- Extraction
- Transformation
- Loading strategies
- Reliable processing
- Applied project: Implement and compare two data-loading strategies
- ETL and ELT — validation and review
About this course
This course develops practical capability in ETL and ELT. Its course-specific content covers Extraction, Transformation, Loading strategies, Reliable processing, followed by an applied project focused on implement and compare two data-loading strategies. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with ETL and ELT or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of data engineering & analytics. Prior experience with the named product helps with the practical exercises.
Implement and compare two data-loading strategies
Deliver implement and compare two data-loading strategies. 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.
01Extraction
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- Source interfaces and change detection
- Incremental boundaries and schema capture
Practical exerciseApply extraction to the implement and compare two data-loading strategies and capture the validation evidence.
02Transformation
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- Validation, cleansing and business rules
- Deduplication and reference-data joins
Practical exerciseApply transformation to the implement and compare two data-loading strategies and capture the validation evidence.
03Loading strategies
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- ETL versus ELT placement decisions
- Merge, append and overwrite semantics
Practical exerciseApply loading strategies to the implement and compare two data-loading strategies and capture the validation evidence.
04Reliable processing
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- Idempotency, checkpoints and retries
- Reconciliation, lineage and recovery evidence
Practical exerciseApply reliable processing to the implement and compare two data-loading strategies and capture the validation evidence.
05Applied project: Implement and compare two data-loading strategies
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- Define inputs, assumptions and acceptance criteria for: Implement and compare two data-loading strategies.
- Implement the project using extraction, transformation, loading strategies, reliable processing.
Practical exerciseImplement and compare two data-loading strategies.
06ETL and ELT — 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
Extraction · Transformation
Establish the ETL and ELT scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Loading strategies · Reliable processing
Apply the core ETL and ELT 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 implement and compare two data-loading strategies. - 04Learning milestone 4 In progress
Deliver the applied project
Complete implement and compare two data-loading strategies, 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 ↗