Apache Spark
Develop the skills to implement a partition-aware Spark transformation job through a learning path centred on Apache Spark. Explore concepts, guided practice and an applied project.
Build practical Apache Spark skills.
Skills and knowledge developed in this course.
- Spark execution
- Data transformation
- Performance behaviour
- Job operations
- Applied project: Implement a partition-aware Spark transformation job
- Apache Spark — validation and review
About this course
This course develops practical capability in Apache Spark. Its course-specific content covers Spark execution, Data transformation, Performance behaviour, Job operations, followed by an applied project focused on implement a partition-aware spark transformation job. 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 Apache Spark 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 a partition-aware Spark transformation job
Deliver implement a partition-aware spark transformation job. 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.
01Spark execution
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- Driver, executors and distributed tasks
- DataFrames, schemas and lazy evaluation
Practical exerciseApply spark execution to the implement a partition-aware spark transformation job and capture the validation evidence.
02Data transformation
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- Joins, aggregations and window functions
- Read-write formats and partition handling
Practical exerciseApply data transformation to the implement a partition-aware spark transformation job and capture the validation evidence.
03Performance behaviour
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- Shuffles, skew and caching
- Execution plans and resource configuration
Practical exerciseApply performance behaviour to the implement a partition-aware spark transformation job and capture the validation evidence.
04Job operations
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- Failure diagnosis and checkpoint concepts
- Structured Streaming introduction and output validation
Practical exerciseApply job operations to the implement a partition-aware spark transformation job and capture the validation evidence.
05Applied project: Implement a partition-aware Spark transformation job
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- Define inputs, assumptions and acceptance criteria for: Implement a partition-aware Spark transformation job.
- Implement the project using spark execution, data transformation, performance behaviour, job operations.
Practical exerciseImplement a partition-aware Spark transformation job.
06Apache Spark — 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
Spark execution · Data transformation
Establish the Apache Spark scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Performance behaviour · Job operations
Apply the core Apache Spark 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 a partition-aware spark transformation job. - 04Learning milestone 4 In progress
Deliver the applied project
Complete implement a partition-aware spark transformation job, 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 ↗