Big Data Engineering with Apache Spark

Big Data Engineering with Apache Spark is an original Mai Studies programme in data science & analytics. Over 8 weeks and roughly 40 guided hours you work through recorded classes, written notes, practical activities, knowledge checks and assessed tasks marked against a published rubric with an 80% pass mark. All material is written in-house. Mai Studies is independent: no official examination content, question bank, recording or branding from any certification body is reproduced, and completion leads to a Mai Studies completion certificate rather than a regulated or accredited qualification.

8 weeks 40 guided hours Advanced

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No account needed to buy. Pay with your card and we create your student account from the email you enter, with this programme ready inside it for twelve months.

Big Data Engineering with Apache Spark course artwork

What you will achieve

Explain the core principles of big data engineering with apache spark in plain language
Apply the methods safely and correctly to realistic scenarios
Produce a documented portfolio piece assessed against a published rubric
Plan your next steps in data analysis and analytics engineering

Entry guidance

— Solid intermediate experience in data analysis and analytics engineering
— Prior hands-on project work recommended

Original Mai Studies learning material. This is a professional completion programme, not a regulated qualification or university accreditation.

Programme syllabus

3 applied modules

Lessons: 6
Assessed tasks: 3
Practice questions: 36
Timed mock papers: 2

Foundations of Big Data Engineering with Apache Spark

Core concepts, vocabulary and working environment for big data engineering with apache spark.

2 lessons · 12 practice questions · 1 assessed task

1. What big data engineering with apache spark really involves21 min study · 3 learning objectives · practical activity
A grounded tour of the problems this advanced programme solves and the language practitioners use.
2. Setting up your working environment21 min study · 3 learning objectives · practical activity
Prepare the tools, accounts and folder structure you will use throughout the programme.

Foundations of Big Data Engineering with Apache Spark: assessed task — marked out of 100, pass at 80%

Applied practice in Big Data Engineering with Apache Spark

Hands-on work that turns theory into a repeatable method for data analysis and analytics engineering.

2 lessons · 12 practice questions · 1 assessed task

1. Working through a realistic scenario21 min study · 3 learning objectives · practical activity
Follow a complete worked example end to end, explaining each decision as you go.
2. Common mistakes and how to avoid them21 min study · 3 learning objectives · practical activity
The errors that cost practitioners most time, and the checks that catch them early.

Applied practice in Big Data Engineering with Apache Spark: assessed task — marked out of 100, pass at 80%

Portfolio project, assessment and next steps

Produce assessed evidence of your capability and plan the study that follows this programme.

2 lessons · 12 practice questions · 1 assessed task

1. Building your portfolio piece21 min study · 3 learning objectives · practical activity
Plan, build and document a piece of work you can show to an employer or client.
2. Where to go next21 min study · 3 learning objectives · practical activity
Honest guidance on further study, practice habits and how this fits a wider career path.

Portfolio project, assessment and next steps: assessed task — marked out of 100, pass at 80%

Timed mock papers

Big Data Engineering with Apache Spark: timed mock paper 1 — 180 minutes, 30 objective questions plus written and computational tasks
Big Data Engineering with Apache Spark: timed mock paper 2 — 180 minutes, 30 objective questions plus written and computational tasks

Lesson pages, assessed tasks and mock papers open the moment you join this programme. Teaching material is studied online; the notes you write are yours to download.

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