Cloud Data Engineering and AI Pipelines (GCP Professional-Aligned Study Support)

An independent Mai Studies study-support programme covering data engineering end to end: storage design, batch and streaming processing, pipeline orchestration, data quality, and preparing data for analytics and machine learning. It supports preparation for professional-level cloud data engineer certifications, a role blend for which reported UK salaries commonly range from GBP 95,000 to GBP 120,000; these are indicative market figures, not a guarantee. All material is original to Mai Studies. Mai Studies is not affiliated with or endorsed by Google or any certification body, no official exam content is reproduced, and completion does not grant any external certification.

10 weeks 50 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.

Cloud Data Engineering and AI Pipelines (GCP Professional-Aligned Study Support) course artwork

What you will achieve

Design storage and warehouse architectures for analytics workloads
Build batch and streaming data pipelines with orchestration
Engineer data quality, lineage and governance into pipelines
Prepare features and pipelines for machine learning workloads

Entry guidance

— Comfort with SQL and at least one programming language, ideally Python
— Basic familiarity with cloud services

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

Programme syllabus

3 applied modules

Lessons: 9
Assessed tasks: 3
Practice questions: 9
Timed mock papers: 0

Data Storage and Warehouse Design

Foundations every pipeline depends on.

3 lessons · 3 practice questions · 1 assessed task

1. Analytical storage fundamentals39 min study · 3 learning objectives · practical activity
How columnar storage and partitioning make analytics fast.
2. Data modelling for analytics21 min study · 3 learning objectives · practical activity
Schemas that serve questions, not just storage.
3. Ingestion architectures21 min study · 3 learning objectives · practical activity
Getting data in reliably, from files to change streams.

Storage and ingestion blueprint — marked out of 100, pass at 80%

Processing Pipelines: Batch and Streaming

Transforming data at rest and in motion.

3 lessons · 3 practice questions · 1 assessed task

1. Batch processing design20 min study · 3 learning objectives · practical activity
Reliable scheduled transformations at scale.
2. Streaming processing21 min study · 3 learning objectives · practical activity
Continuous computation on data in motion.
3. Data quality and observability21 min study · 3 learning objectives · practical activity
Trusting what the pipeline delivers.

Pipeline engineering case — marked out of 100, pass at 80%

Machine Learning Data Engineering

Feeding models with production-grade data.

3 lessons · 3 practice questions · 1 assessed task

1. Feature engineering pipelines21 min study · 3 learning objectives · practical activity
Turning raw data into model-ready features, reproducibly.
2. Operationalising machine learning21 min study · 3 learning objectives · practical activity
Pipelines for training, evaluation, deployment and monitoring.
3. Governance, privacy and responsible AI data21 min study · 3 learning objectives · practical activity
The duties that come with data power.

ML data platform proposal — marked out of 100, pass at 80%

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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